This document discusses using Apache Cassandra to store and manage time series data in OpenNMS. It describes some limitations of the existing RRDTool-based data storage, such as high I/O requirements for updating and aggregating data. Cassandra is presented as an alternative that is optimized for write throughput, flexible data modeling, high availability, and ability to perform aggregations at read time rather than write time. The Newts project is introduced as a standalone time series data store built on Cassandra that aims to provide fast storage and retrieval of raw samples along with flexible aggregation capabilities.

1. Time Series Data With
Apache Cassandra
Strangeloop
September 19, 2014
Eric Evans
eevans@opennms.org
@jericevans

2. Open

3. Open

4. Open

5. Open

6. Network
Management
System

7. OpenNMS: What It Is
● Network Management System
○ Discovery and Provisioning
○ Service monitoring
○ Data collection
○ Event management, notifications
● Java, open source, GPLv3
● Since 1999

8. Time series: RRDTool
● Round Robin Database
● First released 1999
● Time series storage
● File-based, constant-size, self-maintaining
● Automatic, incremental aggregation

9. … and oh yeah, graphing

10. Consider
● 5+ IOPs per update (read-modify-write)!
● 100,000s of metrics, 1,000s IOPS
● 1,000,000s of metrics, 10,000s IOPS
● 15,000 RPM SAS drive, ~175-200 IOPS

12. Hmmm
We collect and write a great deal; We read
(graph) relatively little.
So why are we aggregating everything?

13. Also
● Not everything is a graph
● Inflexible
● Incremental backups impractical
● Availability subject to filesystem access

14. TIL
Metrics typically appear in groups that are
accessed together.
Optimizing storage for grouped access is a
great idea!

15. What OpenNMS needs:
● High throughput
● High availability
● Late aggregation
● Grouped storage/retrieval

16. Cassandra
● Apache top-level project
● Distributed database
● Highly available
● High throughput
● Tunable consistency

17. SSTables
Writes
Memtable
Commitlog
SSTable
Memory
Disk

18. Write Properties
● Optimized for write throughput
● Sorted on disk
● Perfect for time series!

19. Partitioning
A
B
C
Key: Apple
...
Z A

20. Placement
A
B
C
Key: Apple
...

21. Replication
A
B
C
Key: Apple
...

22. CAP Theorem
Consistency
Availability
Partition tolerance

23. Consistency
A
B
?
W=2

24. Consistency
?
B
C
R=2
R+W > N

25. Distribution Properties
● Symmetrical
● Linearly scalable
● Redundant
● Highly available

26. D ata M odel

27. Data Model
resource

28. Data Model
resource
T1 T2 T3

29. Data Model
resource
T1
M1 M2
V1 V2
M3
V3
T2
M1 M2
V1 V2
M3
V3
T3
M1 M2
V1 V2
M3
V3

30. Data Model
CREATE TABLE samples (
T timestamp,
M text,
V double,
resource text,
PRIMARY KEY(resource, T, M)
);

31. Data model
resource T1 M1 V1 T2 M1 V1 T3 M1 V1

32. Data model
resource T1 M1 V1 T2 M1 V1 T3 M1 V1
SELECT * FROM samples
WHERE resource = ‘resource’
AND T >= ‘T1’ AND T <= ‘T3’;

33. Data model
resource T1 M1 V1 T2 M1 V1 T3 M1 V1
SELECT * FROM samples
WHERE resource = ‘resource’
AND T >= ‘T1’ AND T <= ‘T3’;

34. Data model
resource T1 M1 V1 T2 M1 V1 T3 M1 V1
resource T1 M1 V1
resource T2 M2 V2
resource T3 M3 V3

35. Newts
● Standalone time series data-store
○ Java API
○ REST interface
● Raw sample storage and retrieval
● Flexible aggregations (computed at read)
○ Rate (from counter types)
○ Pluggable aggregation functions
○ Arbitrary calculations

36. Newts
● Cassandra-speed
● Resource search indexing (preliminary)
● Approaching “1.0”
● Apache license
● Github (http://github.com/OpenNMS/newts)
● http://newts.io

37. Fin