Scalable Persistent Message Brokering with WSO2 Message Broker

Srinath PereraSenior Software ArchitectWSO2 Inc.

Outline• Understanding Messaging

• Scalable Messaging

• WSO2 MB Architecture

• Distributed Pub/Sub architecture

• Distributed Queues architecture

• Usecases

• Conclusion

photo by John Trainoron Flickr http://www.flickr.com/photos/trainor/2902023575/, Licensed under CC

What is Messaging ?• We often program and design

distributed systems with RPC style communication (E.g. Web Services, Thrift, REST)

• RPC communication is

• Request/Response (there is always a response)

• Synchronous (client waits for response)

• Non-persistent (message is lost if something failed)

• But there are other 7 possibilities

Messaging Systems in Real World

• Sensor networks

• Monitoring/ Surveillance

• Business Activity Monitoring

• Job Scheduling Systems

• Social Networks

http://www.flickr.com/photos/imuttoo/4257813689/ by Ian Muttoo, http://www.flickr.com/photos/eastcapital/4554220770/, http://www.flickr.com/photos/patdavid/4619331472/ by Pat David copyright CC, http://www.fotopedia.com/items/flickr-

2548697541

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Why Messaging?

Messaging Systems

• Message Broker(s) as the middlemen

• There are two main models

• Queues

• Publish/Subscribe

http://www.geograph.org.uk/photo/2639458 and http://www.geograph.org.uk/photo/1138150

7

Distributed Queues

• A queue in the “Network”

• API Operations

• Put(M) – put a message

• Get() – get a message (dqueue)

• Subscribe() – send me a message when there is one

• E.g. SQS (Amazon Queuing Service)

• Use cases:- Job Queues, Store and process

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Publish/ Subscribe

• There is a topic space based on interest groups

• Publishers send messages to brokers

• Subscribers register their interest

• Brokers matches events (messages) and deliver to all interested parties

• Usecases: Surveillance, Monitoring

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Messaging APIs and Message Formats and Standards

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Scaling Message Brokers

There are several dimensions of Scale Number of messages Number of Queues Size of messages

Scaling Pub/Sub is relatively easy E.g. Narada Broker, Padres

Scaling Distributed Queues is harder

Scaling Distributed Queues

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Scaling Distributed Queues (Contd.)

Topology Pros Cons Supporting Systems

Master Salve Support HA No Scalability Qpid, ActiveMQ, RabbitMQ

Queue Distribution Scale to large number of Queues

Does not scale for large number of messages for a queue

RabbitMQ

Cluster Connections Support HA Might not support in-order delivery Logic runs in the client side takes local decisions.

HorentMQ

Broker/Queue Networks Load balancing and distribution

Fair load balancing is hard

ActiveMQ

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MB2 Messaging Architecture

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WSO2 MB

Cassandra and Zookeeper• Cassandra

• NoSQL Highly scalable new data model (column family)

• Highly scalable (multiple Nodes), available and no Single Point of Failure.

• SQL like query language (from 0.8) and support search through secondary indexes (well no JOINs, Group By etc. ..).

• Tunable consistency and replication

• Very high write throughput and good read throughput. It is pretty fast.

• Zookeeper

• Scalable, fault tolerant distributed coordination framework

How Distributed Queues Works ?

How Distributed Queues Works (Contd..)

How Distributed Queues Works (Contd.) Users can publish to any node (to a topic) When published, the node writes the message to queue in

Cassandra called “global queue” Each node have a queue in Cassandra called the node queue A worker running in a node reads message from global queue

and writes messages to a node queue that has a subscription for that topic.

A worker in each node reads messages from node queue and delivers to subscriber for that queue.

Node deletes messages only when subscriber has acked the delivery

How Pub/Sub Works ?

How Pub/Sub Works (Contd.) ?

Fault Tolerance

• We write the message to Cassandra once we receive the message

• We always read, process and then only delete messages (e.g. at client delivery after receiving the Ack)

• In case of a failure of nodes, then worse case there will be duplicates

Copy right , http://www.fotopedia.com/items/flickr-6206406047:CC license

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JMS support for MB2

Feature Yes

Pub / Sub Yes

Durable Subscriptions Yes

Hierarchical Topics Yes

Queues Yes

Message Selectors No, planned for 3.0

Transactions No, planned for 3.0

How does MB2 Make a difference?

• Scale up in all 3 dimensions

• Create only one copy of message while delivery

• High Availability and Fault Tolerance

• Large message transfers in pub/sub (asynchronous style)

• Let users choose between strict and best effort message delivery

• Replication of stored messages in the storagehttp://www.flickr.com/photos/flickcoolpix/

3566848458/sizes/m/in/photostream/

Usecase 1: Store and Process

Usecase 2: Message Bus

Future Work and Roadmap

• WSO2 MB (2013 Q4)

• Support for in-memory delivery through hazelcast (www.hazelcast.com)

• AMQP 1.0 support

• Transaction support

• If you have thoughts, please chat with me or join us at architecture@wso2.org

http://www.flickr.com/photos/24071429@N08/2314391179/

Conclusion• Provides an alternative architecture for

scalable message brokers using Cassandra and Zookeeper

• It provides

• A publish/subscribe model that does not need any coordination between broker nodes

• A strict mode for distributed queues that provides in order delivery

• A best-effort mode for distributed queue

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Questions?