Avoiding the Pit of Despair - Event Sourcing with Akka and Cassandra

Avoiding the Pit of Despair: Event Sourcing

with Akka and Cassandra

Luke Tillman (@LukeTillman)

Language Evangelist at DataStax

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http://datastax.com/cassandrasummit2015

• Evangelist with a focus on Developers

• Long-time Developer on RDBMS (lots of .NET)

Who are you?!

3

1 An Intro to Akka and Event Sourcing

2 An Event Journal in Cassandra

3 Accounting for Deletes

4 Lessons Learned

4

An Intro to Akka and Event Sourcing

5

Akka

• An actor framework for building concurrent and distributed applications

• Originally for the JVM (written in Scala, includes Java bindings)

• Ported to .NET/CLR (written in C#, includes F# bindings)

• Both open source, on GitHub

6

http://akka.io

http://getakka.net

http://akka.io/

http://getakka.net/

Actors in Akka

• Lightweight, isolated processes

• No shared state (so nothing to lock or synchronize)

• Actors have a mailbox (message queue)

• Process messages one at a time – Update state

– Change behavior

– Send messages to other Actors

7

Actor

mailbox

state

behavior

messages

sent asynchronously

send messages to other

Actors (could be replies)

Obligatory E-Commerce Example

8

ShoppingCartActor

mailbox

state

behavior

messages

sent asynchronously

send messages to other

Actors (could be replies)

Examples:

InitializeCart

AddItemToCart

RemoveItemFromCart

ChangeItemQuantity

ApplyDiscount

GetCartItems

{ cart_id: 1345, user_id: 4762 created_on: "7/10/2015", items: [ { item_id: 7621, quantity: 1, unit_price: 19.99 }, { item_id: 9134, quantity: 2, unit_price: 16.99 } ] }

Examples:

CartItems

ItemAdded

GetDiscount

if (items.length > 5) Become(Discounted)

Actors in Akka

• Break a complex system down into lots of smaller pieces

• Can easily scale to millions of actors on a single machine – 2.5 million per GB of heap

• Since actors only communicate via async message passing, they can also be distributed across many machines – Location Transparency

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Actor

Actor

Actor

Actor

Actor

Actor

Actor

Actor

Actor

Actor

Actor

Actor

Actor

Actor

Actor

Actor

Actor

Actor

Actor

Actor

Actor

send messages via network

Persistent Actors in Akka

• Actor mailbox and state are transient by default in Akka – Crash/Restart, messages and state

are lost

10

Actor

mailbox

state

behavior



are lost

• We could just write code in the actor to persist the current state to storage

10

Actor

mailbox

state

behavior



are lost

• We could just write code in the actor to persist the current state to storage

• Akka Persistence plugin provides an API for persisting these to durable storage using Event Sourcing

10

PersistentActor

mailbox

state

behavior

persistenceId

sequenceNr

Persist(event)

SaveSnapshot(payload)

Persistence with Event Sourcing

• Instead of keeping the

current state, keep a journal

of all the deltas (events)

• Append only (no UPDATE or

DELETE)

• We can replay our journal of

events to get the current

state

13

Shopping Cart (id = 1345)

user_id= 4762 created_on= 7/10/2015…

Cart Created

item_id= 7621 quantity= 1 price= 19.99

Item Added


Item Added

Item Removed item_id= 7621

Qty Changed item_id= 9134 quantity= 1

Speeding up Replays with Snapshots

12



Cart Created


Item Added


Item Added


12



Cart Created


Item Added


Item Added

{ event_id: 3, cart_id: 1345, user_id: 4762 created_on: "7/10/2015", items: [ { item_id: 7621, quantity: 1, price: 19.99 }, { item_id: 9134, quantity: 2, price: 16.99 } ] }

Take Snapshot


12



Cart Created


Item Added


Item Added





12



Cart Created


Item Added


Item Added




Load Snapshot

Event Sourcing in Practice

• Typically two kinds of storage:

– Event Journal Store

– Snapshot Store

• A history of how we got to the

current state can be useful

• We've also got a lot more data

to store than we did before

18



Cart Created


Item Added


Item Added



Why Cassandra?

• Lots of Persistence implementations available – Akka: Cassandra, JDBC, Kafka, MongoDB, etc.

– Akka.NET: Cassandra, MS SQL, Postgres

• Cassandra is really easy to scale out as you need to store more events for more actors

• Workload and Data Shape are great fits for C* – Transactional, Write-Heavy workload

– Sequentially written, immutable events (looks a lot like time series data)

19

The Async Journal API

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Task ReplayMessagesAsync( string persistenceId, long fromSequenceNr, long toSequenceNr, long max, Action<IPersistentRepr> replayCallback); Task<long> ReadHighestSequenceNrAsync( string persistenceId, long fromSequenceNr); Task WriteMessagesAsync( IEnumerable<IPersistentRepr> messages); Task DeleteMessagesToAsync( string persistenceId, long toSequenceNr, bool isPermanent);

def asyncReplayMessages( persistenceId: String, fromSequenceNr: Long, toSequenceNr: Long, max: Long) (replayCallback: PersistentRepr => Unit) : Future[Unit] def asyncReadHighestSequenceNr( persistenceId: String, fromSequenceNr: Long) : Future[Long] def asyncWriteMessages( messages: immutable.Seq[PersistentRepr]) : Future[Unit] def asyncDeleteMessagesTo( persistenceId: String, toSequenceNr: Long, permanent: Boolean) : Future[Unit]

The Journal API Summary

• Write Method

– For a given actor, write a group

of messages

• Delete Method

– For a given actor, permanently

or logically delete all messages

up to a given sequence number

• Read Methods

– For a given actor, read back all

the messages between two

sequence numbers

– For a given actor, read the

highest sequence number that's

been written

21

An Event Journal in Cassandra

Data Modeling for Reads and Writes

22

A Simple First Attempt

• Use persistence_id as partition key – all messages for a given persistence Id

together

• Use sequence_number as clustering column

– order messages by sequence number inside a partition

• Read all messages between two sequence numbers

• Read the highest sequence number

23

CREATE TABLE messages ( persistence_id text, sequence_number bigint, message blob, PRIMARY KEY ( persistence_id, sequence_number) );

SELECT * FROM messages WHERE persistence_id = ? AND sequence_number >= ? AND sequence_number <= ?;

SELECT sequence_number FROM messages WHERE persistence_id = ? ORDER BY sequence_number DESC LIMIT 1;

A Simple First Attempt

• Write a group of messages

• Use a Cassandra Batch statement to ensure all messages (success) or no messages (failure) get written

• What's the problem with this data model (ignoring implementing deletes for now)?

24

CREATE TABLE messages ( persistence_id text, sequence_number bigint, message blob, PRIMARY KEY ( persistence_id, sequence_number) );

BEGIN BATCH INSERT INTO messages ... ; INSERT INTO messages ... ; INSERT INTO messages ... ; APPLY BATCH;

Unbounded Partition Growth

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Cassandra Data Modeling Anti-Pattern #1 Unbounded Partition Growth

• Cassandra has a hard limit of 2

billion cells in a partition

• But there's also a practical limit

– Depends on row/cell data size, but

likely not more than millions of rows

26

Journal

INSERT INTO messages ...

persistence_id= '57ab...'

seq_nr= 1

seq_nr= 2

message= 0x00...

message= 0x00...

∞?

Fixing the Unbounded Partition Growth Problem

• General strategy: add a column to the partition key – Compound partition key

• Can be data that's already part of the model, or a "synthetic" column

• Allow users to configure a partition size in the plugin – Partition Size = number of rows per

partition

– This should not be changeable once messages have been written

• Partition number for a given sequence number is then easy to calculate – (seqNr – 1) / partitionSize

(100 – 1) / 100 = partition 0

(101 – 1) / 100 = partition 1

27

CREATE TABLE messages ( persistence_id text, partition_number bigint, sequence_number bigint, message blob, PRIMARY KEY ( (persistence_id, partition_number), sequence_number) );


• Read all messages between two sequence numbers

• Read the highest sequence number

28


SELECT * FROM messages WHERE persistence_id = ? AND partition_number = ? AND sequence_number >= ? AND sequence_number <= ?;

SELECT sequence_number FROM messages WHERE persistence_id = ? AND partition_number = ? ORDER BY sequence_number DESC LIMIT 1;

(repeat until we reach sequence number or run out of partitions)

(repeat until we run out of partitions)


• Write a group of messages

• A Cassandra Batch statement might now write to multiple partitions (if the sequence numbers cross a partition boundary)

• Is that a problem?

29


BEGIN BATCH INSERT INTO messages ... ; INSERT INTO messages ... ; INSERT INTO messages ... ; APPLY BATCH;

RTFM: Cassandra Batches Edition

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"Batches are atomic by default. In the context of a Cassandra batch

operation, atomic means that if any of the batch succeeds, all of it will."

- DataStax CQL Docs http://docs.datastax.com/en/cql/3.1/cql/cql_reference/batch_r.html

"Although an atomic batch guarantees that if any part of the batch succeeds,

all of it will, no other transactional enforcement is done at the batch level.

For example, there is no batch isolation. Clients are able to read the first

updated rows from the batch, while other rows are still being updated on the

server."


Atomic? That's kind of a loaded word.

http://docs.datastax.com/en/cql/3.1/cql/cql_reference/batch_r.html






Multiple Partition Batch Failure Scenario

29

Journal

BEGIN BATCH ... APPLY BATCH;

CL = QUORUM

RF = 3


29

Journal


Batch

Log

Batch

Log

Batch

Log CL = QUORUM

RF = 3


• Once written to the

Batch Log successfully,

we know all the writes

in the batch will

succeed eventually

(atomic?)

29

Journal


CL = QUORUM

RF = 3





in the batch will

succeed eventually

(atomic?)

• Batch has been

partially applied

29

Journal


CL = QUORUM

RF = 3





in the batch will

succeed eventually

(atomic?)

• Batch has been

partially applied

• Possible to read a

partially applied batch

since there is no batch

isolation

29

Journal


CL = QUORUM

RF = 3

WriteTimeout

- writeType = BATCH

Reading Partially Applied Batches

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RTFM: Cassandra Batches Edition Part 2

38

"For example, there is no batch isolation. Clients are able to read the first

updated rows from the batch, while other rows are still being updated on the

server. However, transactional row updates within a partition key are

isolated: clients cannot read a partial update."


What we really need is Isolation.

When writing a group of messages, ensure that

we write the group to a single partition.




Logic Changes to Ensure Batch Isolation

• Still use configurable Partition Size

– not a "hard limit" but a "best attempt"

• On write, see if messages will all fit in the

current partition

• If not, roll over to the next partition early

• Reading is slightly more complicated

– For a given sequence number it might be in

partition n or (n+1)

39

seq_nr = 97

seq_nr = 98

seq_nr = 1

99 100

101

partition_nr = 1

partition_nr = 2

Partition Size = 100

Accounting for Deletes

40

Implementing Logical Deletes, Option 1

• Add an is_deleted column

to our messages table

• Read all messages between

two sequence numbers

41

CREATE TABLE messages ( persistence_id text, partition_number bigint, sequence_number bigint, message blob, is_deleted bool, PRIMARY KEY ( (persistence_id, partition_number), sequence_number) );



... sequence_number message is_deleted

... 1 0x00 true

... 2 0x00 true

... 3 0x00 false

... 4 0x00 false


• Pros: – On replay, easy to check if a

message has been deleted (comes included in message query's data)

• Cons: – Messages not immutable any

more

– Issue lots of UPDATEs to mark each message as deleted

– Have to scan through a lot of rows to find max deleted sequence number if we want to avoid issuing unnecessary UPDATEs

42

CREATE TABLE messages ( persistence_id text, partition_number bigint, sequence_number bigint, message blob, is_deleted bool, PRIMARY KEY ( (persistence_id, partition_number), sequence_number) );


• Add a marker column and

make it a clustering column

– Messages written with 'A'

– Deletes get written with 'D'



43

CREATE TABLE messages ( persistence_id text, partition_number bigint, sequence_number bigint, marker text, message blob, PRIMARY KEY ( (persistence_id, partition_number), sequence_number, marker) );



... sequence_number marker message

... 1 A 0x00

... 1 D null

... 2 A 0x00

... 3 A 0x00


• Pros – On replay, easy to peek at next

row to check if deleted (comes included in message query's data)

– Message data stays immutable

• Cons – Issue lots of INSERTs to mark

each message as deleted

– Have to scan through a lot of rows to find max deleted sequence number if we want to avoid issuing unnecessary INSERTs

– Potentially twice as many rows to store

44


Looking at Physical Deletes

• Physically delete messages to a given sequence number

• Still probably want to scan through rows to see what's already been deleted first

45


BEGIN BATCH DELETE FROM messages WHERE persistence_id = ? AND partition_number = ? AND marker = 'A' AND sequence_number = ?; ... APPLY BATCH;

• Can't range delete, so we have

to do lots of individual

DELETEs

Looking at Physical Deletes



• With how DELETEs work in

Cassandra, is there a potential

problem with this query?

46




Tombstone Hell: Queue-like Data Sets

47

Cassandra Data Modeling Anti-Pattern #2 Queue-like Data Sets

46

Journal persistence_id '57ab...'

partition_nr 1

message= 0x00...

seq_nr=1 marker='A'

...

Delete messages to a sequence number

BEGIN BATCH DELETE FROM messages WHERE persistence_id = '57ab...' AND partition_nr = 1 AND marker = 'A' AND sequence_nr = 1; ... APPLY BATCH;

message= 0x00...

seq_nr=2 marker='A'


46


partition_nr 1

message= 0x00...

seq_nr=1 marker='A'

seq_nr=1 marker='A'

Tombstone NO DATA HERE

...

Delete messages to a sequence number

BEGIN BATCH DELETE FROM messages WHERE persistence_id = '57ab...' AND partition_nr = 1 AND marker = 'A' AND sequence_nr = 1; ... APPLY BATCH;

message= 0x00...

seq_nr=2 marker='A'

seq_nr=2 marker='A'



• At some point compaction runs and we

don't have two versions any more, but

tombstones don't go away immediately

– Tombstones remain for gc_grace_seconds

– Default is 10 days

46


partition_nr 1

seq_nr=1 marker='A'


...

seq_nr=2 marker='A'



51


partition_nr 1

seq_nr=1 marker='A'


...

Read all messages between 2 sequence numbers

SELECT * FROM messages WHERE persistence_id = '57ab...' AND partition_number = 1 AND sequence_number >= 1 AND sequence_number <= [max value];

seq_nr=2 marker='A'


seq_nr=3 marker='A'


seq_nr=4 marker='A'


Avoid Tombstone Hell

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We need a way to avoid reading

tombstones when replaying messages.

SELECT * FROM messages

WHERE persistence_id = ?

AND partition_number = ?

AND sequence_number >= ?

AND sequence_number <= ?;

AND sequence_number >= ?

If we know what sequence number we've already deleted to

before we query, we could make that lower bound smarter.

A Third Option for Deletes

• Use marker as a clustering

column, but change the

clustering order

– Messages still 'A', Deletes 'D'



53

CREATE TABLE messages ( persistence_id text, partition_number bigint, marker text, sequence_number bigint, message blob, PRIMARY KEY ( (persistence_id, partition_number), marker, sequence_number) );

SELECT * FROM messages WHERE persistence_id = ? AND partition_number = ? AND marker = 'A' AND sequence_number >= ? AND sequence_number <= ?;


... sequence_number marker message

... 1 A 0x00

... 2 A 0x00

... 3 A 0x00


• Messages data no longer has deleted information, so how do we know what's already been deleted?

• Get max deleted sequence number

• Can avoid tombstones if done before getting message data

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SELECT sequence_number FROM messages WHERE persistence_id = ? AND partition_number = ? AND marker = 'D' ORDER BY marker DESC, sequence_number DESC LIMIT 1;


• Pros – Message data stays immutable

– Issue a single INSERT when deleting to a sequence number

– Read a single row to find out what's been deleted (no more scanning)

– Can avoid reading tombstones created by physical deletes

• Cons – Requires a separate query to find

out what's been deleted before getting message data

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Final Schema in Akka and Akka.NET

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https://github.com/krasserm/akka-persistence-cassandra https://github.com/akkadotnet/Akka.Persistence.Cassandra

https://github.com/krasserm/akka-persistence-cassandra






https://github.com/akkadotnet/Akka.Persistence.Cassandra

https://github.com/akkadotnet/Akka.Persistence.Cassandra

Lessons Learned

57

Summary

• Seemingly simple data models can

get a lot more complicated

• Avoid unbounded partition growth – Add data to your partition key

• Be aware of how Cassandra Logged Batches work – If you need isolation, only write to a single partition

• Avoid queue-like data sets and be aware of how tombstones might impact your queries – Try to query with ranges that avoid tombstones

58

Promo Codes 50% off Priority Passes: LukeT50

25% off Training and Certification: LukeTCert

http://datastax.com/cassandrasummit2015

Avoiding the Pit of Despair - Event Sourcing with Akka and Cassandra

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