event driven architectures with kinesis

Mark Harrison

Event driven architectures with Kinesis

Justin Potter

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● MONOLITH!● Background● Microservice spaghetti● Microservice eventing● Kinesis Overview● (Soon to be) Open source Kinesis Driver ● Join Us

Agenda

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The traditional Oracle backed monolith architecture● Tight and ever increasing coupling● Difficult to scale with users and features● Difficult to maintain● Difficult to onboard new developers● Lacked modularity

Long ago in a …...

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Background

Journal (Tracking) - When a user enters a food, weight, or activity into Weight Watchers, it is sent to Journal.

Program (Points Calculation) - When a user wishes to view their Weight Watchers points, a call is made to Program to calculate and retrieve their point allocation. Program depends on the Journal service for it’s food tracking.

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Microservices!!● Scala, Akka, Play, Cassandra● REST based services● Each service represents a single domain concept

○ User Profile, Entitlements, Program …

We needed something different!

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It turns out magic bullets aren’t magic after all!!● Features cross service boundaries, a LOT● New features often increase requests between services

○ So one request now hits two services, that’s a 100% increase!● Immediate consistency means reduced availability

○ I’m looking at you… REST● Scaling out worked ok, just add more nodes!● Broadcasting data to other teams result in a direct dependency● Not enough emphasis on logging and monitoring

So… how’d that work out for you???

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Isolation

Pros

IndividuallyScalable

Pros

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Domain-ishDrivenDesign

Pros

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Easier to onboarddevelopers

Pros

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ScalesHorizontally

Pros

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Still TightlyCoupled

Cons

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Convoluted JSONResponses

Cons

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HigherLatency

Cons

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NoBackPressure

Cons

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ComplicatedIntegrationTesting

Cons

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No way to broadcastevents to other teams

Cons

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DataDuplicationBetweenServices

Cons

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More “Reactive”● Better monitoring● Decouple the services● More concise event payloads● Services hold their own state● Backpressure

Fix all the things!!!

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Considerations...● Accept that Eventual consistency is inevitable● Some services do too many things, some should be merged together!● The APIs will give the latest known state● Deal with the fact that duplicates will happen● Did I mention better monitoring??

But… How? What? Um...

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Think Kafka, but not :)● “Real-time” streaming platform● Multiple applications can publish and consumer to/from the same stream● Geared at higher latency workloads● Messages are consumed in batches● Elastic - easy to scale up and down● Some interesting constraints (more on that soon!)

Kinesis

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● Stream - An ordered sequence of data records, each stream has a unique name● Data Record - Unit of data stored in a Stream. Composed of a Sequence number, Partition

Key and Data Blob.● Partition key - Used to control distribution of records● Sequence Number - Each record has a sequence number. Sequence numbers for the same

partition key generally increase over time (non-sequentially). ● SubSequence Number - When aggregating records, multiple will records in the batch will

share a sequence number. In this instance, a SubSequence Number is used in combination to uniquely identify records.

Key concepts

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Even more key concepts

● Shard - A group of data records in a stream. A stream has one or more Shards. A Shard is a unit of throughput capacity and therefore determines the throughput of the Stream

● Producer - Puts messages onto a Shard ● Consumer - Gets data records from one

or more Shards. If multiple consumers share a name, they therefore share a checkpoint position.

● Checkpointing - The per consumer process of tracking the latest consumed record.

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Constraints

Wait.. it’s not all sunshine and roses?● Data can be persisted in Kinesis for up to 7 days, with an initial default of 1 day.● A Shard is a unit of throughput capacity

○ Reads - up to 5 transactions per second, with a maximum total data read rate of 2 MB per second

○ Writes - up to 1,000 records per second, up to a maximum total data write rate of 1 MB per second (including partition keys)

● When one application has multiple consumers, thereby sharing one checkpoint position, you must have at least one shard per instance

○ Think of a database table which tracks the current progress, in which the primary key is a combination of the application name and shard id

● You are charged on a per shard basis

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Interfacing with Kinesis

Out of the box, Amazon provides two libraries for programmatically interfacing with Kinesis● KPL - Kinesis Producer Library● KCL - Kinesis Consumer Library

Both are available in Java and handle a number of low level concerns● Stream connection and disconnection● Enumeration of shards● Parallel processing of the stream: consuming from and producing to a number of shards● Shard worker allocation and reallocation, balancing shards across workers● Batching and aggregation of records

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So what’s lacking???

Nobody’s perfect, right?● Java only, usage involves some interesting use of inheritance● Asynchronous & non-blocking processing on the consumer● Fool proof and non-blocking checkpointing● Throttling to reduce memory footprint● Smarter per message checkpointing● Hard to prevent the driver code becoming tangled with your

business logic

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Introducing...

The Weight Watchers Kinesis client

<Insert cool logo here>

Coming to a github repo near you soon…..

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Producer

Scala & (optionally) Akka based producer● Wraps the KPL driver● Choice of Scala Future or Akka based interface● Scala interface

○ Returns a Future for each message○ Completes when send (batch) is successful

● Actor interface○ Fire and forget or callback messages○ Optional throttling to limit the number of unsent

messages and therefore Futures

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Consumer

Scala & Akka based consumer● Wraps the KCL library● Provides fool proof checkpointing

○ Allows message failures within a configurable threshold● Messages sent for processing to provided Actor● Configurable retries● Asynchronous processing and checkpointing

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Scala Producer

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Akka Producer

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Consumer Event Processor

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Consumer Instantiation

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Performance

The performance scales reasonably well with the number of shards, with consistent increases as each new shard is added.

1 Shard - 5,000,000 messages:Records/sec: 42016Seconds elapsed: 119

2 Shards - 5,000,000 messages:Records/sec: 74626Seconds elapsed: 67

5 Shards - - 10,000,000 messagesRecords/sec: 140845Seconds elapsed: 71

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Mark Harrison

@markglh

Justin Potter

We’re Hiring!!www.weightwatchers.com/us/corporate-careers

Or email: Joanna.mark@weightwatchers.com