kafka-connect-s3 : Ingest data from Kafka to Object Stores(s3)
Java Python
Latest commit de065fe Feb 8, 2017 @PraveenSeluka PraveenSeluka Adding GoogleHadoopFileSystem dependency. StreamX can now write to Go…
…ogle Cloud Storage(GCS). Sample hadoop config is available at config/hdfs-site.xml. You need to provide gs url for s3.url input. Will later refactor and add gs.url

README.md

StreamX: Kafka Connect for S3

Forked from the awesome kafka-connect-hdfs

StreamX is a kafka-connect based connector to copy data from Kafka to Object Stores like Amazon s3, Google Cloud Storage and Azure Blob Store. It focusses on reliable and scalable data copying. It can write the data out in different formats (like parquet, so that it can readily be used by analytical tools) and also in different partitioning requirements.

Features :

StreamX inherits rich set of features from kafka-connect-hdfs.

  • Support for writing data in Avro and Parquet formats.
  • Provides Hive Integration where the connector creates partitioned hive table and periodically does add partitions once it writes a new partition to s3
  • Pluggable partitioner :
    • default partitioner : Each Kafka partition will have its data copied under a partition specific directory
    • time based partitioner : Ability to write data on hourly basis
    • field based partitioner : Ability to use a field in the record as custom partitioner

In addition to these, we have made changes to the following to make it work efficiently with s3

  • Exactly-once guarantee using WAL
  • Support for storing Hive tables in Qubole's hive metastore (coming soon)

Getting Started:

Pre-req : StreamX is based on Kafka Connect framework, which is part of Kafka project. Kafka Connect is added in Kafka 0.9, hence StreamX can only be used with Kafka version >= 0.9. To download Kafka binaries, check here.

Clone : git clone https://github.com/qubole/streamx.git

Branch : For Kafka 0.9, use 2.x branch. For Kafka 0.10 and above, use master branch.

Build : mvn -DskipTests package

Once the build succeeds, StreamX packages all required jars under target/streamx-0.1.0-SNAPSHOT-development/share/java/streamx/* in StreamX repo. This directory needs to be in classpath.

Add Connector to Kafka Connect Classpath:

export CLASSPATH=$CLASSPATH:`pwd`/target/streamx-0.1.0-SNAPSHOT-development/share/java/streamx/*

Start Kafka Connect

In Kafka, change the following in config/connect-distibuted.properties or config/connect-standalone.properties depending on what mode you want to use.

bootstrap.servers=set Kafka end-point (ex: localhost:9092)
key.converter=com.qubole.streamx.ByteArrayConverter
value.converter=com.qubole.streamx.ByteArrayConverter

Use ByteArrayConverter to copy data from Kafka as-is without any changes. (copy JSON/CSV)

Run Kafka Connect in Standalone mode

Set s3.url and hadoop-conf in StreamX config/quickstart-s3.properties. StreamX packages hadoop-conf directory at config/hadoop-conf for ease-of-use. Set s3 access and secret keys in config/hadoop-conf/hdfs-site.xml.

In Kafka, run

bin/connect-standalone etc/kafka/connect-standalone.properties \
  /path/to/streamx/config/quickstart-s3.properties

You are done. Check s3 for ingested data!

Run Kafka Connect in distributed mode
bin/connect-distributed.sh config/connect-distributed.properties

We have started the Kafka Connect framework and the S3 Connector is added to classpath. Kafka Connect framework starts a REST server (rest.port property in connect-distributed.properties) listening for Connect Job requests. The copy job can be submitted by hitting the REST end-point using curl or any REST clients.

For example, to submit a copy job from Kafka to S3

curl -i -X POST \
   -H "Accept:application/json" \
   -H "Content-Type:application/json" \
   -d \
'{"name":"clickstream",
 "config":
{
"name":"clickstream",
"connector.class":"com.qubole.streamx.s3.S3SinkConnector",
"format.class":"com.qubole.streamx.SourceFormat",
"tasks.max":"1",
"topics":"adclicks",
"flush.size":"2",
"s3.url":"s3://streamx/demo",
"hadoop.conf.dir":"/Users/pseluka/src/streamx/hadoop-conf"
}}' \
 'http://localhost:8083/connectors'
  • Uses S3SinkConnector
  • Uses SourceFormat, which copies the data as-is (Note that this needs to be used with ByteArrayConverter)
  • tasks.max refers to number of tasks that copies the data
  • a new file is written after flush.size number of messages
  • S3 Configuration It uses the hadoop file system implementation (s3a/s3n) to write to s3. The connect job has a configuration called hadoop.conf.dir and this needs the directory where hdfs-site.xml and other hadoop configuration resides. StreamX packages the hadoop dependencies, so it need not have hadoop project/jars in its classpath. So, create a directory containing hadoop config files like core-site.xml, hdfs-site.xml and provide the location of this directory in hadoop.conf.dir while submitting copy job. (StreamX provides a default hadoop-conf directory under config/hadoop-conf. Set your s3 access key, secret key there and provide full path in hadoop.conf.dir)

You have submitted the job, check S3 for output files. For the above copy job, it will create s3://streamx/demo/topics/adclicks/partition=x/files.xyz

Note that, a single copy job could consume from multiple topics and writes to topic specific directory.

To delete a Connect job,

curl -i -X DELETE \
   -H "Accept:application/json" \
   -H "Content-Type:application/json" \
 'http://localhost:8083/connectors/clickstream'

To list all Connect jobs,

curl -i -X GET \
   -H "Accept:application/json" \
   -H "Content-Type:application/json" \
 'http://localhost:8083/connectors'

Restarting Connect jobs : All Connect jobs are stored in a Kafka Queue. So, restarting the Kafka Connect will restart all the connectors submitted to it.

Docker Streamx supports Docker, but only in distributed mode To build your image,

docker build -t qubole/streamx .

When you run your container, you can override all the properties in connect-distributed.properties file with env vars. env_vars will be of format CONNECT_BOOTSTRAP_SERVERS corresponding to bootstrap.servers. The convention is to prefix env vars with CONNECT. Example of how to run your container,

docker run -d -p 8083:8083 --env CONNECT_BOOTSTRAP_SERVERS=public_dns:9092 --env CONNECT_AWS_ACCESS_KEY=youracesskey --env CONNECT_AWS_SECRET_KEY=yoursecretkey qubole/streamx

You can also use Avro/Parquet format. Example:

docker run -d -p 8083:8083 --env CONNECT_BOOTSTRAP_SERVERS=public_dns:9092 --env CONNECT_AWS_ACCESS_KEY=youracesskey --env CONNECT_AWS_SECRET_KEY=yoursecretkey  --env CONNECT_KEY_CONVERTER=io.confluent.connect.avro.AvroConverter --env CONNECT_VALUE_CONVERTER=io.confluent.connect.avro.AvroConverter --env CONNECT_KEY_CONVERTER_SCHEMA_REGISTRY_URL=http://your.schema.registry.com:8081 --env CONNECT_VALUE_CONVERTER_SCHEMA_REGISTRY_URL=http://your.schema.registry.com:8081 qubole/streamx

Roadmap

  • Support other object stores like Google Cloud Storage and Azure Blob Store
  • Currently, data can be written in avro/parquet format. This project will add support for more formats
  • Deal with features related to s3, like small-file consolidation