In this talk, we'll dive into the event sourcing API used to persist actor state in Akka and talk about how we build a data model to support it in Cassandra. At first, the data model seems pretty straightforward, but the more we dig in, the more we see that a couple of classic Cassandra anti-patterns are pushing us close to the Pit of Despair. We'll come up with a way to avoid these problems so we can go on building distributed systems happily ever after with Akka and Cassandra.

1. Event Sourcing with Cassandra
Luke Tillman
Technical Evangelist
@LukeTillman

2. • Evangelist with a
focus on
Developers
– Long-time
Developer on
RDBMS (lots of
.NET)
• I still write a lot of
code, but now I also
do a lot of teaching
and speaking
Who are you?
2

3. A Quick Recap of Event Sourcing
3

4. 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
4
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5. 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
5
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Item Added
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quantity= 1

6. Why use Cassandra for Event Sourcing?
• Transactional (OLTP) Workload
• Sequentially written, immutable data
– Looks a lot like time series data
• Easy to scale out to capture more events
6

7. Event Sourcing Example: Akka Persistence
7

8. Akka Persistence 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
8

9. An Event Journal in Cassandra
Data Modeling for Reads and Writes
9

10. 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
10
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;

11. 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)?
11
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;

12. 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
12
Journal
INSERT INTO messages ...
persistence_id=
'57ab...'
seq_nr=
1
seq_nr=
2
message=
0x00...
message=
0x00...
∞?

13. 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
13
CREATE TABLE messages (
persistence_id text,
partition_number bigint,
sequence_number bigint,
message blob,
PRIMARY KEY (
(persistence_id, partition_number),
sequence_number)
);

14. Fixing the Unbounded Partition Growth Problem
• Read all messages between two
sequence numbers
• Read the highest sequence number
14
CREATE TABLE messages (
persistence_id text,
partition_number bigint,
sequence_number bigint,
message blob,
PRIMARY KEY (
(persistence_id, partition_number),
sequence_number)
);
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)

15. Fixing the Unbounded Partition Growth Problem
• 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?
15
CREATE TABLE messages (
persistence_id text,
partition_number bigint,
sequence_number bigint,
message blob,
PRIMARY KEY (
(persistence_id, partition_number),
sequence_number)
);
BEGIN BATCH
INSERT INTO messages ... ;
INSERT INTO messages ... ;
INSERT INTO messages ... ;
APPLY BATCH;

16. RTFM: Cassandra Batches Edition
16
"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."
- DataStax CQL Docs
http://docs.datastax.com/en/cql/3.1/cql/cql_reference/batch_r.html
Atomic? That's kind of a loaded word.

17. Multiple Partition Batch Failure Scenario
17
Journal
RF = 3

18. Multiple Partition Batch Failure Scenario
17
Journal
BEGIN BATCH
...
APPLY BATCH;
CL = QUORUM
RF = 3

19. Multiple Partition Batch Failure Scenario
17
Journal
BEGIN BATCH
...
APPLY BATCH;
Batch
Log
Batch
Log
Batch
Log
CL = QUORUM
RF = 3

20. Multiple Partition Batch Failure Scenario
• Once written to the
Batch Log successfully,
we know all the writes
in the batch will
succeed eventually
(atomic?)
17
Journal
BEGIN BATCH
...
APPLY BATCH;
CL = QUORUM
RF = 3

21. Multiple Partition Batch Failure Scenario
• Once written to the
Batch Log successfully,
we know all the writes
in the batch will
succeed eventually
(atomic?)
17
Journal
BEGIN BATCH
...
APPLY BATCH;
CL = QUORUM
RF = 3

22. Multiple Partition Batch Failure Scenario
• Once written to the
Batch Log successfully,
we know all the writes
in the batch will
succeed eventually
(atomic?)
• Batch has been
partially applied
17
Journal
BEGIN BATCH
...
APPLY BATCH;
CL = QUORUM
RF = 3

23. Multiple Partition Batch Failure Scenario
• Once written to the
Batch Log successfully,
we know all the writes
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
17
Journal
BEGIN BATCH
...
APPLY BATCH;
CL = QUORUM
RF = 3
WriteTimeout
- writeType = BATCH

24. RTFM: Cassandra Batches Edition Part 2
24
"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."
- DataStax CQL Docs
http://docs.datastax.com/en/cql/3.1/cql/cql_reference/batch_r.html
What we really need is Isolation.
When writing a group of messages, ensure that
we write the group to a single partition.

25. 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)
25
seq_nr = 97
seq_nr = 98
seq_nr = 1
99
100
101
partition_nr = 1
partition_nr = 2
PartitionSize=100

26. Accounting for Deletes
26

27. Option 1: Mark Individual Messages as Deleted
• Add an is_deleted column
to our messages table
• Read all messages between
two sequence numbers
27
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)
);
SELECT * FROM messages
WHERE persistence_id = ?
AND partition_number = ?
AND sequence_number >= ?
AND sequence_number <= ?;
(repeat until we reach sequence number or run out of partitions)
... sequence_number message is_deleted
... 1 0x00 true
... 2 0x00 true
... 3 0x00 false
... 4 0x00 false

28. Option 1: Mark Individual Messages as Deleted
• 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
28
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)
);

29. Option 2: Write a Marker Row for Each Deleted Row
• Add a marker column and
make it a clustering column
– Messages written with 'A'
– Deletes get written with 'D'
• Read all messages between
two sequence numbers
29
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)
);
SELECT * FROM messages
WHERE persistence_id = ?
AND partition_number = ?
AND sequence_number >= ?
AND sequence_number <= ?;
(repeat until we reach sequence number or run out of partitions)
... sequence_number marker message
... 1 A 0x00
... 1 D null
... 2 A 0x00
... 3 A 0x00

30. Option 2: Write a Marker Row for Each Deleted Row
• 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
30
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)
);

31. 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
31
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)
);
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

32. Looking at Physical Deletes
• Read all messages between
two sequence numbers
• With how DELETEs work in
Cassandra, is there a potential
problem with this query?
32
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)
);
SELECT * FROM messages
WHERE persistence_id = ?
AND partition_number = ?
AND sequence_number >= ?
AND sequence_number <= ?;
(repeat until we reach sequence number or run out of partitions)

33. Tombstone Hell: Queue-like Data Sets
33
Journal persistence_id
'57ab...'
partition_nr
1
message=
0x00...
seq_nr=1
marker='A'
...
message=
0x00...
seq_nr=2
marker='A'

34. Tombstone Hell: Queue-like Data Sets
33
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'

35. Tombstone Hell: Queue-like Data Sets
33
Journal persistence_id
'57ab...'
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'
Tombstone
NO DATA HERE

36. Tombstone Hell: Queue-like Data Sets
• 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
33
Journal persistence_id
'57ab...'
partition_nr
1
seq_nr=1
marker='A'
Tombstone
NO DATA HERE
...
seq_nr=2
marker='A'
Tombstone
NO DATA HERE

37. Tombstone Hell: Queue-like Data Sets
37
Journal persistence_id
'57ab...'
partition_nr
1
seq_nr=1
marker='A'
Tombstone
NO DATA HERE
...
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'
Tombstone
NO DATA HERE
seq_nr=3
marker='A'
Tombstone
NO DATA HERE
seq_nr=4
marker='A'
Tombstone
NO DATA HERE

38. Avoid Tombstone Hell
38
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.

39. A Third Option for Deletes
• Use marker as a clustering
column, but change the
clustering order
– Messages still 'A', Deletes 'D'
• Read all messages between
two sequence numbers
39
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 <= ?;
(repeat until we reach sequence number or run out of partitions)
... sequence_number marker message
... 1 A 0x00
... 2 A 0x00
... 3 A 0x00

40. A Third Option for Deletes
• 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
40
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 sequence_number FROM messages
WHERE persistence_id = ?
AND partition_number = ?
AND marker = 'D'
ORDER BY marker DESC,
sequence_number DESC
LIMIT 1;

41. A Third Option for Deletes
• 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
41
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)
);

42. Lessons Learned
42

43. 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
43

44. Questions?
@LukeTillman
https://www.linkedin.com/in/luketillman/
https://github.com/LukeTillman/
44