LIGHTWEIGHT MESSAGING AND RPC IN DISTRIBUTED

SYSTEMS

Max A. Alexejev11.10.2012

Some Theory to start with…

Messaging System

Message (not packet/byte/…) as a minimal transmission unit.The whole system unifies• Underlying protocol (TCP, UDP)• UNICAST or MULTICAST• Data format (message types & structure)Tied with• Serialization format (text or binary)

Typical peer-to-peer messaging

Producer[host, port]

Consumer[host, port]

Typical broker-based messaging

Producer[bhost, bport] Broker Consumer

[bhost, bport]

• Broker is an indirection layer between producer and consumer.

• Producer PUSHes messages to broker.• Consumer PULLs messages from broker.

The trick is…

Producer[bhost, bport] Broker Consumer

[bhost, bport]

• Producers and consumers are logical units.• Both P and C may be launched in multiple

instances.• p2p and pubsub terms are expressed in terms of

these logical (!) units.• Even broker may be distributed or replicated entity.

Generic SOA picture

S1

S2

S3

S4

S5

S6

In a generic case

• A service may be both a consumer for many producers and a producer to many consumers

Characteristics and Features• Topology (1-1, 1-N, N-N)• Retries• Service discovery• Guaranteed delivery (in case yes – at-least-once or exactly-once)• Ordering• Acknowledge• Disconnect detection• Transactions support (can participate in distributed transactions)• Persistence• Portability (one or many languages and platforms)• Distributed or not• Highly available or not• Type (p2p or broker-based)• Load balancing strategy for consumers• Client backoff strategy for producers• Tracing support• Library or standalone software

Main classes

• ESBs (Enterprise service buses)– Slow, but most feature-rich. MuleESB, JbossESB,

Apache Camel, many commercial.• JMS implementations– ActiveMQ, JBOSS Messaging, Glassfish, etc.

• AMQP implementations– RabbitMQ, Qpid, HornetQ, etc.

• Lightweight modern stuff - unstandardized– ZeroMQ, Finagle, Kafka, Beanstalkd, etc.

Messaging Performance

As usual, its about throughput and latency…Major throughput factors:– Network hardware used– UNICAST vs MULTICAST (for fan-out)Major latency factors:– Persistence (batched or single-message

persistence involves sequential or random disk writes)

– Transactions– Broker replication– Delivery guarantees (at-least-once & exactly-once)

Guaranteed delivery

Involves additional logic both on Producer, Consumer and Broker (if any)!

This is at-least-once delivery:• Producer needs to get ack’ed by Broker• Consumer needs to track high-watermark of

messages received from Broker

Exact-once delivery requires more work and even more expensive. Typically implemented as 2-phase commit.

Ordering (distributed broker scenario)

• Producers receive messages in any order. Very cheap.

No Ordering

• Messages are ordered within single data partition. Such as: stock symbol, account number, etc. Possible to create well-performing implementation of distributed broker.

Partitioned Ordering

• All incoming messages are fairly ordered. Scalability and performance is limited.

Global (fair) ordering

Remote procedure calls

Inherently builds on top of some messaging.Method call as a minimal unit (3 states: may succeed returning optional value, throw exception, or time out).

Adds some RPC-specific characteristics & features:• Sync or async• Distributed stack traces for exceptions• Interfaces and structs declaration (possibly, via some

DSL) – often come with serialization library• May support schema evolution

Serialization libraries

Currently, there are 4 clear winners:1. Google Protocol buffers (with ProtoStuff)2. Apache Thrift3. Avro4. MessagePack

All provide DSLs and schema evolution. Difference is in wire format and DSL compiler form (program in C, in Java, or does not require compilation).

Messaging vs RPCMessaging

• In Broker-enabled case: Producers are decoupled from Consumers. Just push message and don’t care who pulls it.

• Natively matches messages to events in event-sourcing architectures.

RPC• Need to know

destination (i.e., service A must know service B and call signature).

Messaging and RPC dictate different programming models. RPC requires higher coupling between interacting services.

And Practice to continue!

Today’s Overview

• Broker[less] peer-to-peer messaging

ZeroMQ

• Broker-enabled persistent distributed pubsub

Apache Kafka

• Multi-paradigm and feature-rich RPC in Scala

Twitter Finagle

ZeroMQ

“It's sockets on steroids. It's like mailboxes with routing. It's fast!Things just become simpler. Complexity goes away. It opens the mind. Others try to explain by comparison. It's smaller, simpler, but still looks familiar.”

@ ZeroMQ 2.2 Guide

ZeroMQ - features• Topology – all, very flexible.• Retries – no.• Service discovery – no.• Guaranteed delivery – no.• Acknowledge – no.• Disconnect detection – no.• Transactions support (can participate in distributed transactions) – no.• Persistence – kind of.• Portability (one or many languages and platforms) – yes, there are many bindings.

However, library itself is written in C, so there’s only one “native” binding.• Distributed – yes.• Highly available or not – no.• Type (p2p or broker-based) – mostly p2p. In case of N-N topology, a broker needed in form

of ZMQ “Device” with ROUTER/DEALER type sockets.• Load balancing strategy for consumers – yes (???).• Client backoff strategy for producers – no.• Tracing support – no.• Library or standalone software – platform-native library + language bindings.

ZeroMQ – features explained

Isn’t there too much “no”s ?

Yes and no. Most of the features are not provided out of the box, but may be implemented manually in client and\or server.Some features are easy to implement (heartbeats, ack’s, retries, …) some are very complex (guaranteed delivery, persistence, high availability).

ZeroMQ – what’s bad about it

• First of all – name.Think of ZMQ as a sockets library and u’re happy.Consider it messaging middleware and u got frustrated just while reading guide.

• Complex implementation for multithreaded clients and servers.

• There were issues with services going down due to corrupted packets (so, may not be suitable for WAN).

• Some mess with development process. Initial ZMQ developers forked ZMQ as Crossroads.io

ZeroMQ – what’s good

• Huge list of supported platforms.• MULTICAST support for fan-out (1-N)

topology.• High raw performance.• Fluent connect/disconnect/reconnect

behavior – really feels how it should be.• Wants to be part of Linux kernel.

ZeroMQ – verdict

• Good for non-reliable high performance communication, when delivery semantics is not strict. Example - ngx-zeromq module for NGINX.

• Good if you can invest sufficient effort in building custom messaging platform on top of ZMQ as a network library. Example – ZeroRPC lib by DotCloud.

• Bad for any other purpose.

Apache Kafka

“We have built a novel messaging system for log processing called Kafka that combines the benefits of traditional log aggregators and messaging systems. On the one hand, Kafka is distributed and scalable, and offers high throughput. On the other hand, Kafka provides an API similar to a messaging system and allows applications to consume log events in real time.”

@ Kafka: a Distributed Messaging System for Log Processing, LinkedIn

Kafka - features• Topology – all.• Retries – no.• Service discovery – yes (Zookeeper).• Guaranteed delivery – no (at-least-once in normal case).• Acknowledge – no.• Disconnect detection – yes (Zookeeper).• Transactions support (can participate in distributed transactions) – no.• Persistence – yes.• Portability (one or many languages and platforms) – no.• Distributed – yes.• Highly available or not – no (work in progress).• Type (p2p or broker-based) – broker-enabled with distributed broker.• Load balancing strategy for consumers – yes.• Client backoff strategy for producers – yes .• Tracing support – no.• Library or standalone software – standalone + client libraries in Java.

Kafka - Architecture

Kafka - Internals

• Fast writes– Configurable batching– All writes are continuous, no need for random disk access

(i.e., works well on commodity SATA/SAS disks in RAID arrays)• Fast reads– O(1) disk search– Extensive use of sendfile()– No in-memory data caching inside Kafka – fully relies on OS

file system’s page cache• Elastic horizontal scalability– Zookeeper is used for brokers and consumers discovery– Pubsub topics are distributed among brokers

Kafka - conclusion

• Good for event-sourcing architectures (especially when they add HA support for brokers).

• Good to decouple incoming stream and processing to withstand request spikes.

• Very good for logs aggregation and monitoring data collection.

• Bad for transactional messaging with rich delivery semantics (exact once etc).

Twitter Finagle

“Finagle is a protocol-agnostic, asynchronous RPC system for the JVM that makes it easy to build robust clients and servers in Java, Scala, or any JVM-hosted language.Finagle supports a wide variety of request/response- oriented RPC protocols and many classes of streaming protocols.”

@ Twitter Engineering Blog

Finagle - features• Topology – all, very flexible.• Retries – yes.• Service discovery – yes (Zookeper).• Guaranteed delivery – no.• Acknowledge – no.• Disconnect detection – yes.• Transactions support (can participate in distributed transactions) – no.• Persistence – no.• Portability (one or many languages and platforms) – JVM only.• Distributed – yes.• Highly available – yes.• Type (p2p or broker-based) – p2p.• Load balancing strategy for consumers – yes (least connections etc).• Client backoff strategy for producers – yes (limited exponential).• Tracing support – yes (Zipkin ).• Library or standalone software – Scala library.

Finagle – from authors

Finagle provides a robust implementation of:• connection pools, with throttling to avoid TCP connection churn;• failure detectors, to identify slow or crashed hosts;• failover strategies, to direct traffic away from unhealthy hosts;• load-balancers, including “least-connections” and other strategies; • back-pressure techniques, to defend servers against abusive clients

and dogpiling.

Additionally, Finagle makes it easier to build and deploy a service that• publishes standard statistics, logs, and exception reports;• supports distributed tracing (a la Dapper) across protocols;• optionally uses ZooKeeper for cluster management; and• supports common sharding strategies.

Finagle – Layered architecture

Finagle - Filters

Finagle – Event loop

Finagle – Future pools

Finagle - conclusion

• Good for complex JVM-based RPC architectures.

• Very good for Scala, worse experience with Java (but yes, they have some utility classes).

• Works well with Thrift and HTTP (plus trivial protocols), but lacks support for Protobuf and other popular stuff.

• Active developers community (Google group), but project infrastructure (maven repo, versioning, etc) still being improved.

Resources

• Moscow Big Systems / Big Data grouphttp://www.meetup.com/bigmoscow/

• http://www.zeromq.org• http://zerorpc.dotcloud.com• http://kafka.apache.org• http://twitter.github.io/finagle/

QUESTIONS?

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