Andes: a Scalable persistent Messaging System

Charith Wickramarachchi, Srinath Perera, Shammi

Jayasinghe,Sanjiva WeerawaranaWSO2 Inc.

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Outline Dimensions of Scale Distributed Message Brokers Andes Architecture

Distributed Pub/sub architecture Distributed Queues architecture

Evaluation Conclusion

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Message Brokers (e.g. JMS. AMQP)

• A broker sits in the middle • Users send messages and receive them based

on interests (asynchronous)• Publish/Subscribe (Topic)– deliver to all

• Distributed Queues (Queue)– deliver to one, store and deliver, persistent

Messaging Systems in Real World

• Event Based Systems– Sensor Networks – System Monitoring

• CEP (Complex Event Processing)

• Social Networks • Real time Analytics• Job queues/ scheduling

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Challenges in Modern Message Oriented Middleware

Challenges High Availability Persistence Scale (Dimensions of scale)

Number of messages Number of Queues Size of messages

Current Messaging systems only scale in the first two dimensions

Why? Advances in technology areas like cloud computing and the increase of Internet based user bases demands for scalable message oriented middleware.

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Distributed Message Brokers

Single broker node cannot scale up Often messaging systems scale by a network of brokers where users

can subscribe or publish (both to queues or topics) at any node. There are many algorithms and routing rules (e.g. NaradaBrokering

[9], Gryphon [10], Oracle Advanced Queuing [7], TIBCO Rendezvous [8], IBM WebSphere MQ [6], and Padres [11])

Still doing ordered delivery with queues is a challenge

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

Alternative Message Broker Design• Most persistent message brokers use a per-node DB to

store messages with message routing. • But with large messages, cost of routing messages over the

network is very high• With availability of scalable storage and distributed

coordination middleware we propose an alternative architecture for scalable message brokers

• Main idea– Avoid message routing– Use scalable storage to share messages between nodes – Use distributed coordination to control the behavior

Andes – Overview

Each node polls the queues for subscriptions assigned to itself Andes stores message content separately Delivery logic works with messages IDs written to queue

representation in Cassandra and it only reads the messages at delivery

Andes – Overview (Contd.) Users can publish or subscribe to any node, and Andes delivers

messages to all as if subscribe and publish operations are done in the same node.

When published, each node writes the message to Cassandra There are nodes assigned to handle each queue/ topics, and

they read messages from Cassandra and send them to subscribers

Use Apache Zookeeper for coordination when needed Support for AMQP JMS and WS-Eventing while enabling

interoperability between protocols Built by extending Apache Qpid Code base

Publish/Subscribe Design

Image

Publish/Subscribe design (Contd.)• There is a queue representation implemented on

Cassandra• Andes creates a queues for each subscription • When a broker receives a published message, it stores

the message in a message store in Cassandra.• Broker will write message ids of relevant messages to

the relevant subscriber queues based on the subscribed topic.

• Each node polls Cassandra queue for subscriptions done at that node and delivers them to the subscribers.

• Messages are deleted from Subscription queues after acknowledgement, and Andes deletes messages from the message store after a timeout.

Distributed Queues • Strict ordering means there can be one messages

being delivered at a give time. – Say we receive messages m1, m2 for Queue Q. – Say we deliver messages m1 and m2 to client c1 and

c2 for Queue Q in parallel – Say m1->c1 failed, but by then m2->c2 is done. – If there is no other subscribers, now m1 has to be

delivered out of order. • Two implementation– Strict ordering support - using a distributed shared

lock with Zookeeper– Best effort implementation

Distributed Queue with Strict Ordering

image

Distributed Queue with Best Effort Ordering

Test Setup• Test 1: Comparison with other Brokers – Single Broker Node– Changed the size of messages with different brokers

(with 40 publishers)– Measured the throughput from subscribers for each

case after sending 10,000 messages. • Test 2: Scalability test– Multiple brokers – 20 subscribers on the same queue – Changed the number of publishers– Measured the throughput from subscribers for each

case after sending 10,000 messages.

Comparison with Other Brokers

• Andes does much better than Qpid• Andes does better than HornetMQ for large

messages

Initial Scalability Results

• Adding more nodes improves throughput • But more concurrency deteriorate the results

(need more work)

How does it Make a difference?

• Scale up in all 3 dimensions • Create only one copy of message while

delivery• High Availability and Fault Tolerance • File transfers in pub/sub (asynchronous style) • Let users choose between strict and best

effort messages• Replication of stored messages in the storage

Conclusion and Future Work• 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

• Future work– Further Scalability Tests– Testing with large messages – Fault Tolerance Tests

• Available as open source project under apache License.

Questions?

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