- Hazelcast C++ Client Library
- Introduction
- Resources
- Release Notes
- 1. Getting Started
- 2. Features
- 3. Configuration Overview
- 4. Serialization
- 5. Setting Up Client Network
- 5.1. Providing Member Addresses
- 5.2. Setting Smart Routing
- 5.3. Enabling Redo Operation
- 5.4. Setting Cluster Connection Timeout
- 5.5. Advanced Cluster Connection Retry Configuration
- 5.6. Enabling Client TLS/SSL
- 5.7. Enabling Hazelcast AWS Discovery
- 5.8. Enabling Hazelcast Cloud Discovery
- 5.8.1. Cloud Discovery With SSL Enabled
- 5.9. External Smart Client Discovery
- 5.10. Authentication
- 5.10.1. Username Password Authentication
- 5.10.2. Token Authentication
- 5.11. Configuring Backup Acknowledgment
- 6. Securing Client Connection
- 7. Using C++ Client with Hazelcast
- 7.1. C++ Client API Overview
- 7.2. C++ Client Operation Modes
- 7.3. Handling Failures
- 7.3.4 Client Connection Strategy
- 7.4. Using Distributed Data Structures
- 7.5. Distributed Events
- 7.6. Distributed Computing
- 7.7. Distributed Query
- 7.8. Performance
- 7.9. Monitoring and Logging
- 7.10. Mixed Object Types Supporting Hazelcast Client
- 7.11. SQL API
- 8. Development and Testing
- 9. Getting Help
- 10. Contributing
- 11. License
- 12. Copyright
This document provides information about the C++ client for Hazelcast. This client uses Hazelcast's Open Client Protocol and works with Hazelcast 4.0 and higher versions.
The client API is fully asynchronous. The API returns boost::future API which has the capability of continuations. If the user wants to make sure that the requested operation is completed in the cluster and committed in the distributed database, he/she needs to wait for the result of the future.
See the following for more information on Hazelcast:
- Hazelcast website
- Hazelcast Reference Manual
See the Releases page of this repository.
This chapter provides information on how to get started with your Hazelcast C++ client. It outlines the requirements, installation and configuration of the client, setting up a cluster, and provides a simple application that uses a distributed map in C++ client.
Hazelcast C++ client package is indexed at Conan Center Index. You can use Conan package manager to install Hazelcast C++ client. The package name is hazelcast-cpp-client.
Please see example instructions on how to use conan package manager with your application. In summary,
- You need to put the following lines to your
conanfile.txt:
[requires]
hazelcast-cpp-client/5.2.0
[generators]
cmake
- Then, you execute the following
$ mkdir build && cd build
$ conan install ..
This generates the conanbuildinfo.cmake file to be included in your CMakeLists.txt. Please follow the instructions at the example page and build your application.
Hazelcast C++ client package is available for Vcpkg users. The port name is hazelcast-cpp-client.
Please see Getting Started on how to use Vcpkg package manager with your application. In summary,
> git clone https://github.com/microsoft/vcpkg
> .\vcpkg\bootstrap-vcpkg.bat
> .\vcpkg\vcpkg install hazelcast-cpp-clientThe above code snippet will install hazelcast-cpp-client with its boost dependencies.
After the installation, the library is available for usage. For example, if you are using CMake for your builds, you can use the following cmake build command with the CMAKE_TOOLCHAIN_FILE cmake option to be the vcpkg.cmake.
> cmake -B [build directory] -S . -DCMAKE_TOOLCHAIN_FILE=[path to vcpkg]/scripts/buildsystems/vcpkg.cmake
> cmake --build [build directory]You can find more details on using a Vcpkg installed package from different IDEs in your projects from the Vcpkg Official Getting Started documentation.
If you need to use openssl feature, then you need to install using the following command:
> .\vcpkg\vcpkg install hazelcast-cpp-client[openssl] --recurseThe above code will install hazelcast-cpp-client with its boost and openssl dependencies.
- Linux, macOS or Windows
- A compiler that supports C++11
- CMake 3.10 or above
- Boost 1.71 or above. Minimum boost version is upgraded to 1.73 for Windows due to this bug.
- OpenSSL (optional)
Go to the releases page to download the source code for the latest Hazelcast C++ client.
The releases page has both tar.gz and zip archives available. Choose the one which suits your system the best.
Follow the instructions for your platform:
Here is how you download and extract version 5.2.0 using the curl command:
curl -Lo hazelcast-cpp-client-5.2.0.tar.gz https://github.com/hazelcast/hazelcast-cpp-client/archive/v5.2.0.tar.gz
tar xzf hazelcast-cpp-client-5.2.0.tar.gzAlternatively, you may clone the repository and checkout a specific version:
git clone https://github.com/hazelcast/hazelcast-cpp-client.git
cd hazelcast-cpp-client
git checkout v5.2.0Once you are in the source directory of the Hazelcast C++ client library, create and change into a new directory:
cd hazelcast-cpp-client-5.2.0
mkdir build
cd buildRun cmake (or cmake3 if you are on CentOS or RHEL) to configure:
cmake ..See the advanced installation section for configuration options.
Run cmake again to build and install the library:
cmake --build .
sudo cmake --build . --target installSee this section for information on how to use a different installation location.
Download and extract the release archive from the releases page.
Open a cmd window and change into the folder where you extracted the contents of the release archive. Then create and change into a new directory:
cd hazelcast-cpp-client-5.2.0
mkdir build
cd buildRun cmake to configure:
cmake ..See the advanced installation section for configuration options.
Build and install:
cmake --build . --config Release
cmake --build . --target install --config ReleaseThe above commands will build and install the library with the Release configuration. Make sure you pass the same --config option to both commands.
The install command may require administrator privileges depending on your install prefix. See this section for information on how to use a different installation location.
Pass the argument -DCMAKE_INSTALL_PREFIX=/path/to/install the first time you run cmake to configure the installation directory:
cmake .. -DCMAKE_INSTALL_PREFIX=/path/to/installYou can provide additional configuration options using the -DVARIABLE=VALUE syntax on the command line. Here are all the options that are supported:
WITH_OPENSSL: Set toONto build the library with SSL support. This will require OpenSSL to be installed on your system. The default isOFF.BUILD_SHARED_LIBS: Set toONorOFFdepending on whether you want the shared(ON) or static(OFF) library. The default isON.DISABLE_LOGGING: Setting this option toONdisables logging. The default isOFF.
For example, if you want to build the static library with SSL support, you can use the following command:
cmake .. -DWITH_OPENSSL=ON -DBUILD_SHARED_LIBS=OFFNote that, if you want to use the hazelcast-cpp-client library with -DWITH_OPENSSL=ON option without find_package() then you need to define HZ_BUILD_WITH_SSL symbolic constant before including any hazelcast-cpp-client header. This symbolic constant can be defined via compiler options or can be passed directly through cmake command as -DVARIABLE=VALUE pairs.
For example:
g++ -DHZ_BUILD_WITH_SSL -DBOOST_CHRONO_DYN_LINK -DBOOST_CHRONO_NO_LIB -DBOOST_THREAD_DYN_LINK -DBOOST_THREAD_NO_LIB -DBOOST_THREAD_VERSION=5 -I/var/git/hazelcast-cpp-client/build/include -std=gnu++11 -c main.cppThe Hazelcast C++ client requires a working Hazelcast cluster to run. This cluster handles storage and manipulation of the user data. Clients are a way to connect to the Hazelcast cluster and access such data.
A Hazelcast cluster consists of one or more cluster members. These members generally run on multiple virtual or physical machines and are connected to each other via network. Any data put on the cluster is partitioned to multiple members transparent to the user. It is therefore very easy to scale the system by adding new members as the data grows. Hazelcast clusters also offer resilience. Should any hardware or software problem causes a crash to any member, the data on that member is recovered from backups and the cluster continues to operate without any downtime. Hazelcast clients are an easy way to connect to a Hazelcast cluster and perform tasks on distributed data structures that live on the cluster.
In order to use the Hazelcast C++ client, we first need to setup a Hazelcast server.
The quickest way to start a single member cluster for development purposes is to use our Docker images.
docker run -p 5701:5701 hazelcast/hazelcast:latestFollow the instructions below to create a Hazelcast cluster:
- Go to Hazelcast's download page and download either the
.zipor.tardistribution of Hazelcast. - Decompress the contents into any directory that you want to run members from.
- Change into the directory that you decompressed the Hazelcast content and then into the
bindirectory. - Use either
hz startorhz-start.batdepending on your operating system. Once you run the start script, you should see the Hazelcast logs in the terminal.
You should see a log similar to the following, which means that your single member cluster is ready to be used:
Nov 19, 2022 2:52:59 PM com.hazelcast.internal.cluster.ClusterService
INFO: [192.168.1.112]:5701 [dev] [5.x.x]
Members {size:1, ver:1} [
Member [192.168.1.112]:5701 - 360ba49b-ef33-4590-9abd-ceff3e31dc06 this
]
Nov 19, 2022 2:52:59 PM com.hazelcast.core.LifecycleService
INFO: [192.168.1.112]:5701 [dev] [5.x.x] [192.168.1.112]:5701 is STARTED
When you want to use features such as querying and language interoperability, you might need to add your own Java classes to the Hazelcast member in order to use them from your C++ client. This can be done by adding your own compiled code to the CLASSPATH. To do this, compile your code with the CLASSPATH and add the compiled files to the user-lib directory in the extracted hazelcast-<version>.zip (or tar). Then, you can start your Hazelcast member by using the start scripts in the bin directory. The start scripts will automatically add your compiled classes to the CLASSPATH.
Note that if you are adding an IdentifiedDataSerializable or a Portable class, you need to add its factory too. Then, you should configure the factory in the hazelcast.xml configuration file. This file resides in the bin directory where you extracted the hazelcast-<version>.zip (or tar).
The following is an example configuration when you are adding an IdentifiedDataSerializable class:
<hazelcast>
...
<serialization>
<data-serializable-factories>
<data-serializable-factory factory-id="66">
com.hazelcast.client.test.IdentifiedFactory
</data-serializable-factory>
</data-serializable-factories>
</serialization>
...
</hazelcast>If you want to add a Portable class, you should use <portable-factories> instead of <data-serializable-factories> in the above configuration.
See the Hazelcast Reference Manual for more information on setting up the clusters.
If you are using CMake, see the section for CMake users.
If you are not, then read the instructions specific to your platform:
The Hazelcast C++ client installation comes with package configuration files for CMake. If your project is using CMake, you can easily find and link against the client library:
find_package(hazelcast-cpp-client CONFIG REQUIRED)
target_link_libraries(mytarget PRIVATE hazelcast-cpp-client::hazelcast-cpp-client)Make sure you add the installation prefix of the client library to CMAKE_PREFIX_PATH if you are using a custom installation location.
You can pass the -lhazelcast-cpp-client option to the compiler to link against the client library.
The client library depends on Boost.Thread and Boost.Chrono. You should also link your program against these libraries using -lboost_thread and -lboost_chrono. The Boost.Thread library should be provided with the preprocessor definition BOOST_THREAD_VERSION=5 for necessary features such as futures and future continuations to be enabled.
Here is how you can compile an example from the examples directory:
g++ -std=c++11 \
examples/path/to/example.cpp \
-DBOOST_THREAD_VERSION=5 \
-lhazelcast-cpp-client -lboost_thread -lboost_chronoIf a custom installation directory was used during installation, then you may also need to use the -L and -I options to add the library and include paths to the compiler's search path.
g++ -std=c++11 \
examples/path/to/example.cpp \
-I /path/to/install/include -L /path/to/install/lib \
-lhazelcast-cpp-client -lboost_thread -lboost_chrono
Provide your compiler with the include directories and library files for the Hazelcast C++ client and its dependencies.
You also need to pass the preprocessor definition BOOST_THREAD_VERSION=5 for necessary features such as futures and future continuations to be enabled.
The following is a command that can be used to compile an example from the examples directory.
cl.exe path\to\example.cpp ^
C:\path\to\hazelcast\lib\hazelcast-cpp-client.lib ^
C:\path\to\boost\lib\boost_thread.lib C:\path\to\boost\lib\boost_chrono.lib ^
/EHsc /DBOOST_THREAD_VERSION=5 ^
/I C:\path\to\hazelcast\include /I C:\path\to\boost\includeIf you are using Hazelcast and the Hazelcast C++ Client on the same computer, generally the default configuration should be fine. This is great for trying out the client. However, if you run the client on a different computer than any of the cluster members, you may need to do some simple configurations such as specifying the member addresses.
Hazelcast members and clients have their own configuration options. You may need to reflect some of the member side configurations on the client side to properly connect to the cluster.
This section describes the most common configuration elements to get you started in no time. It discusses some member side configuration options to ease the understanding of Hazelcast's ecosystem. Then, the client side configuration options regarding the cluster connection are discussed. The configurations for the Hazelcast data structures that can be used in the C++ client are discussed in the following sections.
See the Hazelcast Reference Manual and Configuration Overview section for more information.
Hazelcast aims to run out-of-the-box for most common scenarios. However, if you have limitations on your network such as multicast being disabled, you may have to configure your Hazelcast members so that they can find each other on the network. Also, since most of the distributed data structures are configurable, you may want to configure them according to your needs. We will show you the basics about network configuration here.
You can use the following options to configure Hazelcast:
- Using the
hazelcast.xmlconfiguration file. - Programmatically configuring the member before starting it from the Java code.
Since we use standalone servers, we will use the hazelcast.xml file to configure our cluster members.
When you download and unzip hazelcast-<version>.zip (or tar), you see the hazelcast.xml in the bin directory. When a Hazelcast member starts, it looks for the hazelcast.xml file to load the configuration from. A sample hazelcast.xml is shown below.
<hazelcast>
<cluster-name>dev</cluster-name>
<network>
<port auto-increment="true" port-count="100">5701</port>
<join>
<multicast enabled="true">
<multicast-group>224.2.2.3</multicast-group>
<multicast-port>54327</multicast-port>
</multicast>
<tcp-ip enabled="false">
<interface>127.0.0.1</interface>
<member-list>
<member>127.0.0.1</member>
</member-list>
</tcp-ip>
</join>
<ssl enabled="false"/>
</network>
<partition-group enabled="false"/>
<map name="default">
<backup-count>1</backup-count>
</map>
</hazelcast>We will go over some important configuration elements in the rest of this section.
-
<cluster-name>: Specifies which cluster this member belongs to. -
<network><port>: Specifies the port number to be used by the member when it starts. Its default value is 5701. You can specify another port number, and if you setauto-incrementtotrue, then Hazelcast will try the subsequent ports until it finds an available port or theport-countis reached.<join>: Specifies the strategies to be used by the member to find other cluster members. Choose which strategy you want to use by setting itsenabledattribute totrueand the others tofalse.<multicast>: Members find each other by sending multicast requests to the specified address and port. It is very useful if IP addresses of the members are not static.<tcp>: This strategy uses a pre-configured list of known members to find an already existing cluster. It is enough for a member to find only one cluster member to connect to the cluster. The rest of the member list is automatically retrieved from that member. We recommend putting multiple known member addresses there to avoid disconnectivity should one of the members in the list is unavailable at the time of connection.
These configuration elements are enough for most connection scenarios. Now we will move onto the configuration of the C++ client.
You must configure a Hazelcast C++ Client programmatically. Config files of any type are not yet supported for the C++ client.
You can start the client with no custom configuration like this:
auto hz = hazelcast::new_client().get(); // Connects to the clusterThis section describes some network configuration settings to cover common use cases in connecting the client to a cluster. See the Configuration Overview section and the following sections for information about detailed network configurations and/or additional features of Hazelcast C++ client configuration.
An easy way to configure your Hazelcast C++ Client is to create a client_config object and set the appropriate options. Then you need to pass this object to the client when starting it, as shown below.
hazelcast::client::client_config config;
config.set_cluster_name("my-cluster"); // the server is configured to use the `my_cluster` as the cluster name hence we need to match it to be able to connect to the server.
config.get_network_config().add_address(address("192.168.1.10", 5701));
auto hz = hazelcast::new_client(std::move(config)).get(); // Connects to the cluster member at ip address `192.168.1.10` and port 5701If you run Hazelcast members in a different server than the client, you most probably have configured the ports and the cluster names of the members as explained in the previous section. If you did, then you need to make certain changes to the network settings of your client.
You only need to provide the name of the cluster if it is explicitly configured on the server side (otherwise the default value of dev is used).
hazelcast::client::client_config config;
config.set_cluster_name("my-cluster"); // the server is configured to use the `my_cluster` as the cluster name hence we need to match it to be able to connect to the server.You need to provide the IP address and port of at least one member in your cluster so the client can find it.
hazelcast::client::client_config config;
config.get_network_config().add_address(hazelcast::client::address("your server ip", 5701 /* your server port*/));While configuring your C++ client, you can use various system properties provided by Hazelcast to tune its clients. These properties can be set programmatically through config.set_property or by using an environment variable. The value of this property will be:
- the programmatically configured value, if programmatically set,
- the environment variable value, if the environment variable is set,
- the default value, if none of the above is set.
See the following for an example client system property configuration:
Programmatically:
config.set_property(hazelcast::client::client_properties::INVOCATION_TIMEOUT_SECONDS, "2") // Sets invocation timeout as 2 secondsor
config.set_property("hazelcast.client.invocation.timeout.seconds", "2") // Sets invocation timeout as 2 secondsBy using an environment variable on Linux:
export hazelcast.client.invocation.timeout.seconds=2If you set a property both programmatically and via an environment variable, the programmatically set value will be used.
See the complete list of system properties, along with their descriptions, which can be used to configure your Hazelcast C++ client.
Now that we have a working cluster and we know how to configure both our cluster and client, we can run a simple program to use a distributed map in C++ client.
The following example first creates a programmatic configuration object. Then, it starts a client.
#include <hazelcast/client/hazelcast_client.h>
int main() {
auto hz = hazelcast::new_client().get(); // Connects to the cluster
std::cout << "Started the Hazelcast C++ client instance " << hz.get_name() << std::endl; // Prints client instance name
return 0;
}This should print logs about the cluster members and information about the client itself such as client type and local address port.
18/11/2022 21:22:26.835 INFO: [139868602337152] client_1[dev] [5.x.x] [/home/ihsan/hazelcast-cpp-client/hazelcast/src/hazelcast/client/spi.cpp:375] (Wed Nov 18 17:25:23 2022 +0300:3b11bea) LifecycleService::LifecycleEvent Client (75121987-12fe-4ede-860d-59222e6d3ef2) is STARTING
18/11/2022 21:22:26.835 INFO: [139868602337152] client_1[dev] [5.x.x] [/home/ihsan/hazelcast-cpp-client/hazelcast/src/hazelcast/client/spi.cpp:379] (Wed Nov 18 17:25:23 2022 +0300:3b11bea) LifecycleService::LifecycleEvent STARTING
18/11/2022 21:22:26.835 INFO: [139868602337152] client_1[dev] [5.x.x] [/home/ihsan/hazelcast-cpp-client/hazelcast/src/hazelcast/client/spi.cpp:387] LifecycleService::LifecycleEvent STARTED
18/11/2022 21:22:26.837 INFO: [139868602337152] client_1[dev] [5.x.x] [/home/ihsan/hazelcast-cpp-client/hazelcast/src/hazelcast/client/network.cpp:587] Trying to connect to Address[10.212.1.117:5701]
18/11/2022 21:22:26.840 INFO: [139868602337152] client_1[dev] [5.x.x] [/home/ihsan/hazelcast-cpp-client/hazelcast/src/hazelcast/client/spi.cpp:411] LifecycleService::LifecycleEvent CLIENT_CONNECTED
18/11/2022 21:22:26.840 INFO: [139868602337152] client_1[dev] [5.x.x] [/home/ihsan/hazelcast-cpp-client/hazelcast/src/hazelcast/client/network.cpp:637] Authenticated with server Address[:5701]:a27f900e-b1eb-48be-aa46-d7a4922ef704, server version: 4.2, local address: Address[10.212.1.116:37946]
18/11/2022 21:22:26.841 INFO: [139868341360384] client_1[dev] [5.x.x] [/home/ihsan/hazelcast-cpp-client/hazelcast/src/hazelcast/client/spi.cpp:881]
Members [1] {
Member[10.212.1.117]:5701 - a27f900e-b1eb-48be-aa46-d7a4922ef704
}
Started the Hazelcast C++ client instance hz.client_1
Congratulations! You just started a Hazelcast C++ Client.
Using a Map
Let's manipulate a distributed map on a cluster using the client.
Save the following file as IT.cpp and compile it using a command similar to the following (Linux g++ compilation is used for demonstration):
g++ IT.cpp -o IT -lhazelcast-cpp-client -lboost_thread -lboost_chrono -DBOOST_THREAD_VERSION=5Then, you can run the application using the following command:
./IT
main.cpp
#include <hazelcast/client/hazelcast_client.h>
int main() {
auto hz = hazelcast::new_client().get(); // Connects to the cluster
auto personnel = hz.get_map("personnel_map").get();
personnel->put<std::string, std::string>("Alice", "IT").get();
personnel->put<std::string, std::string>("Bob", "IT").get();
personnel->put<std::string, std::string>("Clark", "IT").get();
std::cout << "Added IT personnel. Logging all known personnel" << std::endl;
for (const auto &entry : personnel->entry_set<std::string, std::string>().get()) {
std::cout << entry.first << " is in " << entry.second << " department." << std::endl;
}
return 0;
}Output
18/11/2022 21:22:26.835 INFO: [139868602337152] client_1[dev] [5.x.x] [/home/ihsan/hazelcast-cpp-client/hazelcast/src/hazelcast/client/spi.cpp:375] (Wed Nov 18 17:25:23 2022 +0300:3b11bea) LifecycleService::LifecycleEvent Client (75121987-12fe-4ede-860d-59222e6d3ef2) is STARTING
18/11/2022 21:22:26.835 INFO: [139868602337152] client_1[dev] [5.x.x] [/home/ihsan/hazelcast-cpp-client/hazelcast/src/hazelcast/client/spi.cpp:379] (Wed Nov 18 17:25:23 2022 +0300:3b11bea) LifecycleService::LifecycleEvent STARTING
18/11/2022 21:22:26.835 INFO: [139868602337152] client_1[dev] [5.x.x] [/home/ihsan/hazelcast-cpp-client/hazelcast/src/hazelcast/client/spi.cpp:387] LifecycleService::LifecycleEvent STARTED
18/11/2022 21:22:26.837 INFO: [139868602337152] client_1[dev] [5.x.x] [/home/ihsan/hazelcast-cpp-client/hazelcast/src/hazelcast/client/network.cpp:587] Trying to connect to Address[10.212.1.117:5701]
18/11/2022 21:22:26.840 INFO: [139868602337152] client_1[dev] [5.x.x] [/home/ihsan/hazelcast-cpp-client/hazelcast/src/hazelcast/client/spi.cpp:411] LifecycleService::LifecycleEvent CLIENT_CONNECTED
18/11/2022 21:22:26.840 INFO: [139868602337152] client_1[dev] [5.x.x] [/home/ihsan/hazelcast-cpp-client/hazelcast/src/hazelcast/client/network.cpp:637] Authenticated with server Address[:5701]:a27f900e-b1eb-48be-aa46-d7a4922ef704, server version: 4.2, local address: Address[10.212.1.116:37946]
18/11/2022 21:22:26.841 INFO: [139868341360384] client_1[dev] [5.x.x] [/home/ihsan/hazelcast-cpp-client/hazelcast/src/hazelcast/client/spi.cpp:881]
Members [1] {
Member[10.212.1.117]:5701 - a27f900e-b1eb-48be-aa46-d7a4922ef704
}
Added IT personnel. Logging all known personnel
Alice is in IT department
Clark is in IT department
Bob is in IT department
You see this example puts all the IT personnel into a cluster-wide personnel_map and then prints all the known personnel.
Now create a Sales.cpp file, compile and run it as shown below.
Compile:
g++ Sales.cpp -o Sales -lhazelcast-cpp-client -lboost_thread -lboost_chrono -DBOOST_THREAD_VERSION=5Run
Then, you can run the application using the following command:
./Sales
Sales.cpp
#include <hazelcast/client/hazelcast_client.h>
int main() {
auto hz = hazelcast::new_client().get(); // Connects to the cluster
auto personnel = hz.get_map("personnel_map").get();
personnel->put<std::string, std::string>("Denise", "Sales").get();
personnel->put<std::string, std::string>("Erwing", "Sales").get();
personnel->put<std::string, std::string>("Fatih", "Sales").get();
personnel->put<std::string, std::string>("Bob", "IT").get();
personnel->put<std::string, std::string>("Clark", "IT").get();
std::cout << "Added all sales personnel. Logging all known personnel" << std::endl;
for (const auto &entry : personnel.entry_set().get()) {
std::cout << entry.first << " is in " << entry.second << " department." << std::endl;
}
return 0;
}Output
18/11/2022 21:22:26.835 INFO: [139868602337152] client_1[dev] [5.x.x] [/home/ihsan/hazelcast-cpp-client/hazelcast/src/hazelcast/client/spi.cpp:375] (Wed Nov 18 17:25:23 2022 +0300:3b11bea) LifecycleService::LifecycleEvent Client (75121987-12fe-4ede-860d-59222e6d3ef2) is STARTING
18/11/2022 21:22:26.835 INFO: [139868602337152] client_1[dev] [5.x.x] [/home/ihsan/hazelcast-cpp-client/hazelcast/src/hazelcast/client/spi.cpp:379] (Wed Nov 18 17:25:23 2022 +0300:3b11bea) LifecycleService::LifecycleEvent STARTING
18/11/2022 21:22:26.835 INFO: [139868602337152] client_1[dev] [5.x.x] [/home/ihsan/hazelcast-cpp-client/hazelcast/src/hazelcast/client/spi.cpp:387] LifecycleService::LifecycleEvent STARTED
18/11/2022 21:22:26.837 INFO: [139868602337152] client_1[dev] [5.x.x] [/home/ihsan/hazelcast-cpp-client/hazelcast/src/hazelcast/client/network.cpp:587] Trying to connect to Address[10.212.1.117:5701]
18/11/2022 21:22:26.840 INFO: [139868602337152] client_1[dev] [5.x.x] [/home/ihsan/hazelcast-cpp-client/hazelcast/src/hazelcast/client/spi.cpp:411] LifecycleService::LifecycleEvent CLIENT_CONNECTED
18/11/2022 21:22:26.840 INFO: [139868602337152] client_1[dev] [5.x.x] [/home/ihsan/hazelcast-cpp-client/hazelcast/src/hazelcast/client/network.cpp:637] Authenticated with server Address[:5701]:a27f900e-b1eb-48be-aa46-d7a4922ef704, server version: 4.2, local address: Address[10.212.1.116:37946]
18/11/2022 21:22:26.841 INFO: [139868341360384] client_1[dev] [5.x.x] [/home/ihsan/hazelcast-cpp-client/hazelcast/src/hazelcast/client/spi.cpp:881]
Members [1] {
Member[10.212.1.117]:5701 - a27f900e-b1eb-48be-aa46-d7a4922ef704
}
Added Sales personnel. Logging all known personnel
Denise is in Sales department
Erwing is in Sales department
Fatih is in Sales department
Bob is in IT department
Clark is in IT department
You will see this time we add only the sales employees but we get the list all known employees including the ones in IT. That is because our map lives in the cluster and no matter which client we use, we can access the whole map.
See the Hazelcast C++ code samples for more examples.
Hazelcast C++ client supports the following data structures and features:
- imap
- iqueue
- iset
- ilist
- multi_map
- replicated_map
- Ringbuffer
- reliable_topic
- CRDT pn_counter
- flake_id_generator
- fenced_lock (CP Subsystem)
- counting_semaphore (CP Subsystem)
- atomic_long (CP Subsystem)
- atomic_reference (CP Subsystem)
- latch (CP Subsystem)
- Event Listeners
- Distributed Executor Service
- Entry Processor
- transactional_map
- transactional_multi_map
- transactional_queue
- transactional_list
- transactional_set
- Query (Predicates)
- paging_predicate
- Built-in Predicates
- Listener with predicate
- Near Cache Support
- Programmatic Configuration
- Fail Fast on Invalid Configuration
- SSL Support (requires Enterprise server)
- Authorization
- Smart Client
- Unisocket Client
- Lifecycle Service
- identified_data_serializer Serialization
- portable_serializer Serialization
- custom_serializer Serialization
- JSON Serialization
- Global Serialization
- Hazelcast AWS Discovery
- Hazelcast Cloud Discovery
- External Smart Client Discovery
This chapter describes the options to configure your C++ client.
You can configure Hazelcast C++ client programmatically (API).
For programmatic configuration of the Hazelcast C++ client, just instantiate a client_config object and configure the desired aspects. An example is shown below.
hazelcast::client::client_config config;
config.get_network_config().add_address({ "your server ip", 5701 /* your server port*/});
auto hz = hazelcast::new_client(std::move(config)).get(); // Connects to the clusterSee the client_config class reference at the Hazelcast C++ client API Documentation for details.
Serialization is the process of converting an object into a stream of bytes to store the object in the memory, a file or database, or transmit it through the network. Its main purpose is to save the state of an object in order to be able to recreate it when needed. The reverse process is called deserialization. Hazelcast offers you its own native serialization methods. You will see these methods throughout this chapter.
Hazelcast serializes all your objects before sending them to the server. The unsigned char (byte), bool, char, short, int32_t, int64_t, float, double, std::string types are serialized natively and you cannot override this behavior. The following table is the conversion of types for Java server side.
| C++ | Java |
|---|---|
| byte | Byte |
| bool | Boolean |
| char | Character |
| short | Short |
| int32_t | Integer |
| int64_t | Long |
| float | Float |
| double | Double |
| std::string | String |
Vector of the above types can be serialized as boolean[], byte[], short[], int[], float[], double[], long[] and string[] for the Java server side, respectively.
If you want the serialization to work faster or you use the clients in different languages, Hazelcast offers its own native serialization types, such as identified_data_serializer serialization and portable_serializer serialization.
On top of all, if you want to use your own serialization type, you can use a Custom Serialization.
Hazelcast serialization is mostly performed at compile time by implementing the specialization of template struct hz_serializer<>. The specialized struct should be defined at the hazelcast::client::serialization namespace. The serializer should be derived from one of the following marker classes:
- identified_data_serializer
- portable_serializer
- custom_serializer
- json serialization
- global_serializer
This way of specialization allows us two advantages:
-
No need to modify your classes (no extra API interfaces for existing classes).
-
The determination of the serializer is at compile time.
In addition to deriving from these marker classes, certain methods should be implemented in order your code to compile with this serializations.
You can also configure a global serializer in case non-of the above serializations are implemented for the user object. If no serializer can be found, then exception::hazelcast_serialization is thrown at runtime.
For a faster serialization of objects, Hazelcast recommends to implement the identified_data_serializer interface. The following is an example of a hz_serializer implementing this interface:
struct Person {
std::string name;
bool male;
int32_t age;
};
namespace hazelcast {
namespace client {
namespace serialization {
template<>
struct hz_serializer<Person> : identified_data_serializer {
static int32_t get_factory_id() noexcept {
return 1;
}
static int32_t get_class_id() noexcept {
return 3;
}
static void write_data(const Person &object, object_data_output &out) {
out.write(object.name);
out.write(object.male);
out.write(object.age);
}
static Person read_data(object_data_input &in) {
return Person{in.read<std::string>(), in.read<bool>(), in.read<int32_t>()};
}
};
}
}
}hz_serializer<Person> specialization of hz_serializer should implement the above four methods, namely get_factory_id, get_class_id, write_data, read_data. In case that the object fields are non-public, you can always define struct hz_serializer<Person> as a friend to your object class. You use the object_data_output class methods when serializing the object into binary bytes and the object_data_input class methods when de-serializing the bytes into the concrete object instance. The factory and class ID returned from the associated methods should match the IDs defined at the server side (you need to have the corresponding Java classes at the server side).
If you want to support versioning of the objects, then you can use the portable serialization type. It has the following benefits:
- Support multiversion of the same object type.
- Fetch individual fields without having to rely on the reflection.
- Querying and indexing support without deserialization and/or reflection.
In order to support these features, a serialized Portable object contains meta information like the version and concrete location of the each field in the binary data. This way Hazelcast is able to navigate in the binary data and deserialize only the required field without actually deserializing the whole object which improves the query performance.
With multiversion support, you can have two members where each of them having different versions of the same object, and Hazelcast will store both meta information and use the correct one to serialize and deserialize portable objects depending on the member. This is very helpful when you are doing a rolling upgrade without shutting down the cluster.
Also note that portable serialization is totally language independent and is used as the binary protocol between Hazelcast server and clients.
A sample portable_serializer implementation of a Person class looks like the following:
struct Person {
std::string name;
bool male;
int32_t age;
};
namespace hazelcast {
namespace client {
namespace serialization {
template<>
struct hz_serializer<Person> : portable_serializer {
static int32_t get_factory_id() noexcept {
return 1;
}
static int32_t get_class_id() noexcept {
return 3;
}
static void write_portable(const Person &object, portable_writer &out) {
out.write("name", object.name);
out.write("gender", object.male);
out.write("age", object.age);
}
static Person read_portable(portable_reader &in) {
return Person{in.read<std::string>("name"), in.read<bool>("gender"), in.read<int32_t>("age")};
}
};
}
}
}
Note that, this is very similar to identified_data_serializer implementation except the method signatures and the derived marker class of portable_serializer for the specialized serializer.
hz_serializer<Person> specialization of hz_serializer should implement the above four methods, namely get_factory_id, get_class_id, write_portable, read_portable. In case that the object fields are non-public, you can always define struct hz_serializer<Person> as friend to your object class. You use the portable_writer class methods when serializing the object into binary bytes and you use the portable_reader class methods when de-serializing the bytes into the concrete object instance.
As an enhancement to the existing serialization methods, Hazelcast offers compact serialization, with the following main features:
- Separates the schema from the data and stores it for each type, instead of each object which results in less memory and bandwidth usage compared to other formats
- Does not require a class to implement an interface or change the source code of the class in any way
- Supports schema evolution which permits adding or removing fields, or changing the types of fields
- Can work with any kind of types
- Platform and language independent
- Supports partial deserialization of fields, without deserializing the whole objects during queries or indexing
Hazelcast achieves these features by having a well-known schema of objects and replicating them across the cluster which enables members and clients to fetch schemas they don’t have in their local registries. Each serialized object carries just a schema identifier and relies on the schema distribution service or configuration to match identifiers with the actual schema. Once the schemas are fetched, they are cached locally on the members and clients so that the next operations that use the schema do not incur extra costs.
Schemas help Hazelcast to identify the locations of the fields on the serialized binary data. With this information, Hazelcast can deserialize individual fields of the data, without reading the whole binary. This results in a better query and indexing performance.
Schemas can evolve freely by adding or removing fields. Even, the types of the fields can be changed. Multiple versions of the schema may live in the same cluster and both the old and new readers may read the compatible parts of the data. This feature is especially useful in rolling upgrade scenarios.
The Compact serialization does not require any changes in the user classes as it doesn’t need a class to implement a particular interface. Serializers might be implemented and specified separately from the classes.
The underlying format of the compact serialized objects is platform and language independent.
Refer to documentation for more details about compact binary serialization.
Another way to use compact serialization is to implement the hz_serializer<T> : compact::compact_serializer specialization for a T. A basic serializer could look like:
class PersonDTO
{
int age;
std::string name;
std::string surname;
};
namespace hazelcast {
namespace client {
namespace serialization {
template<>
struct hz_serializer<PersonDTO> : compact::compact_serializer
{
static void write(const PersonDTO& object, compact::compact_writer& out)
{
out.write_int32("age", object.age);
out.write_string("name", object.name);
out.write_string("surname", object.surname);
}
static PersonDTO read(compact::compact_reader& in)
{
PersonDTO person;
person.age = in.read_int32("age");
boost::optional<std::string> name = in.read_string("name");
if (name) {
person.name = *name;
}
boost::optional<std::string> surname = in.read_string("surname");
if (surname) {
person.surname = *surname;
}
return person;
}
static std::string type_name() { return "person"; }
};
} // namespace serialization
} // namespace client
} // namespace hazelcastCompact serialization permits schemas and classes to evolve by adding or removing fields, or by changing the types of fields. More than one version of a class may live in the same cluster and different clients or members might use different versions of the class.
Hazelcast handles the versioning internally. So, you don’t have to change anything in the classes or serializers apart from the added, removed, or changed fields.
Hazelcast achieves this by identifying each version of the class by a unique fingerprint. Any change in a class results in a different fingerprint. Hazelcast uses a 64-bit Rabin Fingerprint to assign identifiers to schemas, which has an extremely low collision rate.
Different versions of the schema with different identifiers are replicated in the cluster and can be fetched by clients or members internally. That allows old readers to read fields of the classes they know when they try to read data serialized by a new writer. Similarly, new readers might read fields of the classes available in the data, when they try to read data serialized by an old writer.
This means that for one type name, there can be several schemas.
In addition, the compact::compact_reader class exposes methods such as field_kind get_field_kind(string name) which returns the kind (i.e. the actual type) of the field.
Compact serialization introduces the generic_record and generic_record_builder classes, which represents a container and builder object that can be used in place of domain classes. The client always knows how to (de) serialize compact instances and therefore does not require any configuration in order to handle them.
A new record can be created as such:
using namespace hazelcast::client::serialization::generic_record;
generic_record record = generic_record_builder{ "type-name" }
.set_boolean("field-name-1", true)
.set_int32("field-name-2", 123)
.set_string("field-name-3", "hello")
.build();
// Put into map and wait
map->put(1234, record).get();A generic record can be used as such:
auto rec = map->get<int, generic_record>(1234).get();
bool field1 = rec->get_boolean("field-name-1");
int field2 = rec->get_int32("field-name-2");
boost::optional<std::string> field3 = rec->get_string("field-name-3");Refer to the general documentation for more details on how to access domain objects without domain classes. Supported types are listed here.
Hazelcast lets you plug a custom serializer to be used for serialization of objects. It allows you alsoan integration point for any external serialization frameworks such as protobuf, flatbuffers, etc.
A sample custom_serializer implementation of a Person class looks like the following:
struct Person {
std::string name;
bool male;
int32_t age;
};
namespace hazelcast {
namespace client {
namespace serialization {
template<>
struct hz_serializer<Person> : custom_serializer {
static constexpr int32_t get_type_id() noexcept {
return 3;
}
static void write(const Person &object, object_data_output &out) {
out.write(object.name);
out.write(object.male);
out.write(object.age);
}
static Person read(object_data_input &in) {
return Person{in.read<std::string>(), in.read<bool>(), in.read<int32_t>()};
}
};
}
}
}hz_serializer<Person> specialization of hz_serializer should implement the above three methods, namely get_type_id, write, read. In case that the object fields are non-public, you can always define struct hz_serializer<Person> as friend to your object class. You use the object_data_output class methods when serializing the object into binary bytes and you use the object_data_input class methods when de-serializing the bytes into the concrete object instance.
You can use the JSON formatted strings as objects in Hazelcast cluster. Creating JSON objects in the cluster does not require any server side coding and hence you can just send a JSON formatted string object to the cluster and query these objects by fields.
In order to use JSON serialization, you should use the hazelcast_json_value object for the key or value. Here is an example imap usage:
auto hz = hazelcast::new_client().get();
auto map = hz.get_map("map").get();
map->put("item1", hazelcast::client::hazelcast_json_value("{ \"age\": 4 }")).get();
map->put("item2", hazelcast::client::hazelcast_json_value("{ \"age\": 20 }")).get();hazelcast_json_value is a simple wrapper and identifier for the JSON formatted strings. You can get the JSON string from the hazelcast_json_value object using the to_string() method.
You can construct a hazelcast_json_value using one of the constructors. All constructors accept the JSON formatted string as the parameter. No JSON parsing is performed but it is your responsibility to provide correctly formatted JSON strings. The client will not validate the string, and it will send it to the cluster as it is. If you submit incorrectly formatted JSON strings and, later, if you query those objects, it is highly possible that you will get formatting errors since the server will fail to deserialize or find the query fields.
You can query JSON objects in the cluster using the predicates of your choice. An example JSON query for querying the values whose age is less than 6 is shown below:
// Get the objects whose age is less than 6
auto result = map->values<hazelcast::client::hazelcast_json_value>(
hazelcast::client::query::greater_less_predicate(hz, "age", 6, false, true)).get();The global serializer is registered as a fallback serializer to handle all other objects if a serializer cannot be located for them.
By default, global serializer is used if there are no specialization hz_serializer<my_class> for my class my_class.
Use Cases:
- Third party serialization frameworks can be integrated using the global serializer.
- For your custom objects, you can implement a single serializer to handle all object serializations.
A sample global serializer that integrates with a third party serializer is shown below.
class MyGlobalSerializer : public hazelcast::client::serialization::global_serializer {
public:
void write(const boost::any &obj, hazelcast::client::serialization::object_data_output &out) override {
auto const &object = boost::any_cast<Person>(obj);
out.write(object.name);
out.write(object.male);
out.write(object.age);
}
boost::any read(hazelcast::client::serialization::object_data_input &in) override {
return boost::any(Person{in.read<std::string>(), in.read<bool>(), in.read<int32_t>()});
}
};As you see fromn the sample, the global serializer class should implement the hazelcast::client::serialization::global_serializer interface.
You should register the global serializer in the configuration.
hazelcast::client::client_config config;
config.get_serialization_config().set_global_serializer(std::make_shared<MyGlobalSerializer>());
auto hz = hazelcast::new_client(std::move(config)).get();You need to utilize the boost::any_cast methods tyo actually use the objects provided for serialization and you are expected to return type boost::any from the read method.
All network related configuration of Hazelcast C++ client is performed programmatically via the client_network_config object. The following is an example configuration.
Here is an example of configuring the network for C++ Client programmatically.
client_config clientConfig;
clientConfig.get_network_config().add_addresses({{"10.1.1.21", 5701}, {"10.1.1.22", 5703}});
clientConfig.get_network_config().set_smart_routing(true);
clientConfig.set_redo_operation(true);
clientConfig.get_connection_strategy_config().get_retry_config().set_cluster_connect_timeout(
std::chrono::seconds(30));Address list is the initial list of cluster addresses which the client will connect to. The client uses this list to find an alive member. Although it may be enough to give only one address of a member in the cluster (since all members communicate with each other), it is recommended that you give the addresses for all the members.
client_config clientConfig;
clientConfig.get_network_config().add_address(address("10.1.1.21", 5701), address("10.1.1.22", 5703));The provided list is shuffled and tried in a random order. If no address is added to the client_network_config, then 127.0.0.1:5701 is tried by default.
Smart routing defines whether the client mode is smart or unisocket. See the C++ Client Operation Modes section for the description of smart and unisocket modes.
The following is an example configuration.
client_config clientConfig;
clientConfig.get_network_config().set_smart_routing(true);Its default value is true (smart client mode).
It enables/disables redo-able operations. While sending the requests to the related members, the operations can fail due to various reasons. Read-only operations are retried by default. If you want to enable retry for the other operations, you can set the redo_operation to true.
client_config clientConfig;
clientConfig.set_redo_operation(true);Its default value is false (disabled).
Cluster connection timeout is the timeout value for which the client tries to connect to the cluster. If the client can not connect to cluster during this timeout duration, the client shuts down itself and it can not be re-used (you need to obtain a new client).
The following example shows how you can set the cluster connection timeout to 30 seconds.
client_config().get_connection_strategy_config().get_retry_config().set_cluster_connect_timeout(std::chrono::seconds(30));When client is disconnected from the cluster, it searches for new connections to reconnect. You can configure the frequency of the reconnection attempts and client shutdown behavior using connection_retry_config.
The following is an example configuration.
Below is an example configuration. It configures a total timeout of 30 seconds to connect to a cluster, by initial backoff time being 100 milliseconds and doubling the time before every try with a jitter of 0.8 up to a maximum of 3 seconds backoff between each try..
client_config().get_connection_strategy_config().get_retry_config().set_cluster_connect_timeout(
std::chrono::seconds(30)).set_multiplier(2.0).set_jitter(0.8).set_initial_backoff_duration(
std::chrono::seconds(100)).set_max_backoff_duration(std::chrono::seconds(3));The following are configuration element descriptions:
initial_backoff_duration: Specifies how long to wait (backoff) after the first failure before retrying.max_backoff_duration: Specifies the upper limit for the backoff between each cluster connect tries.multiplier: Factor to multiply the backoff after a failed retry.cluster_connect)timeout: Timeout value for the client to give up to connect to the current cluster. If the client can not connect during this time, then it shuts down and it can not be re-used.jitter: Specifies by how much to randomize backoffs.
You can use TLS/SSL to secure the connection between the clients and members. If you want to enable TLS/SSL for the client-cluster connection, you should set an SSL configuration. Please see the TLS/SSL section.
As explained in the TLS/SSL section, Hazelcast members have key stores used to identify themselves (to other members) and Hazelcast C++ clients have certificate authorities used to define which members they can trust.
The C++ client can discover the existing Hazelcast servers in the Amazon AWS environment. The client queries the Amazon AWS environment using the [describe-instances] (http://docs.aws.amazon.com/cli/latest/reference/ec2/describe-instances.html) query of AWS. The client finds only the up and running instances and filters them based on the filter config provided at the client_aws_config configuration.
The following is an example configuration:
clientConfig.get_network_config().get_aws_config()
.set_enabled(true)
.set_access_key("my access key id")
.set_secret_key("my secret access key")
.set_tag_key("my tag key")
.set_tag_value("my tag value")
.set_security_group_name("my secure group")
.set_region("us-east-1");You need to enable the discovery by calling the set_enabled(true). You can set your access key and secret in the configuration as shown in this example. You can filter the instances by setting which tags they have or by the security group setting. You can set the region for which the instances will be retrieved from, the default region is us-east-1.
See client_aws_config.h and the AWS examples directory for more details.
If you are using Hazelcast Cloud Service and you want to write an application that will utilize the Hazelcast cloud database service, you can use the C++ client. The configuration is very simple, you just need to set the cluster name, discovery token and enable the cloud discovery on network settings. Here is an example configuration:
hazelcast::client::client_config config;
config.set_cluster_name("my_cluster(should match with the same cluster name at the cloud web site)");
auto &cloud_configuration = config.get_network_config().get_cloud_config();
cloud_configuration.enabled = true;
cloud_configuration.discovery_token = "My token for the cluster (you can get from the cloud web site)";
auto hz = hazelcast::new_client(std::move(config)).get();That is all the configuration you will need. The client will query the hazelcast coordinator service located at "https://coordinator.hazelcast.cloud/cluster/discovery?token=my_token" with the token you configured and will get a list of available server ip port combinations to connect to. The client will always use the public ip to connect to the cluster. The network connection timeout (client_network_config::set_connection_timeout(const std::chrono::milliseconds &timeout)) is used as the timeout value for connecting the coordinator and retrieving the cluster member list (change this setting if you have any timeout problems).
Please check the code sample at examples/cloud-discovery/connect-cloud.cpp for a full featured cloud discovery example.
You can create a Hazelcast cluster in the cloud with "Enable Encryption" option. When this option is selected the cluster requires the clients to connect using the SSL connection and the client should be configured to do mutual authentication. The required certificate authority file, client certificate, client key file and key file PEM pass phrase are located at the Hazelcast cloud web site cluster configuration Configure Clients page. Download the keystore file and unzip it (it will be folder such as hzcloud_xxx_keys where xxx is the cluster number), this zip includes all the required files. Also, copy the Keystore and truststore password which is the client key PEM file pass phrase. Once, you have all this information in hand, you can configure the client as in the following code snippet:
std::string cluster_name = "my_cluster";
std::string cloud_token = "my cloud token for the cluster";
std::string ca_file_path = "/path/to/hzcloud_xxx_keys/ca.pem";
std::string client_certificate = "/path/to/hzcloud_xxx_keys/cert.pem";
std::string client_key = "/path/to/hzcloud_xxx_keys/key.pem";
std::string client_key_pem_pass_phrase = "Keystore and truststore password";
hazelcast::client::client_config config;
config.set_cluster_name(cluster_name);
auto &cloud_configuration = config.get_network_config().get_cloud_config();
cloud_configuration.enabled = true;
cloud_configuration.discovery_token = cloud_token;
// ssl configuration
boost::asio::ssl::context ctx(boost::asio::ssl::context::tlsv12);
ctx.set_verify_mode(boost::asio::ssl::verify_peer);
ctx.load_verify_file(ca_file_path);
ctx.use_certificate_file(client_certificate, boost::asio::ssl::context::pem);
ctx.set_password_callback([&] (std::size_t max_length, boost::asio::ssl::context::password_purpose purpose) {
return client_key_pem_pass_phrase;
});
ctx.use_private_key_file(client_key, boost::asio::ssl::context::pem);
config.get_network_config().get_ssl_config().set_context(std::move(ctx));
auto hz = hazelcast::new_client(std::move(config)).get();As you see in the code snippet, we provide a boost::asio::ssl::context in the client configuration in addition to the cloud token and cluster name configurations. It uses mutual authentication (two-way handshake).
Please check the code sample at examples/cloud-discovery/ssl-connect-cloud.cpp for a full featured cloud discovery with SSL example.
NOTE: This feature requires Hazelcast 4.2 or higher version.
The client sends requests directly to cluster members in the smart client mode (default) in order to reduce hops to accomplish operations. Because of that, the client should know the addresses of members in the cluster.
In cloud-like environments, or Kubernetes, there are usually two network interfaces: the private and public network interfaces. When the client is in the same network as the members, it uses their private network addresses. Otherwise, if the client and the Hazelcast cluster are on different networks, the client cannot connect to members using their private network addresses. Hazelcast 4.2 introduced External Smart Client Discovery to solve that issue. The client needs to communicate with all cluster members via their public IP addresses in this case. Whenever Hazelcast cluster members are able to resolve their own public external IP addresses, they pass this information to the client. As a result, the client can use public addresses for communication.
In order to use this feature, make sure your cluster members are accessible from the network the client resides in,
then set config client_network_config()::use_public_address(true) to true. You should specify the public address of
at least one member in the configuration:
hazelcast::client::client_config config;
constexpr int server_port = 5701;
constexpr const char *server_public_address = "myserver.publicaddress.com";
config.get_network_config().use_public_address(true).add_address(
hazelcast::client::address{server_public_address, server_port});This solution works everywhere without further configuration (Kubernetes, AWS, GCP, Azure, etc.) as long as the corresponding plugin is enabled in Hazelcast server configuration.
By default, the client does not use any authentication method and just uses the cluster "dev" to connect to. You can change which cluster to connect by using the configuration API client_config::set_cluster_name. This way, you can have multiple clusters in the network but the client will only be able to connect to the cluster with the correct cluster name (server should also be started with the cluster name configured to the same cluster name).
If you want to enable authentication, then the client can be configured in one of the two different ways to authenticate:
- Username password based authentication
- Token based authentication
The following is an example configuration where we set the username test-user and password test-pass to be used while trying to connect to the cluster:
hazelcast::client::client_config config;
config.set_credentials(std::make_shared<hazelcast::client::security::username_password_credentials>("test-user", "test-pass"));
auto hz = hazelcast::new_client(std::move(config)).get();Note that the server needs to be configured to use the same username and password. An example server xml config is:
<security enabled="true">
<realms>
<realm name="usernamePasswordIdentityRealm">
<authentication>
<simple>
<user username="test-user" password="test-pass"/>
</simple>
</authentication>
</realm>
</realms>
<client-authentication realm="usernamePasswordIdentityRealm"/>
</security>The following is an example configuration where we set the secret token bytes to be used while trying to connect to the cluster:
std::vector<hazelcast::byte> my_token = {'S', 'G', 'F', '6', 'Z', 'W'};
config.set_credentials(std::make_shared<hazelcast::client::security::token_credentials>(my_token));
auto hz = hazelcast::new_client(std::move(config)).get();Implementing a custom login module is mandatory to use token authentication by itself. An example custom login module is:
import com.hazelcast.config.Config;
import com.hazelcast.security.ClusterLoginModule;
import com.hazelcast.security.Credentials;
import com.hazelcast.security.CredentialsCallback;
import com.hazelcast.security.TokenCredentials;
import javax.security.auth.callback.Callback;
import javax.security.auth.callback.UnsupportedCallbackException;
import javax.security.auth.login.FailedLoginException;
import javax.security.auth.login.LoginException;
import java.io.IOException;
import java.util.Arrays;
public class TokenLoginModule extends ClusterLoginModule {
private String name;
private final byte[] expectedToken = "SGF6ZW".getBytes(StandardCharsets.US_ASCII);
@Override
protected boolean onLogin() throws LoginException {
CredentialsCallback cb = new CredentialsCallback();
name = cb.getCredentials().getName();
try {
callbackHandler.handle(new Callback[] { cb });
} catch (IOException | UnsupportedCallbackException e) {
throw new LoginException("Problem getting credentials");
}
Credentials credentials = cb.getCredentials();
if (!(credentials instanceof TokenCredentials)) {
throw new FailedLoginException();
}
byte[] actualToken = ((TokenCredentials) credentials).getToken();
if (Arrays.equals(actualToken, expectedToken)) {
return true;
}
throw new LoginException("Invalid token");
}
@Override
protected String getName() {
return name;
}
}Note that the server needs to be configured to use the same token. An example server xml config is:
<security enabled="true">
<realms>
<realm name="jaasRealm">
<authentication>
<jaas>
<login-module class-name="com.example.TokenLoginModule" usage="REQUIRED"/>
</jaas>
</authentication>
</realm>
</realms>
<client-authentication realm="jaasRealm"/>
</security>When an operation with sync backup is sent by a client to the Hazelcast member(s), the acknowledgment of the operation's backup is sent to the client by the backup replica member(s). This improves the performance of the client operations.
To disable backup acknowledgement, you should use the backup_acks_enabled configuration option.
// Disable the default backup ack feature
hazelcast_client hz(client_config().backup_acks_enabled(false));Its default value is true. This option has no effect for unisocket clients.
You can also fine-tune this feature using client_config::set_protperty API as described below:
-
hazelcast.client.operation.backup.timeout.millis: Default value is5000milliseconds. If an operation has backups, this property specifies how long (in milliseconds) the invocation waits for acks from the backup replicas. If acks are not received from some of the backups, there will not be any rollback on the other successful replicas. -
hazelcast.client.operation.fail.on.indeterminate.state: Default value isfalse. When it istrue, if an operation has sync backups and acks are not received from backup replicas in time, or the member which owns primary replica of the target partition leaves the cluster, then the invocation fails. However, even if the invocation fails, there will not be any rollback on other successful replicas.
This chapter describes the security features of the Hazelcast C++ client. These include using TLS/SSL for connections between members and between clients and members. These security features require Hazelcast Enterprise server cluster.
One of the offers of Hazelcast is the TLS/SSL protocol which you can use to establish an encrypted communication between the client and the server.
Hazelcast allows you to encrypt socket level communication between Hazelcast members and between Hazelcast clients and members, for end to end encryption. To use it, see the TLS/SSL for Hazelcast Members section in the Hazelcast Reference Manual.
The Hazelcast C++ client uses Boost Asio library for networking and secure communication.
To use TLS/SSL with your Hazelcast C++ client, you should build the library with OpenSSL feature turned on. By default, this feature is turned off. Here are the different ways to install the library with SSL support:
- CMake Users: Provide flag
-DWITH_OPENSSL=ONflag when configuring. Please note that the CMakefind_packageshould be able to locate the OpenSSL installation, otherwise the cmake config will fail. The rest is as usual with the installation with cmake. - Conan Users: Use option
with_opensslwhile installing the conan project. An example command you can use in the build folder of your project with conan:conan install -o hazelcast-cpp-client:with_openssl=True .. --build=hazelcast-cpp-client. This command will install thehazelcast-cpp-clientwith the OpenSSL support.
Once the library is installed properly, you can enable the SSL feature in the client configuration.
You can set the protocol type. If not set, the configuration uses tlsv12 (TLSv1.2) as the default protocol type and version.
There may be different ways to configure ssl communication at the client side. We utilize the boost::asio::ssl::context to configure the SSL communication. You can set the verify mode to ignore or verify the server certificate, set the used SSL protocol, add verify callbacks, and such. The details can be found at the Boost asio ssl context documentation.
Here is the most basic way to enable the SSL on the client:
hazelcast::client::client_config config;
boost::asio::ssl::context ctx(boost::asio::ssl::context::method::tlsv12_client);
config.get_network_config().get_ssl_config().set_context(std::move(ctx));
auto hz = hazelcast::new_client(std::move(config)).get();Once, we set the SSL context, the client will start with SSL enabled and try to connect to the server using the provided context.
If you want to enable the validation of server certificate on the client side and disable any connection without a valid certificate, you can use the context::set_verify_mode API. Here is an example:
hazelcast::client::client_config config;
boost::asio::ssl::context ctx(boost::asio::ssl::context::method::tlsv12_client);
ctx.set_verify_mode(boost::asio::ssl::verify_peer);
ctx.set_default_verify_paths();
config.get_network_config().get_ssl_config().set_context(std::move(ctx));
auto hz = hazelcast::new_client(std::move(config)).get();The client will connect if the server has a certificate which is properly signed by well known Certificate Authorities(CA). On Windows, you may need to set the well known CA Authorities file path since OpenSSL may not find the well known certifite authorities file. This can be solved by providing the SSL_CERT_FILE environment variable set to point a file path that has all the needed well known certificate authorities. For example, you can get such a file maintained by Mozilla at https://curl.se/docs/caextract.html.
If the server is using a user generated certificate file which is not signed by the well known CA authorities, then you can use the public certificate of the server to configure the client to connect and verify the server. Here is an example configuration for this:
hazelcast::client::client_config config;
boost::asio::ssl::context ctx(boost::asio::ssl::context::method::tlsv12_client);
ctx.set_verify_mode(boost::asio::ssl::verify_peer);
ctx.set_default_verify_paths();
ctx.load_verify_file("/path/to/my/server/public/certificate");
config.get_network_config().get_ssl_config().set_context(std::move(ctx));
auto hz = hazelcast::new_client(std::move(config)).get();As you can see in this code snippet, we add the server public certificate using context::load_verify_file as a verify file so that the client can validate the server certificate as valid and connect to the server. There are also other ways to load verify files and use verify directories. You can find the details at Boost Asio ssl context documentation.
You can check BasicTLSClient.cpp example for a full featured example.
Mutual Authentication is the process where the client verifies the identity of the server via server's certificate (either self-signed or signed by a CA authority) and the server verifies the client identity via the client provided certificate (either self-signed or signed by a CA authority). If the Hazelcast server is configured for mutual authentication as REQUIRED, then we can use the ssl::context::use_xxx methods to add the client's public and private certificates and use them during its authentication to the server. An example configuration is as follows:
hazelcast::client::client_config config;
boost::asio::ssl::context ctx(boost::asio::ssl::context::method::tlsv12_client);
ctx.set_verify_mode(boost::asio::ssl::verify_peer);
ctx.set_default_verify_paths();
// This config is to validate the server certificate if server does not have a CA signed certificate
ctx.load_verify_file("/path/to/my/server/public/certificate");
// The following two lines configure the client to use the client certificate to introduce itself to the server
ctx.use_certificate_file("/path/to/my/client/public/certificate", boost::asio::ssl::context::pem);
ctx.use_private_key_file("/path/to/my/client/private/certificate", boost::asio::ssl::context::pem);
config.get_network_config().get_ssl_config().set_context(std::move(ctx));
auto hz = hazelcast::new_client(std::move(config)).get();With the above configuration, both the client and server authenticate each other mutually. The client validates the server certificate and the server validates the client with the client certificate.
You can check mutual_authentication.cpp for a full featured example.
In some cases, you may want to limit the cipher suites allowed for a client while communicating with the server. This can also be configured using ssl_config. Here is an example configuration to enable cipher list which allow only "HIGH" ciphers.
config.get_network_config().get_ssl_config().
set_cipher_list("HIGH"); // optional setting (values for string are described at
// https://www.openssl.org/docs/man1.0.2/apps/ciphers.html and
// https://www.openssl.org/docs/man1.1.1/man1/ciphers.htmlCipher list string format and the details can be found at the official OpenSSL Documentation.
This chapter provides information on how you can use Hazelcast data structures in the C++ client, after giving some basic information including an overview to the client API, operation modes of the client and how it handles the failures.
Most of the methods in C++ API are synchronous. The failures are communicated via exceptions. All exceptions are derived from the hazelcast::client::exception::iexception base method. There are also asynchronous versions of some methods in the API. The asynchronous ones use the hazelcast::client::Future<T> future object. It works similar to the std::future.
If you are ready to go, let's start to use Hazelcast C++ client!
The first step is the configuration. You can configure the C++ client programmatically. See the Programmatic Configuration section for details.
The following is an example on how to create a client_config object and configure it programmatically:
client_config clientConfig;
clientConfig.set_cluster_name("my test cluster");
clientConfig.get_network_config().add_addresses({{"'10.90.0.1", 5701}, {"'10.90.0.2", 5702}});The second step is initializing the hazelcast_client to be connected to the cluster:
auto client = new_client(std::move(clientConfig));
// some operationThis client object is your gateway to access all the Hazelcast distributed objects.
Let's create a map and populate it with some data, as shown below.
// Get the Distributed Map from Cluster.
auto map = client.get_map("my-distributed-map").get();
//Standard put and get.
map->put<std::string, std::string>("key", "value").get();
map->get<std::string, std::string>("key").get();
//Concurrent Map methods, optimistic updating
map->put_if_absent<std::string, std::string>("somekey", "somevalue").get();
map->replace<std::string, std::string>("key", "value", "newvalue").get();As the final step, if you are done with your client, you can shut it down as shown below. This will release all the used resources and close connections to the cluster.
// Shutdown this Hazelcast Client
client.shutdown().get();The client object destructor also shuts down the client upon destruction if you do not explicitly call the shutdown method.
The client has two operation modes because of the distributed nature of the data and cluster: smart and unisocket.
In the smart mode, the clients connect to each cluster member. Since each data partition uses the well known and consistent hashing algorithm, each client can send an operation to the relevant cluster member, which increases the overall throughput and efficiency. Smart mode is the default mode.
For some cases, the clients can be required to connect to a single member instead of each member in the cluster. Firewalls, security or some custom networking issues can be the reason for these cases.
In the unisocket client mode, the client will only connect to one of the configured addresses. This single member will behave as a gateway to the other members. For any operation requested from the client, it will redirect the request to the relevant member and return the response back to the client returned from this member.
You can set the unisocket client mode in the client_config as shown below.
clientConfig.get_network_config().set_smart_routing(false);There are two main failure cases you should be aware of. Below sections explain these and the configurations you can perform to achieve proper behavior.
While the client is trying to connect initially to one of the members in the client_network_config::get_addresses(), all the members might not be available. Instead of giving up, throwing an error and stopping the client, the client retries to connect as configured which is described in the Advanced Cluster Connection Retry Configuration section.
The client executes each operation through the already established connection to the cluster. If this connection(s) disconnects or drops, the client tries to reconnect as configured.
While sending the requests to the related members, the operations can fail due to various reasons. Read-only operations are retried by default. If you want to enable retrying for the other operations, you can set the redo_operation to true. See the Enabling Redo Operation section.
You can set a timeout for retrying the operations sent to a member. This can be provided by using the property hazelcast.client.invocation.timeout.seconds in client_config.properties. The client will retry an operation within this given period, of course, if it is a read-only operation or you enabled the redo_operation as stated in the above paragraph. This timeout value is important when there is a failure resulted by either of the following causes:
- Member throws an exception.
- Connection between the client and member is closed.
- Client’s heartbeat requests are timed out.
When a connection problem occurs, an operation is retried if it is certain that it has not run on the member yet or if it is idempotent such as a read-only operation, i.e., retrying does not have a side effect. If it is not certain whether the operation has run on the member, then the non-idempotent operations are not retried. However, as explained in the first paragraph of this section, you can force all the client operations to be retried (redo_operation) when there is a connection failure between the client and member. But in this case, you should know that some operations may run multiple times causing conflicts. For example, assume that your client sent a queue->offer operation to the member and then the connection is lost. Since there will be no response for this operation, you will not know whether it has run on the member or not. If you enabled redo_operation, it means this operation may run again, which may cause two instances of the same object in the queue.
When invocation is being retried, the client may wait some time before it retries again. This duration can be configured using the following property:
config.set_property(“hazelcast.client.invocation.retry.pause.millis”, “500");
The default retry wait time is 1 second.
Hazelcast uses operations to make remote calls. For example, a map->get is an operation and a map->put is one operation for the primary
and one operation for each of the backups, i.e., map->put is executed for the primary and also for each backup. In most cases, there will be a natural balance between the number of threads performing operations
and the number of operations being executed. However, there are two situations where this balance and operations
can pile up and eventually lead to out_of_memory_exception (OOME):
- Asynchronous calls: With async calls, the system may be flooded with the requests.
- Asynchronous backups: The asynchronous backups may be piling up.
To prevent the system from crashing, Hazelcast provides back pressure. Back pressure works by:
- limiting the number of concurrent operation invocations,
- periodically making an async backup sync.
Sometimes, e.g., when your servers are overloaded, you may want to slow down the client operations to the cluster. Then the client can be configured to wait until number of outstanding invocations whose responses are not received to become less than a certain number. This is called Client Back Pressure. By default, the backpressure is disabled. There are a few properties which control the back pressure. The following are these client configuration properties:
hazelcast.client.max.concurrent.invocations: The maximum number of concurrent invocations allowed. To prevent the system from overloading, you can apply a constraint on the number of concurrent invocations. If the maximum number of concurrent invocations has been exceeded and a new invocation comes in, then Hazelcast will throwhazelcast_overload. By default this property is configured as INT32_MAX.hazelcast.client.invocation.backoff.timeout.millis: Controls the maximum timeout in milliseconds to wait for an invocation space to be available. If an invocation can't be made because there are too many pending invocations, then an exponential backoff is done to give the system time to deal with the backlog of invocations. This property controls how long an invocation is allowed to wait before gettinghazelcast_overload. When set to -1 thenhazelcast_overloadis thrown immediately without any waiting. This is the default value.
For details of backpressure, see the Back Pressure section in the Hazelcast Reference Manual.
Hazelcast client-cluster connection and reconnection strategy can be configured. Sometimes, you may not want your application to wait for the client to connect to the cluster, you may just want to get the client and let the client connect in the background. This is configured as follows:
client_config::get_connection_strategy_config().set_async_start(bool);
When this configuration is set to true, the client creation won't wait to connect to cluster. The client instance will throw an exception for any request, until it connects to the cluster and become ready.
If it is set to false (the default case), hazelcast_client(const client_config) will block until a cluster connection is established and it's ready to use the client instance.
You can configure how the client should act when the client disconnects from the cluster for any reason. This is configured as follows:
client_config::get_connection_strategy_config().set_reconnect_mode(const hazelcast::client::config::client_connection_strategy_config::reconnect_mode &);
Possible values for reconnect_mode are:
OFF: Prevents reconnection to the cluster after a disconnect.ON: Reconnects to the cluster by blocking invocations.ASYNC: Reconnects to the cluster without blocking invocations. Invocations will receivehazelcast_client_offline.
Most of the distributed data structures are supported by the C++ client. In this chapter, you will learn how to use these distributed data structures.
Hazelcast Map (imap) is a distributed map. Through the C++ client, you can perform operations like reading and writing from/to a Hazelcast Map with the well known get and put methods. For details, see the Map section in the Hazelcast Reference Manual.
A Map usage example is shown below.
// Get the Distributed Map from Cluster.
auto map = hz.get_map("my-distributed-map").get();
//Standard Put and Get.
map->put<std::string, std::string>("key", "value").get();
map->get<std::string, std::string>("key").get();
//Concurrent Map methods, optimistic updating
map->put_if_absent<std::string, std::string>("somekey", "somevalue").get();
map->replace<std::string, std::string>("key", "value", "newvalue").get();Hazelcast multi_map is a distributed and specialized map where you can store multiple values under a single key. For details, see the MultiMap section in the Hazelcast Reference Manual.
A multi_map usage example is shown below.
// Get the Distributed multi_map from Cluster.
auto multiMap = hz.get_multi_map("my-distributed-multimap").get();
// Put values in the map against the same key
multiMap->put<std::string, std::string>("my-key", "value1").get();
multiMap->put<std::string, std::string>("my-key", "value2").get();
multiMap->put<std::string, std::string>("my-key", "value3").get();
// Print out all the values for associated with key called "my-key"
for (const auto &value : multiMap->get<std::string, std::string>("my-key").get()) {
std::cout << value << std::endl; // will print value1, value2 and value3 each per line.
}
// remove specific key/value pair
multiMap->remove<std::string, std::string>("my-key", "value2").get();Hazelcast replicated_map is a distributed key-value data structure where the data is replicated to all members in the cluster. It provides full replication of entries to all members for high speed access. For details, see the ReplicatedMap section in the Hazelcast Reference Manual.
A replicated_map usage example is shown below.
auto replicatedMap = hz.get_replicated_map("myReplicatedMap").get();
replicatedMap->put<int, std::string>(1, "Furkan").get();
replicatedMap->put<int, std::string>(2, "Ahmet").get();
std::cout << "Replicated map value for key 2 is " << *replicatedMap->get<int, std::string>(2).get() << std::endl; // Replicated map value for key 2 is AhmetHazelcast Queue (iqueue) is a distributed queue which enables all cluster members to interact with it. For details, see the Queue section in the Hazelcast Reference Manual.
A Queue usage example is shown below.
// Get a Blocking Queue called "my-distributed-queue"
auto queue = hz.get_queue("my-distributed-queue").get();
// Offer a String into the Distributed Queue
queue->offer<std::string>("item").get();
// Poll the Distributed Queue and return the String
auto item = queue->poll<std::string>().get(); // gets first "item"
//Timed blocking Operations
queue->offer<std::string>("anotheritem", 500).get();
auto anotherItem = queue->poll<std::string>(5 * 1000).get();
//Indefinitely blocking Operations
queue->put<std::string>("yetanotheritem").get();
std::cout << *queue->take<std::string>().get() << std::endl; // Will print yetanotheritemHazelcast Set (iset) is a distributed set which does not allow duplicate elements. For details, see the Set section in the Hazelcast Reference Manual.
A Set usage example is shown below.
// Get the Distributed Set from Cluster.
auto set = hz.get_set("my-distributed-set").get();
// Add items to the set with duplicates
set->add<std::string>("item1").get();
set->add<std::string>("item1").get();
set->add<std::string>("item2").get();
set->add<std::string>("item2").get();
set->add<std::string>("item2").get();
set->add<std::string>("item3").get();
// Get the items. Note that there are no duplicates.
for (const auto &value : set->toArray().get()) {
std::cout << value << std::endl;
}Hazelcast List (ilist) is a distributed list which allows duplicate elements and preserves the order of elements. For details, see the List section in the Hazelcast Reference Manual.
A List usage example is shown below.
// Get the Distributed List from Cluster.
auto list = hz.get_list("my-distributed-list").get();
// Add elements to the list
list->add<std::string>("item1").get();
list->add<std::string>("item2").get();
// Remove the first element
std::cout << "Removed: " << *list.remove<std::string>(0).get();
// There is only one element left
std::cout << "Current size is " << list.size().get() << std::endl;
// Clear the list
list.clear().get();Hazelcast ringbuffer is a replicated but not partitioned data structure that stores its data in a ring-like structure. You can think of it as a circular array with a given capacity. Each ringbuffer has a tail and a head. The tail is where the items are added and the head is where the items are overwritten or expired. You can reach each element in a ringbuffer using a sequence ID, which is mapped to the elements between the head and tail (inclusive) of the ringbuffer. For details, see the Ringbuffer section in the Hazelcast Reference Manual.
A ringbuffer usage example is shown below.
auto rb = hz.get_ring_buffer("rb").get();
// add two items into ring buffer
rb->add(100).get();
rb->add(200).get();
// we start from the oldest item.
// if you want to start from the next item, call rb.tailSequence()+1
auto sequence = rb->head_sequence().get();
std::cout << *rb->read_one<int>(sequence).get() << std::endl; //
sequence++;
std::cout << *rb->read_one<int>(sequence).get() << std::endl;Hazelcast reliable_topic is a distributed topic implementation backed up by the ringbuffer data structure. For details, see the Reliable Topic section in the Hazelcast Reference Manual.
A reliable_topic usage example is shown below.
hazelcast::client::topic::reliable_listener make_listener(std::atomic<int> &n_received_messages, int64_t sequence_id = -1) {
using namespace hazelcast::client::topic;
return reliable_listener(false, sequence_id).on_received([&n_received_messages](message &&message){
++n_received_messages;
auto object = message.get_message_object().get<std::string>();
if (object) {
std::cout << "[GenericListener::onMessage] Received message: " << *object << " for topic:" << message.get_name() << std::endl;
} else {
std::cout << "[GenericListener::onMessage] Received message with NULL object for topic:" << message.get_name() << std::endl;
}
});
}
void listen_with_default_config() {
auto client = hazelcast::new_client().get();
std::string topicName("MyReliableTopic");
auto topic = client.get_reliable_topic(topicName).get();
std::atomic<int> numberOfMessagesReceived{ 0 };
auto listenerId = topic->add_message_listener(make_listener(numberOfMessagesReceived));
std::cout << "Registered the listener with listener id:" << listenerId << std::endl;
while (numberOfMessagesReceived < 1) {
std::this_thread::sleep_for(std::chrono::seconds(1));
}
if (topic->remove_message_listener(listenerId)) {
std::cout << "Successfully removed the listener " << listenerId << " for topic " << topicName << std::endl;
} else {
std::cerr << "Failed to remove the listener " << listenerId << " for topic " << topicName << std::endl;
}
}
void listen_with_config() {
hazelcast::client::client_config clientConfig;
std::string topicName("MyReliableTopic");
hazelcast::client::config::reliable_topic_config reliableTopicConfig(topicName.c_str());
reliableTopicConfig.set_read_batch_size(5);
clientConfig.add_reliable_topic_config(reliableTopicConfig);
auto client = hazelcast::new_client(std::move(clientConfig)).get();
auto topic = client.get_reliable_topic(topicName).get();
std::atomic<int> numberOfMessagesReceived{ 0 };
auto listenerId = topic->add_message_listener(make_listener(numberOfMessagesReceived));
std::cout << "Registered the listener with listener id:" << listenerId << std::endl;
while (numberOfMessagesReceived < 1) {
std::this_thread::sleep_for(std::chrono::seconds(1));
}
if (topic->remove_message_listener(listenerId)) {
std::cout << "Successfully removed the listener " << listenerId << " for topic " << topicName << std::endl;
} else {
std::cerr << "Failed to remove the listener " << listenerId << " for topic " << topicName << std::endl;
}
}Hazelcast pn_counter (Positive-Negative Counter) is a CRDT positive-negative counter implementation. It is an eventually consistent counter given there is no member failure. For details, see the PN Counter section in the Hazelcast Reference Manual.
A pn_counter usage example is shown below.
auto hz = hazelcast::new_client().get();
auto pnCounter = hz.get_pn_counter("pncounterexample").get();
std::cout << "Counter started with value:" << pnCounter->get().get() << std::endl;
std::cout << "Counter new value after adding is: " << pnCounter->add_and_get(5).get() << std::endl;
std::cout << "Counter new value before adding is: " << pnCounter->get_and_add(2).get() << std::endl;
std::cout << "Counter new value is: " << pnCounter->get().get() << std::endl;
std::cout << "Decremented counter by one to: " << pnCounter->decrement_and_get().get() << std::endl;Hazelcast flake_id_generator is used to generate cluster-wide unique identifiers. Generated identifiers are long primitive values and are k-ordered (roughly ordered). IDs are in the range from 0 to 2^63-1 (maximum signed long value). For details, see the Flake ID Generator section in the Hazelcast Reference Manual.
A flake_id_generator usage example is shown below.
auto generator = hz.get_flake_id_generator("flakeIdGenerator").get();
std::cout << "Id : " << generator->newId().get() << std::endl; // Id : <some unique number>Hazelcast 4.0 introduced CP concurrency primitives with respect to the CAP principle, i.e., they always maintain linearizability and prefer consistency over availability during network partitions and client or server failures.
All data structures within CP Subsystem are available through the hazelcast_client::get_cp_subsystem() API.
Before using the CP structures, CP Subsystem has to be enabled on the cluster-side. Refer to the CP Subsystem documentation for more information.
Data structures in CP Subsystem run in CP groups. Each CP group elects its own Raft leader and runs the Raft consensus algorithm independently. The CP data structures differ from the other Hazelcast data structures in two aspects. First, an internal commit is performed on the METADATA CP group every time you fetch a proxy from this interface. Hence, callers should cache the returned proxy objects. Second, if you call DistributedObject::destroy() on a CP data structure proxy, that data structure is terminated on the underlying CP group and cannot be reinitialized until the CP group is force-destroyed. For this reason, please make sure that you are completely done with a CP data structure before destroying its proxy.
Hazelcast atomic_long is the distributed implementation of atomic 64-bit integer counter. It offers various atomic operations such as get, set, get_and_set, compare_and_set, increment_and_get. This data structure is a part of CP Subsystem.
An Atomic Long usage example is shown below.
// also present the future continuation capability. you can use sync version as well.
// Get current value (returns a int64_t)
auto future = atomic_counter->get().then(boost::launch::deferred, [=] (boost::future<int64_t> f) {
// Prints:
// Value: 0
std::cout << "Value: " << f.get() << "\n";
// Increment by 42
auto value = atomic_counter->add_and_get(42).get();
std::cout << "New value: " << value << "\n";
// Set to 0 atomically if the current value is 42
auto result = atomic_counter->compare_and_set(42, 0).get();
std::cout << "result: " << value << "\n";
// Prints:
// CAS operation result: 1
});
future.get();atomic_long implementation does not offer exactly-once / effectively-once execution semantics. It goes with at-least-once execution semantics by default and can cause an API call to be committed multiple times in case of CP member failures. It can be tuned to offer at-most-once execution semantics. Please see the fail-on-indeterminate-operation-state server-side setting.
Hazelcast fenced_lock is the distributed implementation of a linearizable lock. It offers multiple operations for acquiring the lock. This data structure is a part of CP Subsystem.
A basic Lock usage example is shown below.
// Get an fenced_lock named 'my-lock'
auto lock = hz.get_cp_subsystem().get_lock("my-lock").get();
// Acquire the lock
lock->lock().get();
try {
// Your guarded code goes here
} catch (...) {
// Make sure to release the lock
lock->unlock().get();
}fenced_lock works on top of CP sessions. It keeps a CP session open while the lock is acquired. Please refer to the CP Sessions documentation for more information.
Distributed locks are unfortunately not equivalent to single-node mutexes because of the complexities in distributed systems, such as uncertain communication patterns, and independent and partial failures. In an asynchronous network, no lock service can guarantee mutual exclusion, because there is no way to distinguish between a slow and a crashed process. Consider the following scenario, where a Hazelcast client acquires a fenced_lock, then hits a long GC pause. Since it will not be able to commit session heartbeats while paused, its CP session will be eventually closed. After this moment, another Hazelcast client can acquire this lock. If the first client wakes up again, it may not immediately notice that it has lost ownership of the lock. In this case, multiple clients think they hold the lock. If they attempt to perform an operation on a shared resource, they can break the system. To prevent such situations, you can choose to use an infinite session timeout, but this time probably you are going to deal with liveliness issues. For the scenario above, even if the first client actually crashes, requests sent by 2 clients can be re-ordered in the network and hit the external resource in reverse order.
There is a simple solution for this problem. Lock holders are ordered by a monotonic fencing token, which increments each time the lock is assigned to a new owner. This fencing token can be passed to external services or resources to ensure sequential execution of the side effects performed by the lock holders.
You can read more about the fencing token idea in Martin Kleppmann's "How to do distributed locking" blog post and Google's Chubby paper.
As an alternative approach, you can use the try_lock() method of fenced_lock. It tries to acquire the lock in optimistic manner and immediately returns with either a valid fencing token or undefined. It also accepts an optional timeout argument which specifies the timeout (in milliseconds resolution) to acquire the lock before giving up.
// Try to acquire the lock
auto success = lock->try_lock().get();
// Check for valid fencing token
if (success) {
try {
// Your guarded code goes here
} catch (...) {
// Make sure to release the lock
lock->unlock().get();
}
}Hazelcast counting_semaphore is the distributed implementation of a linearizable and distributed semaphore. It offers multiple operations for acquiring the permits. This data structure is a part of CP Subsystem.
counting_semaphore is a cluster-wide counting semaphore. Conceptually, it maintains a set of permits. Each acquire() waits if necessary until a permit is available, and then takes it. Dually, each release() adds a permit, potentially releasing a waiting acquirer. However, no actual permit objects are used; the semaphore just keeps a count of the number available and acts accordingly.
A basic counting_semaphore usage example is shown below.
// Get counting_semaphore named 'my-semaphore'
auto semaphore = hz.get_cp_subsystem().get_semaphore("my-semaphore").get();
// Try to initialize the semaphore
// (does nothing if the semaphore is already initialized)
semaphore->init(3).get();
// Acquire 3 permits out of 3
semaphore->acquire(3).get();
// Release 2 permits
semaphore->release(2).get();
// Check available permits
auto available = semaphore->available_permits().get();
std::cout << "Available:" << available << "\n";
// Prints:
// Available: 1Beware of the increased risk of indefinite postponement when using the multiple-permit acquire. If permits are released one by one, a caller waiting for one permit will acquire it before a caller waiting for multiple permits regardless of the call order. Correct usage of a semaphore is established by programming convention in the application.
As an alternative, potentially safer approach to the multiple-permit acquire, you can use the try_acquire() method of counting_semaphore. It tries to acquire the permits in optimistic manner and immediately returns with a boolean operation result. It also accepts an optional timeout argument which specifies the timeout (in milliseconds resolution) to acquire the permits before giving up.
// Try to acquire 1 permit
auto success = semaphore->try_acquire(1).get();
// Check for valid fencing token
if (success) {
try {
// Your guarded code goes here
} catch (...) {
// Make sure to release the permits
semaphore->release(1).get();
}
}counting_semaphore data structure has two variations:
- The default implementation is session-aware. In this one, when a caller makes its very first
acquire()call, it starts a new CP session with the underlying CP group. Then, liveliness of the caller is tracked via this CP session. When the caller fails, permits acquired by this caller are automatically and safely released. However, the session-aware version comes with a limitation, that is, a Hazelcast client cannot release permits before acquiring them first. In other words, a client can release only the permits it has acquired earlier. - The second implementation is sessionless. This one does not perform auto-cleanup of acquired permits on failures. Acquired permits are not bound to callers and permits can be released without acquiring first. However, you need to handle failed permit owners on your own. If a Hazelcast server or client fails while holding some permits, they will not be automatically released. You can use the sessionless CP counting_semaphore implementation by enabling JDK compatibility
jdk-compatibleserver-side setting. Refer to the CP Subsystem Configuration documentation for more details.
Hazelcast latch is the distributed implementation of a linearizable and distributed countdown latch. This data structure is a cluster-wide synchronization aid that allows one or more callers to wait until a set of operations being performed in other callers completes. This data structure is a part of CP Subsystem.
A basic latch usage example is shown below.
// Get a latch called 'my-latch'
auto latch = hz.get_cp_subsystem().get_latch("my-latch'").get();
// Try to initialize the latch
// (does nothing if the count is not zero)
auto initialized = latch->try_set_count(1).get();
std::cout << "Initialized:" << initialized << "\n";
// Check count
auto count = latch->get_count().get();
std::cout << "Count:" << count << "\n";
// Prints:
// Count: 1
// Bring the count down to zero after 10ms
auto f = std::async([=] () {
std::this_thread::sleep_for(std::chrono::milliseconds(10));
latch->count_down().get();
});
// Wait up to 1 second for the count to become zero up
auto status = latch->wait_for(std::chrono::seconds(1)).get();
if (status == std::cv_status::no_timeout) {
std::cout << "latch is count down\n";
}NOTE: latch count can be reset with
try_set_count()after a count_down has finished, but not during an active count.
Hazelcast atomic_reference is the distributed implementation of a linearizable object reference. It provides a set of atomic operations allowing to modify the value behind the reference. This data structure is a part of CP Subsystem.
A basic atomic_reference usage example is shown below.
// Get an atomic_reference named 'my-ref'
auto ref = hz.get_cp_subsystem().get_atomic_reference("my-ref'").get();
// Set the value atomically
ref->set(42).get();
// Read the value
auto value = ref->get<int>().get();
std::cout << "Value:" << value << "\n";
// Prints:
// Value: 42
// Try to replace the value with 'value'
// with a compare-and-set atomic operation
auto result = ref->compare_and_set(42, "value").get();
std::cout << "CAS result:" << result << "\n";
// Prints:
// CAS result: 1The following are some considerations you need to know when you use atomic_reference:
- atomic_reference works based on the byte-content and not on the object-reference. If you use the
compare_and_set()method, do not change to the original value because its serialized content will then be different. - All methods returning an object return a private copy. You can modify the private copy, but the rest of the world is shielded from your changes. If you want these changes to be visible to the rest of the world, you need to write the change back to the atomic_reference; but be careful about introducing a data-race.
- The 'in-memory format' of an atomic_reference is
binary. The receiving side does not need to have the class definition available unless it needs to be deserialized on the other side., e.g., because a method likealter()is executed. This deserialization is done for every call that needs to have the object instead of the binary content, so be careful with expensive object graphs that need to be deserialized. - If you have an object with many fields or an object graph, and you only need to calculate some information or need a subset of fields, you can use the
apply()method. With theapply()method, the whole object does not need to be sent over the line; only the information that is relevant is sent.
atomic_reference does not offer exactly-once / effectively-once execution semantics. It goes with at-least-once execution semantics by default and can cause an API call to be committed multiple times in case of CP member failures. It can be tuned to offer at-most-once execution semantics. Please see the fail-on-indeterminate-operation-state server-side setting.
Hazelcast C++ client provides transactional operations like beginning transactions, committing transactions and retrieving transactional data structures like the transactional_map, transactional_set, transactional_list, transactional_queue and transactional_multi_map.
You can create a transaction_context object using the C++ client to begin, commit and rollback a transaction. You can obtain transaction-aware instances of queues, maps, sets, lists and multimaps via the transaction_context object, work with them and commit or rollback in one shot. For details, see the Transactions section in the Hazelcast Reference Manual.
// Create a Transaction object and begin the transaction
auto context = client->new_transaction_context();
context.begin_transaction().get();
// Get transactional distributed data structures
auto txnMap = context.get_map("transactional-map").get();
auto txnmulti_map = context.get_multi_map("transactional-multimap");
auto txnQueue = context.get_queue("transactional-queue");
auto txnSet = context.get_set("transactional-set");
try {
auto obj = txnQueue->poll<std::string>().get();
//process object
txnMap->put<std::string, std::string>( "1", "value1" ).get();
txnmulti_map->put<std::string, std::string>("2", "value2_1").get();
txnmulti_map->put<std::string, std::string>("2", "value2_2").get();
txnSet->add<std::string>( "value" ).get();
//do other things
// Commit the above changes done in the cluster.
context.commit_transaction().get();
} catch (...) {
context.rollback_transaction().get();
throw;
}In a transaction, operations will not be executed immediately. Their changes will be local to the transaction_context until committed. However, they will ensure the changes via locks.
For the above example, when map->put is executed, no data will be put in the map but the key will be locked against changes. While committing, operations will be executed, the value will be put to the map and the key will be unlocked.
The isolation level in Hazelcast Transactions is READ_COMMITTED on the level of a single partition. If you are in a transaction, you can read the data in your transaction and the data that is already committed. If you are not in a transaction, you can only read the committed data.
This chapter explains when various events are fired and describes how you can add event listeners on a Hazelcast C++ client. These events can be categorized as cluster and distributed data structure events.
You can add event listeners to a Hazelcast C++ client. You can configure the following listeners to listen to the events on the client side:
- Membership Listener: Notifies when a member joins to/leaves the cluster, or when an attribute is changed in a member.
- Lifecycle Listener: Notifies when the client is starting, started, shutting down and shutdown.
You can listen the following types of member events in the cluster.
member_added: A new member is added to the cluster.member_removed: An existing member leaves the cluster.
You can use cluster (hazelcast_client::hazelcast_client::get_cluster()) object to register for the membership listeners. There are two types of listeners: InitialMembershipListener and membership_listener. The difference is that InitialMembershipListener also gets notified when the client connects to the cluster and retrieves the whole membership list. You need to implement one of these two interfaces and register an instance of the listener to the cluster.
The following example demonstrates both initial and regular membership listener registrations.
membership_listener make_membership_listener() {
return membership_listener().on_joined([](const hazelcast::client::membership_event &membership_event) {
std::cout << "New member joined: "
<< membership_event.get_member().get_address() << std::endl;
}).on_left([](const hazelcast::client::membership_event &membership_event) {
std::cout << "Member left: "
<< membership_event.get_member().get_address() << std::endl;
});
}
membership_listener make_initial_membership_listener() {
return membership_listener().on_init([](const hazelcast::client::initial_membership_event &event){
auto members = event.get_members();
std::cout << "The following are the initial members in the cluster:" << std::endl;
for (const auto &member : members) {
std::cout << member.get_address() << std::endl;
}
}).on_joined([](const hazelcast::client::membership_event &membership_event) {
std::cout << "New member joined: " << membership_event.get_member().get_address() << std::endl;
}).on_left([](const hazelcast::client::membership_event &membership_event) {
std::cout << "Member left: " <<
membership_event.get_member().get_address() << std::endl;
});
}
int main() {
auto memberListener = make_membership_listener();
auto initialMemberListener = make_initial_membership_listener();
hazelcast::client::cluster *clusterPtr = nullptr;
boost::uuids::uuid listenerId, initialListenerId;
try {
auto hz = hazelcast::new_client().get();
hazelcast::client::cluster &cluster = hz.get_cluster().get();
clusterPtr = &cluster;
auto members = cluster.get_members();
std::cout << "The following are members in the cluster:" << std::endl;
for (const auto &member: members) {
std::cout << member.get_address() << std::endl;
}
listenerId = cluster.add_membership_listener(std::move(memberListener));
initialListenerId = cluster.add_membership_listener(std::move(initialMemberListener));
// sleep some time for the events to be delivered before exiting
std::this_thread::sleep_for(std::chrono::seconds(3));
cluster.remove_membership_listener(listenerId).get();
cluster.remove_membership_listener(initialListenerId).get();
} catch (hazelcast::client::exception::iexception &e) {
std::cerr << "failed !!! " << e.what() << std::endl;
if (nullptr != clusterPtr) {
clusterPtr->remove_membership_listener(listenerId).get();
clusterPtr->remove_membership_listener(initialListenerId).get();
}
exit(-1);
}
...The lifecycle_listener interface notifies for the following events:
starting: The client is starting.started: The client has started.shutting_down: The client is shutting down.shutdown: The client’s shutdown has completed.client_connected: The client is connected to the cluster.client_disconnected: The client is disconnected from the cluster.
The following is an example of the lifecycle_listener that is added to the client_config object and its output.
int main() {
hazelcast::client::client_config config;
// Add a lifecycle listener so that we can track when the client is connected/disconnected
config.add_listener(
hazelcast::client::lifecycle_listener().on_connected([](){
std::cout << "Client connected to the cluster" << std::endl;
}).on_disconnected([] () {
std::cout << "Client is disconnected from the cluster" << std::endl;
});
);
auto hz = hazelcast::new_client(std::move(config)).get();
hz.shutdown().get();
return 0;
}Output:
20/11/2022 12:26:43.340 INFO: [0x11a0acdc0] hz.client_1[dev] [5.x.x] [../hazelcast/src/hazelcast/client/spi.cpp:375] (Fri Nov 20 11:03:59 2022 +0300:f2326084c3) LifecycleService::LifecycleEvent Client (7add62a3-c6ec-4002-a9d8-79ed4639f8e9) is STARTING
20/11/2022 12:26:43.343 INFO: [0x11a0acdc0] hz.client_1[dev] [5.x.x] [../hazelcast/src/hazelcast/client/spi.cpp:379] (Fri Nov 20 11:03:59 2022 +0300:f2326084c3) LifecycleService::LifecycleEvent STARTING
20/11/2022 12:26:43.343 INFO: [0x11a0acdc0] hz.client_1[dev] [5.x.x] [../hazelcast/src/hazelcast/client/spi.cpp:387] LifecycleService::LifecycleEvent STARTED
20/11/2022 12:26:43.398 INFO: [0x11a0acdc0] hz.client_1[dev] [5.x.x] [../hazelcast/src/hazelcast/client/network.cpp:587] Trying to connect to Address[127.0.0.1:5701]
20/11/2022 12:26:43.409 INFO: [0x11a0acdc0] hz.client_1[dev] [5.x.x] [../hazelcast/src/hazelcast/client/spi.cpp:411] LifecycleService::LifecycleEvent CLIENT_CONNECTED
20/11/2022 12:26:43.410 INFO: [0x11a0acdc0] hz.client_1[dev] [5.x.x] [../hazelcast/src/hazelcast/client/network.cpp:637] Authenticated with server Address[:5701]:8324bc53-8190-400c-bcbf-e582834a0542, server version: 4.2, local address: Address[127.0.0.1:63383]
20/11/2022 12:26:43.412 INFO: [0x7000062b8000] hz.client_1[dev] [5.x.x] [../hazelcast/src/hazelcast/client/spi.cpp:881]
Members [1] {
Member[localhost]:5701 - 8324bc53-8190-400c-bcbf-e582834a0542
}
Client connected to the cluster
20/11/2022 12:26:48.700 INFO: [0x11a0acdc0] hz.client_1[dev] [5.x.x] [../hazelcast/src/hazelcast/client/spi.cpp:395] LifecycleService::LifecycleEvent SHUTTING_DOWN
20/11/2022 12:26:48.701 INFO: [0x11a0acdc0] hz.client_1[dev] [5.x.x] [../hazelcast/src/hazelcast/client/network.cpp:545] Removed connection to endpoint: Address[localhost:5701], connection: ClientConnection{alive=0, connectionId=1, remoteEndpoint=Address[localhost:5701], lastReadTime=2022-11-20 12:26:43.-286, closedTime=2022-11-20 12:26:48.000, connected server version=4.2}
20/11/2022 12:26:48.701 INFO: [0x11a0acdc0] hz.client_1[dev] [5.x.x] [../hazelcast/src/hazelcast/client/spi.cpp:419] LifecycleService::LifecycleEvent CLIENT_DISCONNECTED
Client is disconnected from the cluster
20/11/2022 12:26:48.703 INFO: [0x11a0acdc0] hz.client_1[dev] [5.x.x] [../hazelcast/src/hazelcast/client/spi.cpp:403] LifecycleService::LifecycleEvent SHUTDOWN
You can add event listeners to the distributed data structures.
You can listen to map-wide or entry-based events by registering an instance of the entry_listener class. You should provide the entry_listener instance with function objects for each type of event you want to listen to and then use imap::add_entry_listener to register it.
An entry-based event is fired after operations that affect a specific entry. For example, imap::put(), imap::remove() or imap::evict(). The corresponding function that you provided to the listener will be called with an entry_event when an entry-based event occurs.
See the following example.
int main() {
auto hz = hazelcast::new_client().get();
auto map = hz.get_map("EntryListenerExampleMap").get();
auto registrationId = map->add_entry_listener(
entry_listener().on_added([](hazelcast::client::EntryEvent &&event) {
std::cout << "Entry added:" << event.getKey().get<int>().value() << " --> " << event.getValue().get<std::string>().value() << std::endl;
}).on_removed([](hazelcast::client::EntryEvent &&event) {
std::cout << "Entry removed:" << event.getKey().get<int>().value()
<< " --> " << event.getValue().get<std::string>().value() << std::endl;
}), true).get();
map->put(1, "My new entry").get();
map->remove<int, std::string>(1).get();
/* ... */
return 0;
}A map-wide event is fired as a result of a map-wide operation (e.g.: imap::clear(), imap::evict_all()) which can also be listened to via an entry_listener. The functions you provided to the listener will be called with a map_event when a map-wide event occurs.
See the example below.
int main() {
auto hz = hazelcast::new_client().get();
auto map = hz.get_map("EntryListenerExampleMap").get();
auto registrationId = map->add_entry_listener(
entry_listener().on_map_cleared([](hazelcast::client::map_event &&event) {
std::cout << "Map cleared:" << event.getNumberOfEntriesAffected() << std::endl; // Map Cleared: 3
}), false).get();
map->put(1, "Mali").get();
map->put(2, "Ahmet").get();
map->put(3, "Furkan").get();
map->clear().get();
/* ... */
return 0;
}This section describes how Hazelcast's distributed executor service and entry processor features can be used in the C++ client.
Hazelcast C++ client allows you to asynchronously execute your tasks (logical units of work) in the cluster, such as database queries, complex calculations and image rendering.
With iexecutor_service, you can execute tasks asynchronously and perform other useful tasks. If your task execution takes longer than expected, you can cancel the task execution. Tasks should be Hazelcast serializable, since they will be distributed in the cluster.
You need to implement the actual task logic at the server side as a Java code. The task should implement Java's java.util.concurrent.Callable interface.
Note that, the distributed executor service (iexecutor_service) is intended to run processing where the data is hosted: on the server members.
For more information on the server side configuration, see the Executor Service section in the Hazelcast Reference Manual.
You implement a C++ class which is Hazelcast serializable. This is the task class to be run on the server side. The client side implementation does not need to have any logic, it is purely for initiating the server side task.
On the server side, when you implement the task as java.util.concurrent.Callable (a task that returns a value), implement one of the Hazelcast serialization methods for the new class. The serialization type needs to match with that of the client side task class.
An example C++ task class implementation is shown below.
struct MessagePrinter {
std::string message;
};
namespace hazelcast {
namespace client {
namespace serialization {
template<>
struct hz_serializer<MessagePrinter> : identified_data_serializer {
static int32_t get_factory_id() noexcept {
return 1;
}
static int32_t get_class_id() noexcept {
return 555;
}
static void write_data(const MessagePrinter &object, object_data_output &out) {
out.write(object.message);
}
static MessagePrinter read_data(object_data_input &in) {
return MessagePrinter{in.read<std::string>(); }
}
};
}
}
}An example of a Callable Java task which matches the above C++ class is shown below. MessagePrinter prints out the message sent from the C++ client at the cluster members.
// The Java server side example MessagePrinter implementation looks like the following (Please observe that Java class
// should implement the Callable and the IdentifiedDataSerializable interfaces):
//
public class MessagePrinter implements IdentifiedDataSerializable, Callable<String> {
private String message;
public MessagePrinter(String message) {
this.message = message;
}
@Override
public int getFactoryId() {
return 1;
}
@Override
public int getId() {
return 555;
}
@Override
public void writeData(object_data_output out)
throws io {
out.writeUTF(message);
}
@Override
public void readData(object_data_input in)
throws io {
message = in.readUTF();
}
@Override
public String call() throws Exception {
System.out.println(message);
return message;
}
}You need to compile and link the Java class on the server side (add it to the server's classpath), implement and register a DataSerializableFactory on the server side. In this example, we designed the task as IdentifiedDataSerializable and you can see that the factory and class ID for both C++ and Java classes are the same. You can of course use the other Hazelcast serialization methods as well.
To execute a callable task:
- Retrieve the executor from
hazelcast_client. - Submit a task which returns a
future. - After executing the task, you do not have to wait for the execution to complete, you can process other things.
- When ready, use the
futureobject to retrieve the result as shown in the code example below.
An example where MessagePrinter task is executed is shown below.
// Start the Hazelcast Client and connect to an already running Hazelcast Cluster on 127.0.0.1
auto hz = hazelcast::new_client().get();
// Get the Distributed Executor Service
std::shared_ptr<iexecutor_service> ex = hz.get_executor_service("my-distributed-executor").get();
// Submit the MessagePrinter Runnable to a random Hazelcast Cluster Member
auto result_future = ex->submit<MessagePrinter, std::string>(MessagePrinter{ "message to any node" });
// Wait for the result of the submitted task and print the result
auto result = result_future.get_future().get();
std::cout << "Server result: " << *result << std::endl;You can scale the Executor service both vertically (scale up) and horizontally (scale out). See the Scaling The Executor Service section in the Hazelcast Reference Manual for more details on its configuration.
The distributed executor service allows you to execute your code in the cluster. In this section, the code examples are based on the MessagePrinter class above. The code examples show how Hazelcast can execute your code:
printOnTheMember: On a specific cluster member you choose with theiexecutor_service::submit_to_membermethod.printOnTheMemberOwningTheKey: On the member owning the key you choose with theiexecutor_service::submit_to_key_ownermethod.printOnSomewhere: On the member Hazelcast picks with theiexecutor_service::submitmethod.printOnMembers: On all or a subset of the cluster members with theiexecutor_service::submit_to_membersmethod.
void printOnTheMember(const std::string &input, const Member &member) {
// Start the Hazelcast Client and connect to an already running Hazelcast Cluster on 127.0.0.1
auto hz = hazelcast::new_client().get();
// Get the Distributed Executor Service
std::shared_ptr<iexecutor_service> ex = hz.get_executor_service("my-distributed-executor").get();
// Get the first Hazelcast Cluster Member
member firstMember = hz.get_cluster().get_members()[0];
// Submit the MessagePrinter Runnable to the first Hazelcast Cluster Member
ex->execute_on_member<MessagePrinter>(MessagePrinter{ "message to very first member of the cluster" }, firstMember).get();void printOnTheMemberOwningTheKey(const std::string &input, const std::string &key) {
// Start the Hazelcast Client and connect to an already running Hazelcast Cluster on 127.0.0.1
auto hz = hazelcast::new_client().get();
// Get the Distributed Executor Service
std::shared_ptr<iexecutor_service> ex = hz.get_executor_service("my-distributed-executor").get();
// Submit the MessagePrinter Runnable to the Hazelcast Cluster Member owning the key called "key"
ex->execute_on_key_owner<MessagePrinter, std::string>(MessagePrinter{"message to the member that owns the key"}, "key").get();
}void printOnSomewhere(const std::string &input) {
// Start the Hazelcast Client and connect to an already running Hazelcast Cluster on 127.0.0.1
auto hz = hazelcast::new_client().get();
// Get the Distributed Executor Service
std::shared_ptr<iexecutor_service> ex = hz.get_executor_service("my-distributed-executor").get();
// Submit the MessagePrinter Runnable to a random Hazelcast Cluster Member
auto result_future = ex->submit<MessagePrinter, std::string>(MessagePrinter{ "message to any node" }).get();
}void printOnMembers(const std::string input, const std::vector<Member> &members) {
// Start the Hazelcast Client and connect to an already running Hazelcast Cluster on 127.0.0.1
auto hz = hazelcast::new_client().get();
// Get the Distributed Executor Service
std::shared_ptr<iexecutor_service> ex = hz.get_executor_service("my-distributed-executor").get();
ex->execute_on_members<MessagePrinter>(MessagePrinter{ "message to very first member of the cluster" }, members);
}NOTE: You can obtain the list of cluster members via the hazelcast_client::get_cluster()::get_bembers() call.
A task in the code that you execute in a cluster might take longer than expected. If you cannot stop/cancel that task, it will keep eating your resources.
To cancel a task, you can use the executor_promise<T>::cancel() API. This API lets you to code and design for cancellations, a highly ignored part of software development. Please keep in mind that if the execution is already started or finished, the API may not be able to cancel the task.
The following code waits for the task to be completed in 3 seconds. If it is not finished within this period, a timeout is thrown from the get() method, and we cancel the task with the cancel() method. The remote execution of the task is being cancelled.
auto service = client->get_executor_service(get_test_name()).get();
executor::tasks::CancellationAwareTask task{INT64_MAX};
auto promise = service->submit<executor::tasks::CancellationAwareTask, bool>(task);
auto future = promise.get_future();
if (future.wait_for(boost::chrono::seconds(3) == boost::future_status::timeout) {
std::cout << "Could not get response back in 3 seconds\n";
}
if (promise.cancel(true)) {
std::cout << "Cancelled the submitted task\n";
}
As previously mentioned, it is possible to indicate where in the Hazelcast cluster the task is executed. Usually you execute these in the cluster based on the location of a key or set of keys, or you allow Hazelcast to select a member.
If you want more control over where your code runs, use the member_selector interface. For example, you may want certain tasks to run only on certain members, or you may wish to implement some form of custom load balancing regime. The member_selector is an interface that you can implement and then provide to the iexecutor_service when you submit or execute.
The bool select(const Member &member) method is called for every available member in the cluster. Implement this method to decide if the member is going to be used or not.
In the simple example shown below, we select the cluster members based on the presence of an attribute.
class MyMemberSelector :
public member_selector {
public:
bool select(const member &member) const override {
const std::string *attribute = member.get_attribute("my.special.executor");
if (attribute == NULL) {
return false;
}
return *attribute == "true";
}
};You can now submit your task using this selector and the task will run on the member whose attribute key "my.special.executor" is set to "true". An example is shown below:
// Choose which member to submit the task to using a member selector
ex->submit<MessagePrinter, std::string>(MessagePrinter{"Message when using the member selector"},
MyMemberSelector());
Hazelcast supports entry processing. An entry processor is a function that executes your code on a map entry in an atomic way.
An entry processor is a good option if you perform bulk processing on an imap. Usually you perform a loop of keys -- executing imap->get(key), mutating the value and finally putting the entry back in the map using imap->put(key,value). If you perform this process from a client or from a member where the keys do not exist, you effectively perform two network hops for each update: the first to retrieve the data and the second to update the mutated value.
If you are doing the process described above, you should consider using entry processors. An entry processor executes a read and updates upon the member where the data resides. This eliminates the costly network hops described above.
NOTE: Entry processor is meant to process a single entry per call. Processing multiple entries and data structures in an entry processor is not supported as it may result in deadlocks on the server side.
Hazelcast sends the entry processor to each cluster member and these members apply it to the map entries. Therefore, if you add more members, your processing completes faster.
The imap interface provides the following functions for entry processing:
execute_on_keyprocesses an entry mapped by a key.execute_on_keysprocesses entries mapped by a list of keys.execute_on_entriescan process all entries in a map with a defined predicate. predicate is optional.
In the C++ client, an entry_processor should be Hazelcast serializable, because the server should be able to deserialize it to process.
The following is an example for entry_processor which is serialized by identified_data_serializer.
struct IdentifiedEntryProcessor {
std::string name;
};
namespace hazelcast {
namespace client {
namespace serialization {
template<>
struct hz_serializer<IdentifiedEntryProcessor> : identified_data_serializer {
static int32_t get_factory_id() noexcept {
return 66;
}
static int32_t get_class_id() noexcept {
return 1;
}
static void
write_data(const IdentifiedEntryProcessor &object, object_data_output &out) {
out.write(object.name);
}
static IdentifiedEntryProcessor read_data(object_data_input &in) {
// no-need to implement here, since we do not expect to receive this object but we only send it to server
assert(0);
return IdentifiedEntryProcessor{};
}
};
}
}
}Now, you need to make sure that the Hazelcast member recognizes the entry processor. For this, you need to implement the Java equivalent of your entry processor and its factory, and create your own compiled class or JAR files. For adding your own compiled class or JAR files to the server's CLASSPATH, see the Adding User Library to CLASSPATH section.
The following is the Java equivalent of the entry processor in C++ client given above:
public class IdentifiedEntryProcessor implements EntryProcessor<String, String, String>, IdentifiedDataSerializable {
static final int CLASS_ID = 1;
private String value;
public IdentifiedEntryProcessor() {
}
@Override
public int getFactoryId() {
return IdentifiedFactory.FACTORY_ID;
}
@Override
public int getClassId() {
return CLASS_ID;
}
@Override
public void writeData(ObjectDataOutput out) throws IOException {
out.writeUTF(value);
}
@Override
public void readData(ObjectDataInput in) throws IOException {
value = in.readUTF();
}
@Override
public String process(Map.Entry<String, String> entry) {
entry.setValue(value);
return value;
}
}You can implement the above processor’s factory as follows:
public class IdentifiedFactory implements DataSerializableFactory {
public static final int FACTORY_ID = 66;
@Override
public IdentifiedDataSerializable create(int typeId) {
if (typeId == IdentifiedEntryProcessor.CLASS_ID) {
return new IdentifiedEntryProcessor();
}
...
return null;
} Now you need to configure the hazelcast.xml to add your factory as shown below.
<hazelcast>
<serialization>
<data-serializable-factories>
<data-serializable-factory factory-id="66">
com.hazelcast.client.test.IdentifiedFactory
</data-serializable-factory>
</data-serializable-factories>
</serialization>
</hazelcast>The code that runs on the entries is implemented in Java on the server side. The client side entry processor is used to specify which entry processor should be called. For more details about the Java implementation of the entry processor, see the Entry Processor section in the Hazelcast Reference Manual.
After the above implementations and configuration are done and you start the server where your library is added to its CLASSPATH, you can use the entry processor in the imap functions. See the following example.
auto map = hz.get_map("my-distributed-map").get();
map->execute_on_key<std::string, IdentifiedEntryProcessor>("key", IdentifiedEntryProcessor("my new name"));
std::cout << "Value for 'key' is : '" << *map->get<std::string, std::string>("key").get() << "'" << std::endl; // value for 'key' is 'my new name'Hazelcast partitions your data and spreads it across cluster of members. You can iterate over the map entries and look for certain entries (specified by predicates) you are interested in. However, this is not very efficient because you will have to bring the entire entry set and iterate locally. Instead, Hazelcast allows you to run distributed queries on your distributed map.
- The requested predicate is sent to each member in the cluster.
- Each member looks at its own local entries and filters them according to the predicate. At this stage, key-value pairs of the entries are deserialized and then passed to the predicate.
- The predicate requester merges all the results coming from each member into a single set.
Distributed query is highly scalable. If you add new members to the cluster, the partition count for each member is reduced and thus the time spent by each member on iterating its entries is reduced. In addition, the pool of partition threads evaluates the entries concurrently in each member, and the network traffic is also reduced since only filtered data is sent to the requester.
If queried item is Portable, it can be queried for the fields without deserializing the data at the server side and hence no server side implementation of the queried object class will be needed.
Built-in Predicates For Query
There are many built-in predicate implementations for your query requirements. Some of them are explained below.
true_predicate: This predicate returns true and hence includes all the entries on the response.false_predicate: This predicate returns false and hence filters out all the entries in the response.equal_predicate: Checks if the result of an expression is equal to a given value.not_equal_predicate: Checks if the result of an expression is not equal to a given value.instance_of_predicate: Checks if the result of an expression has a certain type.like_predicate: Checks if the result of an expression matches some string pattern.%(percentage sign) is the placeholder for many characters,_(underscore) is placeholder for only one character.ilike_predicate: Same as like_predicate. Checks if the result of an expression matches some string pattern.%(percentage sign) is the placeholder for many characters,_(underscore) is placeholder for only one character.greater_less_predicate: Checks if the result of an expression is greater equal or less equal than a certain value.between_predicate: Checks if the result of an expression is between two values (start and end values are inclusive).in_predicate: Checks if the result of an expression is an element of a certain list.not_predicate: Negates a provided predicate result.regex_predicate: Checks if the result of an expression matches some regular expression.sql_predicate: Query using SQL syntax.paging_predicate: Returns matching entries page by page.
Hazelcast offers the following ways for distributed query purposes:
- Combining predicates with
and_predicate,or_predicateandnot_predicate - Distributed SQL Query
Assume that you have a person map containing the values of person objects, as coded below.
struct person {
std::string name;
bool male;
int32_t age;
};
std::ostream &operator<<(std::ostream &os, const person &obj) {
os << "name: " << obj.name << " male: " << obj.male << " age: " << obj.age;
return os;
}
namespace hazelcast {
namespace client {
namespace serialization {
template<>
struct hz_serializer<person> : portable_serializer {
static int32_t get_factory_id() noexcept {
return 1;
}
static int32_t get_class_id() noexcept {
return 3;
}
static void write_data(const person &object, portable_writer &out) {
out.write(object.name);
out.write(object.male);
out.write(object.age);
}
static person read_data(portable_reader &in) {
return person{in.read<std::string>(), in.read<bool>(), in.read<int32_t>()};
}
};
}
}
}Note that person is being serialized with portable_serializer`. As portable types are not deserialized on the server side for querying, you don't need to implement its Java equivalent on the server side for querying.
For the non-portable types, you need to implement its Java equivalent and its serializable factory on the server side for server to reconstitute the objects from binary formats.
In this case before starting the server, you need to compile the person and related factory classes with server's CLASSPATH and add them to the user-lib directory in the extracted hazelcast-<version>.zip (or tar). See the Adding User Library to CLASSPATH section.
NOTE: Querying with
portable_serializeris faster as compared toidentified_data_serializer.
You can combine predicates by using the and_predicate, or_predicate and not_predicate operators, as shown in the below example.
// and_predicate
// 5 <= key <= 10 AND Values in {4, 10, 19} = values {4, 10}
std::vector<int> inVals{4, 10, 19};
values = intMap->values<int>(query::and_predicate(client,
query::between_predicate(client,
query::query_constants::KEY_ATTRIBUTE_NAME,
5, 10),
query::in_predicate(client,
query::query_constants::THIS_ATTRIBUTE_NAME,
inVals))).get();
// 5 <= key <= 10 OR Values in {4, 10, 19} = values {4, 10, 12, 14, 16, 18, 20}
values = intMap->values<int>(query::or_predicate(client,
query::between_predicate(client,
query::query_constants::KEY_ATTRIBUTE_NAME,
5, 10),
query::in_predicate(client,
query::query_constants::THIS_ATTRIBUTE_NAME,
inVals))).get();
// not_predicate
// !(5 <= key <= 10)
values = intMap->values<int>(query::not_predicate(client, query::between_predicate(client,
query::query_constants::KEY_ATTRIBUTE_NAME,
5, 10))).get();See examples/distributed-map/query folder for more examples.
sql_predicate takes the regular SQL where clause. See the following example:
// sql_predicate
// __key BETWEEN 4 and 7 : {4, 5, 6, 7} -> {8, 10, 12, 14}
auto sql = (boost::format("%1% BETWEEN 4 and 7") % query::query_constants::KEY_ATTRIBUTE_NAME).str();
values = intMap->values<int>(query::sql_predicate(client, sql)).get();AND/OR: <expression> AND <expression> AND <expression>…
active AND age > 30active = false OR age = 45 OR name = 'Joe'active AND ( age > 20 OR salary < 60000 )
Equality: =, !=, <, ⇐, >, >=
<expression> = valueage <= 30name = 'Joe'salary != 50000
BETWEEN: <attribute> [NOT] BETWEEN <value1> AND <value2>
age BETWEEN 20 AND 33 ( same as age >= 20 AND age ⇐ 33 )age NOT BETWEEN 30 AND 40 ( same as age < 30 OR age > 40 )
IN: <attribute> [NOT] IN (val1, val2,…)
age IN ( 20, 30, 40 )age NOT IN ( 60, 70 )active AND ( salary >= 50000 OR ( age NOT BETWEEN 20 AND 30 ) )age IN ( 20, 30, 40 ) AND salary BETWEEN ( 50000, 80000 )
LIKE: <attribute> [NOT] LIKE 'expression'
The % (percentage sign) is the placeholder for multiple characters, an _ (underscore) is the placeholder for only one character.
name LIKE 'Jo%'(true for 'Joe', 'Josh', 'Joseph' etc.)name LIKE 'Jo_'(true for 'Joe'; false for 'Josh')name NOT LIKE 'Jo_'(true for 'Josh'; false for 'Joe')name LIKE 'J_s%'(true for 'Josh', 'Joseph'; false 'John', 'Joe')
ILIKE: <attribute> [NOT] ILIKE 'expression'
ILIKE is similar to the LIKE predicate but in a case-insensitive manner.
name ILIKE 'Jo%'(true for 'Joe', 'joe', 'jOe','Josh','joSH', etc.)name ILIKE 'Jo_'(true for 'Joe' or 'jOE'; false for 'Josh')
REGEX: <attribute> [NOT] REGEX 'expression'
name REGEX 'abc-.*'(true for 'abc-123'; false for 'abx-123')
You can use the query::query_constants::KEY_ATTRIBUTE_NAME (__key) attribute to perform a predicated search for entry keys. See the following example:
auto map = hz.get_map("personMap").get();
map->put<std::string, int>("Mali", 28).get();
map->put<std::string, int>("Ahmet", 30).get();
map->put<std::string, int>("Furkan", 23).get();
query::sql_predicate sqlPredicate("__key like F%");
auto startingWithF = map->entry_set<std::string, int>(sqlPredicate).get();
std::cout << "First person:" << startingWithF[0].first << ", age:" << startingWithF[0].second << std::endl;In this example, the code creates a list with the values whose keys start with the letter "F”.
You can use query::query_constants::THIS_ATTRIBUTE_NAME (this) attribute to perform a predicated search for entry values. See the following example:
auto map = hz.get_map("personMap").get();
map->put<std::string, int>("Mali", 28).get();
map->put<std::string, int>("Ahmet", 30).get();
map->put<std::string, int>("Furkan", 23).get();
query::greater_less_predicate<int> greaterThan27Predicate(query::query_constants::THIS_ATTRIBUTE_NAME, 27, false, false);
std::vector<std::string> olderThan27 = map->keySet<std::string>(greaterThan27Predicate).get();
std::cout << "First person:" << olderThan27[0] << ", second person:" << olderThan27[1] << std::endl;In this example, the code creates a list with the values greater than or equal to "27".
You can query JSON strings stored inside your Hazelcast clusters. To query the JSON string,
you first need to create a hazelcast_json_value from the JSON string using the hazelcast_json_value(const std::string &)
constructor. You can use hazelcast_json_values both as keys and values in the distributed data structures. Then, it is
possible to query these objects using the Hazelcast query methods explained in this section.
std::string person1 = "{ \"name\": \"John\", \"age\": 35 }";
std::string person2 = "{ \"name\": \"Jane\", \"age\": 24 }";
std::string person3 = "{ \"name\": \"Trey\", \"age\": 17 }";
auto idPersonMap = hz.get_map("jsonValues").get();
idPersonMap->put<int, hazelcast_json_value>(1, hazelcast_json_value(person1)).get();
idPersonMap->put<int, hazelcast_json_value>(2, hazelcast_json_value(person2)).get();
idPersonMap->put<int, hazelcast_json_value>(3, hazelcast_json_value(person3)).get();
auto peopleUnder21 = idPersonMap.values(query::greater_less_predicate<int>("age", 21, false, true)).get();When running the queries, Hazelcast treats values extracted from the JSON documents as Java types so they
can be compared with the query attribute. JSON specification defines five primitive types to be used in the JSON
documents: number,string, true, false and null. The string, true/false and null types are treated
as String, boolean and null, respectively. We treat the extracted number values as longs if they
can be represented by a long. Otherwise, numbers are treated as doubles.
It is possible to query nested attributes and arrays in the JSON documents. The query syntax is the same
as querying other Hazelcast objects using the predicates.
/**
* Sample JSON object
*
* {
* "departmentId": 1,
* "room": "alpha",
* "people": [
* {
* "name": "Peter",
* "age": 26,
* "salary": 50000
* },
* {
* "name": "Jonah",
* "age": 50,
* "salary": 140000
* }
* ]
* }
*
*
* The following query finds all the departments that have a person named "Peter" working in them.
*/
auto departmentWithPeter = departments->values(query::equal_predicate<std::string>("people[any].name", "Peter")).get();hazelcast_json_value is a lightweight wrapper around your JSON strings. It is used merely as a way to indicate
that the contained string should be treated as a valid JSON value. Hazelcast does not check the validity of JSON
strings put into to the maps. Putting an invalid JSON string into a map is permissible. However, in that case
whether such an entry is going to be returned or not from a query is not defined.
The C++ client provides paging for defined predicates. With its paging_predicate object, you can get a list of keys, values, or entries page by page by filtering them with predicates and giving the size of the pages. Also, you can sort the entries by specifying comparators.
auto map = hz.get_map("personMap").get();
// paging_predicate with inner predicate (value < 10)
auto predicate = intMap->new_paging_predicate<int, int>(5,
query::greater_less_predicate(client, query::query_constants::THIS_ATTRIBUTE_NAME, 10, false, false));
// Set page to retrieve third page
pagingPredicate.set_page(3);
auto values = map->values(pagingPredicate).get();
//...
// Set up next page
pagingPredicate.next_page();
// Retrieve next page
values = map->values(pagingPredicate).get();
//...If you want to sort the result before paging, you need to specify a comparator object that implements the query::entry_comparator interface. Also, this comparator object should be Hazelcast serializable. After implementing After implementing this object in C++, you need to implement the Java equivalent of it and its factory. The Java equivalent of the comparator should implement java.util.Comparator. Note that the compare function of Comparator on the Java side is the equivalent of the sort function of Comparator on the C++ side. When you implement the Comparator and its factory, you can add them to the CLASSPATH of the server side. See the Adding User Library to CLASSPATH section.
Also, you can access a specific page more easily with the help of the set_page function. This way, if you make a query for the 100th page, for example, it will get all 100 pages at once instead of reaching the 100th page one by one using the next_page function.
Partition Aware ensures that the related entries exist on the same member. If the related data is on the same member, operations can be executed without the cost of extra network calls and extra wire data, and this improves the performance. This feature is provided by using the same partition keys for related data.
Hazelcast has a standard way of finding out which member owns/manages each key object. The following operations are routed to the same member, since all of them are operating based on the same key 'key1'.
hazelcast::client::hazelcast_client hazelcastInstance;
auto mapA = hazelcastInstance.get_map("mapA");
auto mapB = hazelcastInstance.get_map("mapB");
auto mapC = hazelcastInstance.get_map("mapC");
// since map names are different, operation will be manipulating
// different entries, but the operation will take place on the
// same member since the keys ("key1") are the same
mapA->put("key1", value);
mapB.get("key1");
mapC.remove("key1");When the keys are the same, entries are stored on the same member. However, we sometimes want to have the related entries stored on the same member, such as a customer and his/her order entries. We would have a customers map with customerId as the key and an orders map with orderId as the key. Since customerId and orderId are different keys, a customer and his/her orders may fall into different members in your cluster. So how can we have them stored on the same member? We create an affinity between the customer and orders. If we make them part of the same partition then these entries will be co-located. We achieve this by making orderKey s partition_aware.
struct OrderKey : public hazelcast::client::partition_aware<std::string> {
OrderKey(const std::string &order_id_string) : order_id_string(order_id_string) {}
const std::string *get_partition_key() const override {
return &customer_id_string;
}
std::string order_id_string;
static const std::string customer_id_string;
};
const std::string OrderKey::customer_id_string = "My customer id 99";
namespace hazelcast {
namespace client {
namespace serialization {
template<>
struct hz_serializer<OrderKey> : identified_data_serializer {
static int32_t get_factory_id() noexcept {
return 1;
}
static int32_t get_class_id() noexcept {
return 10;
}
static void write_data(const OrderKey &object, object_data_output &out) {
out.write(object.order_id_string);
}
static OrderKey read_data(object_data_input &in) {
return OrderKey{in.read<std::string>()};
}
};
}
}
}Notice that OrderKey implements partition_aware interface and that get_partition_key() returns the OrderKey::customer_id_string. This will make sure that the Customer entry and its Orders will be stored on the same member.
hazelcast::client::hazelcast_client hazelcastInstance;
auto mapCustomers = hazelcastInstance.get_map("customers");
auto mapOrders = hazelcastInstance.get_map("orders");
// create the customer entry with customer id = "My customer id 99"
mapCustomers->put(OrderKey::customer_id_string, customer).get();
// now create the orders for this customer
mapOrders->put<OrderKey, Order>(OrderKey{"1"}, order1).get();
mapOrders->put<OrderKey, Order>(OrderKey{"2"}, order2).get();
mapOrders->put<OrderKey, Order>(OrderKey{"3"}, order3).get();For more details, see the Data Affinity section in the Hazelcast Reference Manual.
Map entries in Hazelcast are partitioned across the cluster members. Hazelcast clients do not have local data at all. Suppose you read the key k a number of times from a Hazelcast client or k is owned by another member in your cluster. Then each map->get(k) will be a remote operation, which creates a lot of network trips. If you have a data structure that is mostly read, then you should consider creating a local Near Cache, so that reads are sped up and less network traffic is created.
These benefits do not come for free, please consider the following trade-offs:
-
Clients with a Near Cache will have to hold the extra cached data, which increases memory consumption.
-
If invalidation is enabled and entries are updated frequently, then invalidations will be costly.
-
Near Cache breaks the strong consistency guarantees; you might be reading stale data.
Near Cache is highly recommended for maps that are mostly read.
In a client/server system you must enable the Near Cache separately on the client, without the need to configure it on the server. Please note that the Near Cache configuration is specific to the server or client itself: a data structure on a server may not have a Near Cache configured while the same data structure on a client may have it configured. They also can have different Near Cache configurations.
If you are using the Near Cache, you should take into account that your hits to the keys in the Near Cache are not reflected as hits to the original keys on the primary members. This has for example an impact on imap's maximum idle seconds or time-to-live seconds expiration. Therefore, even though there is a hit on a key in the Near Cache, your original key on the primary member may expire.
NOTE: Near Cache works only when you access data via the map->get(k) method. Data returned using a predicate is not stored in the Near Cache.
A Near Cache can have its own in-memory-format which is independent of the in-memory-format of the data structure.
Hazelcast Map can be configured to work with near cache enabled. You can enable the Near Cache on a Hazelcast Map by adding its configuration for that map-> An example configuration for myMap is shown below.
client_config config;
const char *mapName = "EvictionPolicyMap";
config::near_cache_config nearCacheConfig(mapName, config::OBJECT);
nearCacheConfig.set_invalidate_on_change(false);
nearCacheConfig.get_eviction_config().set_eviction_policy(config::LRU)
.set_maximum_size_policy(config::eviction_config::ENTRY_COUNT).set_size(100);
config.add_near_cache_config(nearCacheConfig);
hazelcast_client hz{ new_client(std::move(config)).get() };
Following are the descriptions of all configuration elements:
set_in_memory_format: Specifies in which format data will be stored in your Near Cache. Note that a map’s in-memory format can be different from that of its Near Cache. Available values are as follows:BINARY: Data will be stored in serialized binary format (default value).OBJECT: Data will be stored in deserialized form.set_invalidate_on_change: Specifies whether the cached entries are evicted when the entries are updated or removed in members. Its default value is true.set_time_to_live_seconds: Maximum number of seconds for each entry to stay in the Near Cache. Entries that are older than this period are automatically evicted from the Near Cache. Regardless of the eviction policy used,timeToLiveSecondsstill applies. Any integer between 0 andINT32_MAX. 0 means infinite. Its default value is 0.set_max_idle_seconds: Maximum number of seconds each entry can stay in the Near Cache as untouched (not read). Entries that are not read more than this period are removed from the Near Cache. Any integer between 0 andINT32_MAX. 0 means infinite. Its default value is 0.set_eviction_config: Eviction policy configuration. The following can be set on this config:set_eviction: Specifies the eviction behavior when you use High-Density Memory Store for your Near Cache. It has the following attributes:set_eviction_policy: Eviction policy configuration. Available values are as follows:LRU: Least Recently Used (default value).LFU: Least Frequently Used.NONE: No items will be evicted and the property max-size will be ignored. You still can combine it withtime-to-live-secondsandmax-idle-secondsto evict items from the Near Cache.RANDOM: A random item will be evicted.
set_max_size_policy: Maximum size policy for eviction of the Near Cache. Available values are as follows:ENTRY_COUNT: Maximum size based on the entry count in the Near Cache (default value).
set_size: Maximum size of the Near Cache used formax-size-policy. When this is reached the Near Cache is evicted based on the policy defined. Any integer between1andINT32_MAX. Default isINT32_MAX.
set_local_update_policy: Specifies the update policy of the local Near Cache. It is available on JCache clients. Available values are as follows:INVALIDATE: Removes the Near Cache entry on mutation. After the mutative call to the member completes but before the operation returns to the caller, the Near Cache entry is removed. Until the mutative operation completes, the readers still continue to read the old value. But as soon as the update completes the Near Cache entry is removed. Any threads reading the key after this point will have a Near Cache miss and call through to the member, obtaining the new entry. This setting provides read-your-writes consistency. This is the default setting.CACHE_ON_UPDATE: Updates the Near Cache entry on mutation. After the mutative call to the member completes but before the put returns to the caller, the Near Cache entry is updated. So a remove will remove it and one of the put methods will update it to the new value. Until the update/remove operation completes, the entry's old value can still be read from the Near Cache. But before the call completes the Near Cache entry is updated. Any threads reading the key after this point will read the new entry. If the mutative operation was a remove, the key will no longer exist in the cache, both the Near Cache and the original copy in the member. The member will initiate an invalidate event to any other Near Caches, however the caller Near Cache is not invalidated as it already has the new value. This setting also provides read-your-writes consistency.
The following is an example configuration for a Near Cache defined in the mostlyReadMap map-> According to this configuration, the entries are stored as OBJECT's in this Near Cache and eviction starts when the count of entries reaches 5000; entries are evicted based on the LRU (Least Recently Used) policy. In addition, when an entry is updated or removed on the member side, it is eventually evicted on the client side.
config::near_cache_config nearCacheConfig(mapName, config::OBJECT);
nearCacheConfig.set_invalidate_on_change(false);
nearCacheConfig.get_eviction_config().set_eviction_policy(config::LRU)
.set_maximum_size_policy(config::eviction_config::ENTRY_COUNT).set_size(100);In the scope of Near Cache, eviction means evicting (clearing) the entries selected according to the given eviction_policy when the specified size has been reached.
The eviction_config::set_size defines the entry count when the Near Cache is full and determines whether the eviction should be triggered.
Once the eviction is triggered the configured eviction_policy determines which, if any, entries must be evicted.
Expiration means the eviction of expired records. A record is expired:
- if it is not touched (accessed/read) for
max_idle_seconds time_to_live_secondspassed since it is put to Near Cache
The actual expiration is performed when a record is accessed: it is checked if the record is expired or not. If it is expired, it is evicted and the value shall be looked up from the cluster.
Invalidation is the process of removing an entry from the Near Cache when its value is updated or it is removed from the original map (to prevent stale reads). See the Near Cache Invalidation section in the Hazelcast Reference Manual.
With the pipelining, you can send multiple
requests in parallel using a single thread and therefore can increase throughput.
As an example, suppose that the round trip time for a request/response
is 1 millisecond. If synchronous requests are used, e.g., imap::get(), then the maximum throughput out of these requests from
a single thread is 1/001 = 1000 operations/second. One way to solve this problem is to introduce multithreading to make
the requests in parallel. For the same example, if we would use 2 threads, then the maximum throughput doubles from 1000
operations/second, to 2000 operations/second.
However, introducing threads for the sake of executing requests isn't always convenient and doesn't always lead to an optimal
performance; this is where the pipelining can be used. If you would use 2 asynchronous calls from a single thread,
then the maximum throughput is 2*(1/001) = 2000 operations/second. Therefore, to benefit from the pipelining, asynchronous calls need to
be made from a single thread. The pipelining is a convenience implementation to provide back pressure, i.e., controlling
the number of inflight operations, and it provides a convenient way to wait for all the results.
constexpr int depth = 10;
auto pipelining = pipelining<std::string>::create(depth);
for (int k = 0; k < 100; ++k) {
int key = rand() % keyDomain;
pipelining->add(map->get(key));
}
// wait for completion
auto results = pipelining->results();In the above example, we make 100 asynchronous map->get() calls, but the maximum number of inflight calls is 10.
By increasing the debt of the pipelining, throughput can be increased. The pipelining has its own back pressure, you do not need to enable the Client BackPressure section on the client or member to have this feature on the pipelining. However, if you have many pipelines, you may still need to enable the client/member back pressure because it is possible to overwhelm the system with requests in that situation. See the Client BackPressure section to learn how to enable it on the client or member.
You do not need a special configuration, it works out-of-the-box.
The pipelining can be used for any asynchronous call. You can use it for imap asynchronous get/put methods. It cannot be used as a transaction mechanism though. So you cannot do some calls and throw away the pipeline and expect that none of the requests are executed. The pipelining is just a performance optimization, not a mechanism for atomic behavior.
The pipelines are cheap and should frequently be replaced because they accumulate results. It is fine to have a few hundred or even a few thousand calls being processed with the pipelining. However, all the responses to all requests are stored in the pipeline as long as the pipeline is referenced. So if you want to process a huge number of requests, then every few hundred or few thousand calls wait for the pipelining results and just create a new instance.
Note that the pipelines are not thread-safe. They must be used by a single thread.
You can enable the client statistics before starting your clients. There are two properties related to client statistics:
-
hazelcast.client.statistics.enabled: If set totrue, it enables collecting the client statistics and sending them to the cluster. When it istrueyou can monitor the clients that are connected to your Hazelcast cluster, using Hazelcast Management Center. Its default value isfalse. -
hazelcast.client.statistics.period.seconds: Period in seconds the client statistics are collected and sent to the cluster. Its default value is3.
You can enable client statistics and set a non-default period in seconds as follows:
hazelcast::client::client_config config;
config.set_property(hazelcast::client::client_properties::STATISTICS_ENABLED, "true");
config.set_property(hazelcast::client::client_properties::STATISTICS_PERIOD_SECONDS, "4");After enabling the client statistics, you can monitor your clients using Hazelcast Management Center. Please refer to the Monitoring Clients section in the Hazelcast Management Center Reference Manual for more information on the client statistics.
Hazelcast C++ client emits log messages to provide information about important events and errors. The log levels in increasing order of severity are: FINEST, FINER, FINE, INFO, WARNING and SEVERE.
If no logging configuration is made by the user, the client prints log messages of level INFO or above to the standard output.
You can set the minimum level in which the log messages are printed using logger_config::level:
clientConfig.get_logger_config().level(hazelcast::logger::level::warning);hazelcast::logger::level::off and hazelcast::logger::level::all can be used to enable or disable all levels:
clientConfig.get_logger_config().level(hazelcast::logger::level::off); // disables logging completely
clientConfig.get_logger_config().level(hazelcast::logger::level::all); // enables all log levelsYou can set a callback function to be called on each log message via logger_config::handler:
clientConfig.get_logger_config().handler(my_log_handler);Setting a log handler will disable the default log handling behavior.
The handler takes instance name of the client object that emitted the message, cluster name that the client is connected, name of the source code and the line number where the log was produced, the severity level of the log message, and the log message itself. Here is the exact signature for a log handler function:
void my_log_handler(
const std::string &instance_name, // instance name of the client
const std::string &cluster_name, // name of the cluster to which the client is connected
const char *file_name, // name of the source file where the log was produced
int line, // line number where the log was produced
level lvl, // severity level of the message
const std::string &msg); // the messageAs the callback function is called from multiple threads, it must be thread-safe.
Refer to the examples directory and the API documentation for details.
Sometimes, you may need to use Hazelcast data structures with objects of different types. For example, you may want to put int, string, identified data serializable, portable serializable, etc. objects into the same Hazelcast imap data structure.
The typed_data class is a wrapper class for the serialized binary data. It presents the following user APIs:
/**
*
* @return The type of the underlying object for this binary.
*/
serialization::pimpl::object_type get_type() const;
/**
* Deserializes the underlying binary data and produces the object of type T.
*
* <b>CAUTION</b>: The type that you provide should be compatible with what object type is returned with
* the get_type API, otherwise you will either get an exception of incorrectly try deserialize the binary data.
*
* @tparam T The type to be used for deserialization
* @return The object instance of type T.
*/
template <typename T>
boost::optional<T> get() const;
typed_data does late deserialization of the data only when the get method is called.
This typed_data allows you to retrieve the data type of the underlying binary to be used when being deserialized. This class represents the type of a Hazelcast serializable object. The fields can take the following values:
- Primitive types:
factory_id=-1,class_id=-1,type_idis the type ID for that primitive as listed inserialization_constants - Array of primitives:
factory_id=-1,class_id=-1,type_idis the type ID for that array as listed inserialization_constants - identified_data_serializer serialized objects:
factory_id,class_idandtype_idare non-negative values. - Portable serialized objects:
factory_id,class_idandtype_idare non-negative values. - Custom serialized objects:
factory_id=-1,class_id=-1,type_idis the non-negative type ID.
To use SQL API, the Jet engine must be enabled on the members and the hazelcast-sql module must be in the classpath of the members.
If you are using the CLI, Docker image, or distributions to start Hazelcast members, then you don't need to do anything, as the above preconditions are already satisfied for such members.
However, if you are using Hazelcast members in the embedded mode, or receiving errors saying that The Jet engine is disabled or Cannot execute SQL query because "hazelcast-sql" module is not in the classpath. while executing queries, enable the Jet engine following one of the instructions pointed out in the error message, or add the hazelcast-sql module to your member's classpath as it is stated here.
All the sql related types and functionalities accomodate in hazelcast::client::sql namespace. It is possible to execute queries on imap, Kafka and Files by using this module.
sql_service is the main controller of this module. Queries can be executed via sql_service class. It also supports to specify query parameters. After executing any query boost::future<std::shared_ptr<sql_result>> is returned.
If it is a SELECT query then it provides a way to iterate over rows. Rows are stored in pages and essentially, hazelcast serves result of SELECT queries page by page so the interface reflects this approach.
sql_result provides a page_iterator which allows caller to fetch and iterate over pages. sql_result::page_iterator::next() returns boost::future<std::shared_ptr<sql_page>>. Every sql_page contains either 0 or more rows. It also tells that whether this page is the last or not via last() method.
Before you can access any object using SQL, a mapping has to be created. See the documentation for the CREATE MAPPING command.
using namespace hazelcast::client::sql;
auto hz = hazelcast::new_client().get();
// populate the map with some data
auto map = hz.get_map("integers").get();
for (int i = 0; i < 1000; ++i) {
map->put(i, i).get();
}
auto sql = hz.get_sql();
// Create mapping for the integers. This needs to be done only once per map.
auto result = sql.execute(R"(
CREATE OR REPLACE MAPPING integers
TYPE IMap
OPTIONS (
'keyFormat' = 'int',
'valueFormat' = 'int'
)
)").get();SELECT results are fetched page by page. A page can accomodate zero or more rows.
sql_service::execute executes the query and returns a boost::future<std::shared_ptr<sql_result>>. Via sql_result::iterator() method, sql_result::page_iterator is acquired and pages can be iterated.
using namespace hazelcast::client::sql;
auto hz = hazelcast::new_client().get();
auto result = hz.get_sql().execute("SELECT * FROM integers").get();
result->row_metadata(); // Tells about metadata
for (auto itr = result->iterator(); itr.has_next();)
{
auto page = itr.next().get();
for(const sql_page::sql_row& row : page->rows())
{
std::cout << row.get_object<int>(0)
<< ", "
<< row.get_object<int>(1)
<< '\n';
}
}Note-1: First call to sql_result::page_iterator::next() function will return ready future because first page is already loaded as a result of the sql_service::execute call.
auto itr = result->iterator();
itr.next().get(); // This will not block even `get()`
// is called because it will return
// a future which is in ready state.Note-2: After first page every sql_result::page_iterator::next() call fetches a new page future.
Before waiting this future it is not allowed to do another sql_result::page_iterator::next() call.
auto itr = result->iterator();
itr.next().get(); // It will return first page
auto page_2 = itr.next(); // Made request to second page
// page_2.get(); // I should have waited but didn't
auto page_3 = itr.next(); // This probably throws an `hazelcast::illegal_access` exception
// because previous page might not be fetched yet. To prevent this
// `page_2.get()` should have called to ensure that page_2 is fetched.Note-3: If fetched page is marked as last there should not be any further fetch requests.
auto itr = result->iterator();
auto page_1 = itr.next().get();
if (page_1->last())
itr.next(); // This will throw an `hazelcast::no_such_element` exception.Note-4: Zero rows do not mean that it is the last page.
SELECT * FROM TABLE(generate_stream(1)) query mostly returns first page as empty because it is a stream.
Stream is an endless flow of data from server to the client.
using namespace hazelcast::client::sql;
auto hz = hazelcast::new_client().get();
auto result = hz.get_sql().execute("SELECT * FROM TABLE(generate_stream(1))").get();
auto itr = result->iterator();
auto page = itr.next().get();
std::cout << page.row_count() << std::endl; // This may print zero if no data is yet generated at the server side.Note-5: boost::future<std::shared_ptr<sql_result>> is returned from sql_service::execute and boost::future<std::shared_ptr<sql_page>> is returned from sql_result::page_iterator::next().
using namespace hazelcast::client::sql;
auto hz = hazelcast::new_client().get();
auto result_f = hz.get_sql().execute("SELECT * FROM TABLE(generate_stream(1))");
try
{
result_f.get();
}
catch (const hazelcast_sql_exception&)
{
// Do error handling
}using namespace hazelcast::client::sql;
auto hz = hazelcast::new_client().get();
auto result = hz.get_sql().execute("SELECT * FROM TABLE(generate_stream(1))").get();
try
{
for (auto itr = result->iterator(); itr.has_next();)
{
auto page_f = itr.next();
auto page = page_f.get();
}
}
catch (const hazelcast_sql_exception&)
{
// Do error handling
}sql_statement is a class which holds query string, its parameters and options.
It can be passed to sql_service::execute method and its parameters and options will be taken into consideration.
using namespace hazelcast::client::sql;
auto hz = hazelcast::new_client().get();
// Constructs sql_statement with default options which are :
// cursor_buffer_size: 4096
// timeout: std::chrono::millisecond{ -1 } which means no timeout
// expected_result_type: any
sql_statement statement
{
hz ,
R"(
SELECT * FROM some_table WHERE this > ?
)"
};
// Methods which specifies options are able to be called in chain, returns self references
statement.add_parameter(15) // '?' part of the query will be filled with this parameter.
.cursor_buffer_size(96) // Set '96' to 'cursor_buffer_size' option
.timeout(std::chrono::milliseconds {500}) // Set '500' milliseconds timeout, if the time is exceeded query will be cancelled.
.expected_result_type(sql_expected_result_type::rows); // Expect a table which contains rows
// To fetch use non-parameterized overloads
std::cout << "cursor_buffer_size : " << statement.cursor_buffer_size() << std::endl
<< "timeout : " << statement.timeout().count() << std::endl
<< "expected_result_type : " << int(statement.expected_result_type()) << std::endl;
hz.get_sql().execute(statement); // OK, execute this statementRow metadatas can be acquired with sql_result::row_metadata() and sql_page::sql_row::row_metadata() method.
using namespace hazelcast::client::sql;
auto hz = hazelcast::new_client().get();
auto result = hz.get_sql().execute("SELECT name, surname, age FROM employees").get();
auto metadata = result->row_metadata();
// Print column count
std::cout << "There are " << metadata.column_count() << " columns" << std::endl;
// Print columns
for (const sql_column_metadata& col : metadata.columns())
{
std::cout << "name : " << col.name
<< " type : " << int(col.type)
<< " nullable : " << col.nullable
<< std::endl;
}
// Get column by index
auto age_col_metadata = metadata.column(2);
std::cout << "name : " << age_col_metadata.name
<< " type : " << int(age_col_metadata.type)
<< " nullable : " << age_col_metadata.nullable
<< std::endl;Note : There is no metadata for non-SELECT queries.
auto result = sql.execute("DELETE FROM employees WHERE age < 18").get();
result->row_metadata(); // Throws an `exception::illegal_state`
// This is not a SELECT queryboost::optional<T> sql_page::sql_row::get_object<T> is used for reading column value. T specifies the return type and it should correspond with the column type. Otherwise it will throw boost::bad_any_cast. There are two overloads one can be used to read value by column idx and other by column name.
for (auto itr = result->iterator(); itr.has_next();)
{
auto page = itr.next().get();
for(const sql_page::sql_row& row : page->rows())
{
std::cout << row.get_object<int>(0) // Read first column value as int
<< ", "
<< row.get_object<int>("age") // Read 'age' column value
<< ", "
<< row.get_object<std::string>("surname") // Read 'surname' column value
<< std::endl;
}
}There are some exceptions which expresses an error occurred while performing an operation. So those exceptions should be taken into consideration for production ready code.
Most of the exceptions related with SQL API wrapped with sql::hazelcast_sql_exception
so it can be seen as general exception which expresses the errors in SQL API.
It contains several useful methods which represents the error better.
Those methods are stated at below :
sql::hazelcast_sql_exception::code()returns an internal error code. It might be useful to express an error more detailed.sql::hazelcast_sql_exception::suggestion()returns an suggestion message to fix the problem. It can be null.sql::hazelcast_sql_exception::originating_member_id()returns anboost:uuids::uuidwhich is ID of the member that caused or initiated an error condition.
Exceptions which can be thrown by SQL API are stated at below:
sql_service::executecan throwsql::hazelcast_sql_exceptionin case of an error.page_iterator::next()can throw three types of exceptions.sql::hazelcast_sql_exceptionin case of an error related with execution.no_such_elementis thrown if it is called after last page is fetched.illegal_accessis thrown if page fetch operation is already progress. To prevent this wait for theboost::future<sql_page>which belongs to previousnext()call.
sql_result::iterator()can throw two types of exceptions.illegal_stateis thrown if it is not anSELECTquery orsql_result::iterator()is requested more than once.sql_result::iterator()throwshazelcast_sql_exceptionif it sql_result is already closed.
sql_result::pbegin()callssql_result::iterator()method internally, so it throws same exceptions.sql_result::row_metadata()can throwillegal_stateexception if the result contains only update count.sql_page::sql_row::get_object(int)can throwindex_out_of_boundsexception if the index is out of range.sql_page::sql_row::get_object(std::string)can throwillegal_argumentexception if the column doesn't exist.sql_result::page_iterator_sync::operator++()can throwno_such_elementexception if the fetch operation is timed out.sql_result::page_iterator_sync::operator*()can throwno_such_elementexception if the iterator points to past-end element;sql_result::row_iterator_sync::operator++()can throwno_such_elementexception if the fetch operation is timed out.sql_result::row_iterator_sync::operator*()can throwno_such_elementexception if the iterator points to past-end element;
In addition, any method which returns boost::future<T> can throw an exception.
Unless otherwise is stated, sql::hazelcast_sql_exception is thrown.
There are several iterators in SQL Api to satisfy different use cases. They are listed below:
page_iterator(Async page iterator)page_iterator_sync(Sync page iterator)row_iterator_sync(Sync row iterator)
Reminder 1, only one type of iterator can be used. So it is suggested to choose appropriate one to fetch SELECT query results.
Reminder 2, it is not possible to iterate from beginning to end more than once.
Reminder 3, it is not possible to acquire iterators more than once, so consecutive calls to sql_result::iterator(), sql_result::pbegin(std::chrono::milliseconds timeout), sql_result::begin(std::chrono::milliseconds timeout) methods will throw illegal_state exception.
page_iterator is the basic and the most essential one. It supports fetching pages asynchronously by next() method which returns boost::future<sql_page>.
Therefore, users are able to fetch SELECT query results page by page. It is acquired by calling sql_result::iterator() method.
auto result = sql.execute("SELECT * FROM integers").get();
for (auto itr = result->iterator(); itr.has_next();) {
auto page = itr.next().get();
std::cout << "There are " << page->row_count() << " rows the page."
<< std::endl;
for (auto const& row : page->rows()) {
std::cout << "(" << row.get_object<int>(0) << ")" << std::endl;
}
}page_iterator_sync is the second iterator which wraps page_iterator and serves it as a sync iterator. It allows users to iterator over pages.
It is similar to native C++ iterators. So it can be used with algorithm header.
It supports operator*(), operator->(), operator++() operators. Post increment operator is marked as delete.
operator++()(pre-increment operator) fetches the next page in a blocking manner. timeout can be set. operator++() might throw exception::no_such_element exception if the fetch operation is timed out.
page_iterator_sync is copyable but it is there only for convenience. Copy is shallow copy so copied instances should not be used simultaneously.
This iterator is acquired by sql_result::pbegin(std::chrono::milliseconds timeout) and sql_result::pend(). timeout is defaulted to std::chrono::milliseconds{ -1 } which means wait forever.
auto result = client.get_sql().execute("SELECT * FROM integers").get();
std::vector<std::shared_ptr<sql_page>> pages;
copy(result->pbegin(), result->pend(), back_inserter(pages));row_iterator_sync is the third iterator which wraps page_iterator_sync and allows user to iterate over rows rather than pages.
It is similar to native C++ iterators. So it can be used with algorithm header.
It supports operator*(), operator->(), operator++() operators. Post increment operator is marked as delete.
timeout can be set, it is used when fetching new page. It throws exception::no_such_element exception if the fetch operation is timed out.
row_iterator_sync is copyable but it is there only for convenience. Copy is shallow copy so copied instances should not be used simultaneously.
This iterator is acquired by sql_result::begin() and sql_result::end(). Hence, this iterator can be used in range-for loop. timeout is defaulted to std::chrono::milliseconds{ -1 } which means wait forever.
range-for loop example:
auto result = client.get_sql().execute("SELECT * FROM integers").get();
for (const sql_page::sql_row& row : *result) {
std::cout << row.get_object<int>(0);
}algorithm header example:
auto result = client.get_sql().execute("SELECT * FROM integers").get();
std::vector<int> numbers;
transform(
begin(*result),
end(*result),
back_inserter(numbers),
[](const sql_page::sql_row& row) { return *row.get_object<int>(0); });Hazelcast C++ client is developed using C++. If you want to help with bug fixes, develop new features or tweak the implementation to your application's needs, you can follow the steps in this section.
In order to test Hazelcast C++ client locally, you will need the following:
- Java 8 or newer (for server)
- Maven
- cmake
- OpenSSL
- Boost
- thrift
You need to enable the examples in the build with the cmake flag -DBUILD_EXAMPLES=ON. When you build, the result will
produce the executable client_test on Linux/macOS and client_test.exe on Windows. You need to start the remote
controller first before running this executable by executing scripts/start-rc.sh on Linux/macOS
or scripts/start-rc.bat on Windows. Then you can run the test executable.
You can use the following channels for your questions and development/usage issues:
- This repository by opening an issue.
- Hazelcast Community Slack - C++ Client Channel
- Google Groups: https://groups.google.com/forum/#!forum/hazelcast
- Stack Overflow: https://stackoverflow.com/questions/tagged/hazelcast
Besides your development contributions as explained in the Development and Testing chapter above, you can always open a pull request on this repository for your other requests such as documentation changes.
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Visit www.hazelcast.com for more information.