Ivan Burmistrov presents an algorithm for using Cassandra as an event logger. The algorithm aims to minimize time lag between writes and reads while ensuring write safety. Events are initially written in a "bad" state and only transition to "good" once no conflicting events exist. This allows reads to utilize caching while avoiding reading unstable data. The algorithm provides advantages like small time lags, cacheability and independence from synchronization, though writes are slower. Potential improvements and applications are also discussed.

1. Using Cassandra for event logging
Presentation
Using Cassandra for event logging
Ivan Burmistrov
SKB Kontur
December 9th, 2013

2. Using Cassandra for event logging
EDI
We develop EDI (Electronic data interchange) system.

3. Using Cassandra for event logging
EDI
We develop EDI (Electronic data interchange) system.

4. Using Cassandra for event logging
EventLogger
interface IEventLogger
{
void Write ( Event [] events ) ;
Event [] Read ( Event exclusiveEvent , int count ) ;
}
Availability for Read and Write.
Fast Write.
Read with minimal time lag.
No events in past while we making Read.
Same events chronology in any thread.
For any thread events orders for write and read are equals.

5. Using Cassandra for event logging
EventLogger
interface IEventLogger
{
void Write ( Event [] events ) ;
Event [] Read ( Event exclusiveEvent , int count ) ;
}
Availability for Read and Write.
Fast Write.
Read with minimal time lag.
No events in past while we making Read.
Same events chronology in any thread.
For any thread events orders for write and read are equals.

6. Using Cassandra for event logging
EventLogger
interface IEventLogger
{
void Write ( Event [] events ) ;
Event [] Read ( Event exclusiveEvent , int count ) ;
}
Availability for Read and Write.
Fast Write.
Read with minimal time lag.
No events in past while we making Read.
Same events chronology in any thread.
For any thread events orders for write and read are equals.

7. Using Cassandra for event logging
EventLogger
interface IEventLogger
{
void Write ( Event [] events ) ;
Event [] Read ( Event exclusiveEvent , int count ) ;
}
Availability for Read and Write.
Fast Write.
Read with minimal time lag.
No events in past while we making Read.
Same events chronology in any thread.
For any thread events orders for write and read are equals.

8. Using Cassandra for event logging
EventLogger
interface IEventLogger
{
void Write ( Event [] events ) ;
Event [] Read ( Event exclusiveEvent , int count ) ;
}
Availability for Read and Write.
Fast Write.
Read with minimal time lag.
No events in past while we making Read.
Same events chronology in any thread.
For any thread events orders for write and read are equals.

9. Using Cassandra for event logging
EventLogger
interface IEventLogger
{
void Write ( Event [] events ) ;
Event [] Read ( Event exclusiveEvent , int count ) ;
}
Availability for Read and Write.
Fast Write.
Read with minimal time lag.
No events in past while we making Read.
Same events chronology in any thread.
For any thread events orders for write and read are equals.

10. Using Cassandra for event logging
EventLogger
interface IEventLogger
{
void Write ( Event [] events ) ;
Event [] Read ( Event exclusiveEvent , int count ) ;
}
Availability for Read and Write.
Fast Write.
Read with minimal time lag.
No events in past while we making Read.
Same events chronology in any thread.
For any thread events orders for write and read are equals.

11. Using Cassandra for event logging
Obvious solution
Event
string EventId;
long Timestamp;
byte[] Content;

12. Using Cassandra for event logging
Obvious solution
Write(Event[] events)
Write columns for each event e:
e.Timestamp = NowTicks;
RowKey = e.Timestamp / Partition;
ColumnName = e.Timestamp + ":" + e.EventId;
ColumnValue = e.Content;
Read(Event exclusiveEvent, int count)
Execute get_slice from exclusive column;
Skip events with Timestamp > NowTicks - TimeLag.

13. Using Cassandra for event logging
Obvious solution
Write(Event[] events)
Write columns for each event e:
e.Timestamp = NowTicks;
RowKey = e.Timestamp / Partition;
ColumnName = e.Timestamp + ":" + e.EventId;
ColumnValue = e.Content;
Read(Event exclusiveEvent, int count)
Execute get_slice from exclusive column;
Skip events with Timestamp > NowTicks - TimeLag.

14. Using Cassandra for event logging
Advantages and disadvantages
Advantages
Easy implementation.
Write action is very fast.
Disadvantages
TimeLag =
TimeLag >
TimeLag >
TimeLag >
?
WriteTimeout + SyncDiff;
ReadTimeout + SyncDiff;
Cassandra worst write time;
We can’t cache events in unstable zone, if we want to read
events from it. I.e. more requests −→ more CPU for cassandra.

15. Using Cassandra for event logging
Advantages and disadvantages
Advantages
Easy implementation.
Write action is very fast.
Disadvantages
TimeLag =
TimeLag >
TimeLag >
TimeLag >
?
WriteTimeout + SyncDiff;
ReadTimeout + SyncDiff;
Cassandra worst write time;
We can’t cache events in unstable zone, if we want to read
events from it. I.e. more requests −→ more CPU for cassandra.

16. Using Cassandra for event logging
Advantages and disadvantages
Advantages
Easy implementation.
Write action is very fast.
Disadvantages
TimeLag =
TimeLag >
TimeLag >
TimeLag >
?
WriteTimeout + SyncDiff;
ReadTimeout + SyncDiff;
Cassandra worst write time;
We can’t cache events in unstable zone, if we want to read
events from it. I.e. more requests −→ more CPU for cassandra.

17. Using Cassandra for event logging
Advantages and disadvantages
Advantages
Easy implementation.
Write action is very fast.
Disadvantages
TimeLag =
TimeLag >
TimeLag >
TimeLag >
?
WriteTimeout + SyncDiff;
ReadTimeout + SyncDiff;
Cassandra worst write time;
We can’t cache events in unstable zone, if we want to read
events from it. I.e. more requests −→ more CPU for cassandra.

18. Using Cassandra for event logging
Advantages and disadvantages
Advantages
Easy implementation.
Write action is very fast.
Disadvantages
TimeLag =
TimeLag >
TimeLag >
TimeLag >
?
WriteTimeout + SyncDiff;
ReadTimeout + SyncDiff;
Cassandra worst write time;
We can’t cache events in unstable zone, if we want to read
events from it. I.e. more requests −→ more CPU for cassandra.

19. Using Cassandra for event logging
Advantages and disadvantages
Advantages
Easy implementation.
Write action is very fast.
Disadvantages
TimeLag =
TimeLag >
TimeLag >
TimeLag >
?
WriteTimeout + SyncDiff;
ReadTimeout + SyncDiff;
Cassandra worst write time;
We can’t cache events in unstable zone, if we want to read
events from it. I.e. more requests −→ more CPU for cassandra.

20. Using Cassandra for event logging
Advantages and disadvantages
Advantages
Easy implementation.
Write action is very fast.
Disadvantages
TimeLag =
TimeLag >
TimeLag >
TimeLag >
?
WriteTimeout + SyncDiff;
ReadTimeout + SyncDiff;
Cassandra worst write time;
We can’t cache events in unstable zone, if we want to read
events from it. I.e. more requests −→ more CPU for cassandra.

21. Using Cassandra for event logging
Our algorithm
Event
string EventId;
long Timestamp;
byte[] Content;
State State (Good or Bad);

22. Using Cassandra for event logging
Our algorithm
Write(Event event)
Timestamp = NowTicks, State = Bad, write event.
Are there Good events with bigger Timestamp?
No? State = Good, rewrite event.
Yes? Delete event and try again.

23. Using Cassandra for event logging
Our algorithm
Write(Event event)
Timestamp = NowTicks, State = Bad, write event.
Are there Good events with bigger Timestamp?
No? State = Good, rewrite event.
Yes? Delete event and try again.

24. Using Cassandra for event logging
Our algorithm
Write(Event event)
Timestamp = NowTicks, State = Bad, write event.
Are there Good events with bigger Timestamp?
No? State = Good, rewrite event.
Yes? Delete event and try again.

25. Using Cassandra for event logging
Our algorithm
Write(Event event)
Timestamp = NowTicks, State = Bad, write event.
Are there Good events with bigger Timestamp?
No? State = Good, rewrite event.
Yes? Delete event and try again.

26. Using Cassandra for event logging
Our algorithm
Read
We can read events (same as obvious solution) while it’s Good.

27. Using Cassandra for event logging
Our algorithm
Read
We can read events (same as obvious solution) while it’s Good.

28. Using Cassandra for event logging
Our algorithm
TimeLag = ?
TimeLag do not depends on timeouts.
Less write intensity −→ less TimeLag.
Less cassandra write time −→ less TimeLag.

29. Using Cassandra for event logging
Our algorithm
TimeLag = ?
TimeLag do not depends on timeouts.
Less write intensity −→ less TimeLag.
Less cassandra write time −→ less TimeLag.

30. Using Cassandra for event logging
Our algorithm
TimeLag = ?
TimeLag do not depends on timeouts.
Less write intensity −→ less TimeLag.
Less cassandra write time −→ less TimeLag.

31. Using Cassandra for event logging
Improvements
Improvements
"Are there Good events with bigger Timestamp?" for O(1).
Queue write requests and process its by batches.
We could remove the dependency from time sync, if NowTicks
will be monotonous.

32. Using Cassandra for event logging
Improvements
Improvements
"Are there Good events with bigger Timestamp?" for O(1).
Queue write requests and process its by batches.
We could remove the dependency from time sync, if NowTicks
will be monotonous.

33. Using Cassandra for event logging
Improvements
Improvements
"Are there Good events with bigger Timestamp?" for O(1).
Queue write requests and process its by batches.
We could remove the dependency from time sync, if NowTicks
will be monotonous.

34. Using Cassandra for event logging
Sharding
Simple sharding
We can divide all entities by independent parts.
For each part we can use separate eventLogger.

35. Using Cassandra for event logging
Sharding
Simple sharding
We can divide all entities by independent parts.
For each part we can use separate eventLogger.

36. Using Cassandra for event logging
Sharding
Sharding for reading
Timestamp = NowTicks, State = Bad, write events in
different rows.
Are there Good events with bigger Timestamp in any row?
No? State = Good, rewrite events.
Yes? Delete event and try again.

37. Using Cassandra for event logging
Sharding
Sharding for reading
Timestamp = NowTicks, State = Bad, write events in
different rows.
Are there Good events with bigger Timestamp in any row?
No? State = Good, rewrite events.
Yes? Delete event and try again.

38. Using Cassandra for event logging
Sharding
Sharding for reading
Timestamp = NowTicks, State = Bad, write events in
different rows.
Are there Good events with bigger Timestamp in any row?
No? State = Good, rewrite events.
Yes? Delete event and try again.

39. Using Cassandra for event logging
Advantages and disadvantages
Advantages
Small size of time lag. For 3500 writes per sec worst time lag
is 50ms and 10 events.
More cacheable.
Easy sharding.
Can be independed from time sync.
Disadvantages
More slow writes. But still enough fast.
Limit for writing threads.

40. Using Cassandra for event logging
Advantages and disadvantages
Advantages
Small size of time lag. For 3500 writes per sec worst time lag
is 50ms and 10 events.
More cacheable.
Easy sharding.
Can be independed from time sync.
Disadvantages
More slow writes. But still enough fast.
Limit for writing threads.

41. Using Cassandra for event logging
Advantages and disadvantages
Advantages
Small size of time lag. For 3500 writes per sec worst time lag
is 50ms and 10 events.
More cacheable.
Easy sharding.
Can be independed from time sync.
Disadvantages
More slow writes. But still enough fast.
Limit for writing threads.

42. Using Cassandra for event logging
Advantages and disadvantages
Advantages
Small size of time lag. For 3500 writes per sec worst time lag
is 50ms and 10 events.
More cacheable.
Easy sharding.
Can be independed from time sync.
Disadvantages
More slow writes. But still enough fast.
Limit for writing threads.

43. Using Cassandra for event logging
Advantages and disadvantages
Advantages
Small size of time lag. For 3500 writes per sec worst time lag
is 50ms and 10 events.
More cacheable.
Easy sharding.
Can be independed from time sync.
Disadvantages
More slow writes. But still enough fast.
Limit for writing threads.

44. Using Cassandra for event logging
Advantages and disadvantages
Advantages
Small size of time lag. For 3500 writes per sec worst time lag
is 50ms and 10 events.
More cacheable.
Easy sharding.
Can be independed from time sync.
Disadvantages
More slow writes. But still enough fast.
Limit for writing threads.

45. Using Cassandra for event logging
Another applications
Another applications
Indexing. We can use event log as notification about change
state of some objects −→ indexing. Cacheable is very useful
here!
Message queues.

46. Using Cassandra for event logging
Another applications
Another applications
Indexing. We can use event log as notification about change
state of some objects −→ indexing. Cacheable is very useful
here!
Message queues.