Manta: Triton's object storage and converged analytics solution
Manta, Triton's object storage and converged analytics solution, is an open-source, HTTP-based object store that uses OS containers to enable running arbitrary compute on data at rest (i.e., without copying data out of the object store). The intended use-cases are wide-ranging:
- web assets (e.g., images, HTML and CSS files, and so on), with the ability to convert or resize images without copying any data out of Manta
- backup storage (e.g., tarballs)
- video storage and transcoding
- log storage and analysis
- data warehousing
- software crash dump storage and analysis
Joyent operates a public-facing production Manta service, but all the pieces required to deploy and operate your own Manta are open source. This repo provides documentation for the overall Manta project and pointers to the other repositories that make up a complete Manta deployment.
The fastest way to get started with Manta depends on what exactly one wishes to do.
To see a detailed, real example of using Manta, check out Kartlytics: Applying Big Data Analytics to Mario Kart.
To learn about installing and operating your own Manta deployment, see the Manta Operator's Guide.
For help with working on Manta and building and testing your changes, see the developer notes
Community discussion about Manta happens in two main places:
The manta-discuss mailing list. If you wish to send mail to the list you'll need to join, but you can view and search the archives online without being a member.
In the #manta IRC channel on the Freenode IRC network.
You can also follow @MantaStorage on Twitter for updates.
Manta is deployed on top of Joyent's Triton DataCenter platform (just "Triton" for short), which is also open-source. Triton provides services for operating physical servers (compute nodes), deploying services in containers, monitoring services, transmitting and visualizing real-time performance data, and a bunch more. Manta primarily uses Triton for initial deployment, service upgrade, and service monitoring.
Building and Deploying Manta
Manta is built and packaged with Triton DataCenter. Building the raw pieces uses the same mechanisms as building the services that are part of Triton. When you build a Triton headnode image (which is the end result of the whole Triton build process), one of the built-in services you get is a Manta deployment service, which is used to bootstrap a Manta installation.
Once you have Triton set up, follow the instructions in the Manta Operator's Guide to deploy Manta. The easiest way to play around with your own Manta installation is to first set up a Triton cloud-on-a-laptop (COAL) installation in VMware and then follow those instructions to deploy Manta on it.
If you want to deploy your own builds of Manta components, see "Deploying your own Manta Builds" below.
This repository is just a wrapper containing documentation about Manta. Manta is actually made up of several components stored in other repos.
The front door services respond to requests from the internet at large:
- muppet: haproxy + stud-based SSL terminator and loadbalancer
- muskie: Node-based API server
- mahi: authentication cache
- medusa: handles interactive (mlogin) sessions
The metadata tier stores the entire object namespace (not object data) as well as information about compute jobs and backend storage system capacity:
- manatee: high-availability postgres cluster using synchronous replication and automatic fail-over
- moray: Node-based key-value store built on top of manatee. Also responsible for monitoring manatee replication topology (i.e., which postgres instance is the master).
- electric-moray: Node-based service that provides the same interface as Moray, but which directs requests to one or more Moray+Manatee shards based on hashing the Moray key.
The storage tier is responsible for actually storing bits on disk:
- mako: nginx-based server that receives PUT/GET requests from Muskie to store object data on disk.
The compute tier (also called Marlin) is responsible for the distributed execution of user jobs. Most of it is contained in the Marlin repo, and it consists of:
- jobsupervisor: Node-based service that stores job execution state in moray and coordinates execution across the physical servers
- marlin agent: Node-based service (a Triton agent) that runs on each physical server and is responsible for executing user jobs on that server
- lackey: a Node-based service that runs inside each compute zone under the direction of the marlin agent. The lackey is responsible for actually executing individual user tasks inside compute containers.
- wrasse: job archiver and purger, which removes job information from moray after the job completes and saves the lists of inputs, outputs, and errors back to Manta for user reference
There are a number of services not part of the data path that are critical for Manta's operation:
- binder: hosts both ZooKeeper (used for manatee leader election and for group membership) and a Node-based DNS server that keeps track of which instances of each service are online at any given time
- mola: garbage collection (removing files from storage servers corresponding to objects that have been deleted from the namespace) and audit (verifying that objects in the index tier exist on the storage hosts)
- mackerel: metering (computing per-user details about requests made, bandwidth used, storage used, and compute time used)
- madtom: real-time "is-it-up?" dashboard, showing the status of all services deployed
- marlin-dashboard: real-time dashboard showing detaild status for the compute tier
- minnow: a Node-based service that runs inside mako zones to periodically report storage capacity into Moray
With the exception of the Marlin agent and lackey, each of the above components are services, of which there may be multiple instances in a single Manta deployment. Except for the last category of non-data-path services, these can all be deployed redundantly for availability and additional instances can be deployed to increase capacity.
Finally, scripts used to set up these component zones live in the https://github.com/joyent/manta-scripts repo.
For more details on the architecture, including how these pieces actually fit together, see "Architecture Basics" in the Manta Operator's Guide.
Deploying your own Manta Builds
As described above, as part of the normal Manta deployment process, you start with the "manta-deployment" zone that's built into Triton. Inside that zone, you run "manta-init" to fetch the latest Joyent build of each Manta component. Then you run Manta deployment tools to actually deploy zones based on these builds.
The easiest way to use your own custom build is to first deploy Manta using the default Joyent build and then replace whatever components you want with your own builds. This will also ensure that you're starting from a known-working set of builds so that if something goes wrong, you know where to start looking. To do this:
Complete the Manta deployment procedure from the Manta Operator's Guide.
Build a zone image for whatever zone you want to replace. See the instructions for building Triton zone images. Manta zones work the same way. The output of this process will be a zone image, identified by uuid. The image is comprised of two files: an image manifest (a JSON file) and the image file itself (a binary blob).
Import the image into the Triton DataCenter that you're using to deploy Manta. (If you've got a multi-datacenter Manta deployment, you'll need to import the image into each datacenter separately using this same procedure.)
Copy the image and manifest files to the Triton headnode where the Manta deployment zone is deployed. For simplicity, assume that the manifest file is "/var/tmp/my_manifest.json" and the image file is "/var/tmp/my_image". You may want to use the image uuid in the filenames instead.
Import the image using:
sdc-imgadm import -m /var/tmp/my_manifest.json -f /var/tmp/my_image
Now you can use the normal Manta zone update procedure (from the Manta Operator's Guide. This involves saving the current configuration to a JSON file using "manta-adm show -sj > config.json", updating the configuration file, and then applying the changes with "manta-adm update < config.json". When you modify the configuration file, you can use your image's uuid in place of whatever service you're trying to replace.
If for some reason you want to avoid deploying the Joyent builds at all, you'll have to follow a more manual procedure. One approach is to update the SAPI configuration for whatever service you want (using sdc-sapi -- see SAPI) immediately after running manta-init but before deploying anything. Note that each subsequent "manta-init" will clobber this change, though the SAPI configuration is normally only used for the initial deployment anyway. The other option is to apply the fully-manual install procedure from the Manta Operator's Guide (i.e., instead of using manta-deploy-coal or manta-deploy-lab) and use a custom "manta-adm" configuration file in the first place. If this is an important use case, file an issue and we can improve this procedure.
The above procedure works to update Manta zones, which are most of the components above. The other two kinds of components are the platform and agents. Both of these procedures are documented in the Manta Operator's Guide, and they work to deploy custom builds as well as the official Joyent builds.
Contributing to Manta
To report bugs or request features, you can submit issues to the Manta project on Github. If you're asking for help with Joyent's production Manta service, you should contact Joyent support instead.
See the Contribution Guidelines for information about contributing changes to the project.
Manta assumes several constraints on the data storage problem:
- There should be one canonical copy of data. You shouldn't need to copy data in order to analyze it, transform it, or serve it publicly over the internet.
- The system must scale horizontally in every dimension. It should be possible to add new servers and deploy software instances to increase the system's capacity in terms of number of objects, total data stored, or compute capacity.
- The system should be general-purpose. (That doesn't preclude special-purpose interfaces for use-cases like log analysis or video transcoding.)
- The system should be strongly consistent and highly available. In terms of CAP, Manta sacrifices availability in the face of network partitions. (The reasoning here is that an AP cache can be built atop a CP system like Manta, but if Manta were AP, then it would be impossible for anyone to get CP semantics.)
- The system should be transparent about errors and performance. The public API only supports atomic operations, which makes error reporting and performance easy to reason about. (It's hard to say anything about the performance of compound operations, and it's hard to report failures in compound operations.) Relatedly, a single Manta deployment may span multiple datacenters within a region for higher availability, but Manta does not attempt to provide a global namespace across regions, since that would imply uniformity in performance or fault characteristics.
From these constraints, we define a few design principles:
- Manta presents an HTTP interface (with REST-based PUT/GET/DELETE operations) as the primary way of reading and writing data. Because there's only one copy of data, and some data needs to be available publicly (e.g., on the internet over standard protocols), HTTP is a good choice.
- Manta is an object store, meaning that it only provides PUT/GET/DELETE for entire objects. You cannot write to the middle of an object or append to the end of one. This constraint makes it possible to guarantee strong consistency and high availability, since only the metadata tier (i.e., the namespace) needs to be strongly consistent, and objects themselves can be easily replicated for availability.
- Users express computation in terms of shell scripts, which can make use of any programs installed in the default compute environment, as well as any objects stored in Manta. You can store your own programs in Manta and use those, or you can use tools like curl(1) to fetch a program from the internet and use that. This approach falls out of the requirement to be a general-purpose system, and imposes a number of other constraints on the implementation (like the use of strong OS-based containers to isolate users).
- Users express distributed computation in terms of map and reduce operations. As with Hadoop and other MapReduce-based systems, this allows the system to identify which parts can be parallelized and which parts cannot in order to maximize performance.
It's easy to underestimate the problem of just reliably storing bits on disk. It's commonly assumed that the only components that fail are disks, that they fail independently, and that they fail cleanly (e.g., by reporting errors). In reality, there are a lot worse failure modes than disks failing cleanly, including:
- disks or HBAs dropping writes
- disks or HBAs redirecting both read and write requests to the wrong physical blocks
- disks or HBAs retrying writes internally, resulting in orders-of-magnitude latency bubbles
- disks, HBAs, or buses corrupting data at any point in the data path
Manta delegates to ZFS to solve the single-system data storage problem. To handle these cases,
- ZFS stores block checksums separately from the blocks themselves.
- Filesystem metadata is stored redundantly (on separate disks). Data is typically stored redundantly as well, but that's up to user configuration.
- ZFS is aware of how the filesystem data is stored across several disks. As a result, when reads from one disk return data that doesn't match the expected checksum, it's able to read another copy and fix the original one.
For a more detailed discussion, see the ACM Queue article "Bringing Arbitrary Compute to Authoritative Data".
For background on the problem space and design principles, check out "Bringing Arbitrary Compute to Authoritative Data".
For background on the overall design approach, see "There's Just No Getting Around It: You're Building a Distributed System".
For information about how Manta is designed to survive component failures and maintain strong consistency, see Fault tolerance in Manta.
Applications and customer stories: