Describes how Blueflood works and future development direction

1. Blueflood
Simple Metrics Processing
Gary Dusbabek • Cassandra EU 2013

2. Motivation
Building Blocks
Future
Future Stuff

3. Motivation

4. Get
the Data
In

5. Each check
generates 2-20
metrics
Multiply by data
centers

7. Currently handling
120 million metrics
per hour

8. 40 million aggregate
Cassandra write
operations per hour

9. Get
the
Data
Out
Fast
Graphs!
Think:
Dashboards
SLA
is
important

10. Get
the
Data
Out
Get
the Data
Out
Fast
Graphs!
Think:
Dashboards
SLA
is
important

11. Get
the
Data
Out
Fast
Graphs!
Think:
Dashboards
SLA
is
important
Fast
Graphs

12. Multitenant

13. Different
SLAs
expectations

14. Hard

15. Tenants imply
Metadata

16. Hampers
generic computing
Systems

17. Lipstick system

18. Nice to Have

19. Not
Mission Critical

20. Don’t
Break the Bank

22. Avoid
Hadoop

23. HATE
Hadoop

24. HATE
Hadoop

25. We Ended Up With This
Ingestion API
Ingestion
Transform
Query API
Metadata + Cache
Rollup Scheduler
State Management
Java Ingestion
Library
Java Rollup
Library
Database
(Cassandra)
Java Query
Library

26. We Ended Up With This
Ingestion API
Ingestion
Transform
Query API
Metadata + Cache
Rollup Scheduler
State Management
Java Ingestion
Library
Java Rollup
Library
Database
(Cassandra)
Java Query
Library

27. Cassandra
Database
(Cassandra)

28. Cassandra
1.0, 1.1, 1.2 Compatible
No 2.0 yet

29. Cassandra
Experimented with CQL very early on
CQL 1.0 time frame

30. Cassandra
Experimented with CQL very early on
CQL 1.0 time frame

31. Cassandra
Astyanax now
Mostly happy with it
Connection pool implementation is
very sensitive to network bumps

32. Cassandra
Experimented with various compaction
strategies
No real winner
Leveldb bugs in 1.0 made it almost a
non-starter

33. Cassandra
CASSANDRA-5685
Per-CF TTLs
Doesn’t help us
Might help you

34. Cassandra
CASSANDRA-3974
TTL histogram used to give input on
which sstables are good candidates
for compaction (size-tiered only)

35. Cassandra
CASSANDRA-5228
Track max TTL per sstable to expire the
whole thing.
We could use this by using bucketed CFs

36. Anatomy of a Metric
One dimensional signal
Has an ID
We call this a locator
Mostly opaque
Tuple of (tenantId [,other things,…]

37. Anatomy of a Metric
Example:
6335,web01,ping,bytes

38. Anatomy of a Metric
Stuff whatever you want in there
Just don’t change it
It becomes a key

39. Anatomy of a Metric
Has a type associated with it:
long, double, string, boolean
Type determines on-disk
serialization

40. !
{!
"timestamp": 1319222001982,!
"monitoring_zone_id": "mzXXXXXXXX",!
"available": true,!
"status": "code=200,rt=0.257s,bytes=0",!
"metrics": {!
"bytes": {!
"type": "i",!
"data": "0"!
},!
"tt_firstbyte": {!
"type": "I",!
"data": "257"!
},!
"tt_connect": {!
"type": "I",!
"data": "128"!
},!
"code": {!
"type": "s",!
"data": "200"!
},!
"duration": {!
"type": "I",!
"data": "257"!
}!
}!
Example
}!

41. Anatomy of a Metric
Sometimes has units
Example: seconds, bytes, light
years
We guess on this

42. Column Families
Metrics
Full resolution
One per granularity (5m, 20m, 60m, 240m,
1440m)
One row per metric
Locator is the key

43. Column Families
Metrics
No Bucketing
Will be required for high frequency metrics
Solution is easy
Just complicates Locator resolution

44. Column Families
Metadata
One row per metric
Rollup State
Nasty map for tracking shard state
Active Metrics
Shard to list of locators

45. Column Families
STRING & BOOLEAN
Speshul
Only updated when values change
Plumbing keeps old values in
memory

46. Libraries
Java Ingestion
Library
Java Rollup
Library
Database
(Cassandra)
Java Query
Library

47. Ingestion LibrarY
insert_metrics(list<metric>)!

48. Ingestion LibrarY
update_state(shard, granularity, slot)!
SLOT == Bucket of time

49. Rollup LibrarY
get_active_locators(shard)!
get_state(shard, granularity, slot)!
get_metrics(from, to, locator, granularity)!
write_rollups(list<rollup>)!
update_state(shard, granularity, slot)!

50. Rollup LibrarY
Supports bulk operations outside
of the service
Enables tools to be written

51. Rollup LibrarY
Rollups contain
count, min, max, mean, variance
Serialization is versioned

52. Query LibrarY
get_data(form, to, granularity)!
get_data(from, to, num_points)!

53. Metadata & Cache
Metadata + Cache
State Management
Java Ingestion
Library
Java Rollup
Library
Database
(Cassandra)
Java Query
Library

54. Metadata & Cache
Integrated into services (ingestion
& rollup)
Backed by Cassandra
Supports different eviction
strategies based on needs

55. Metadata & Cache
Example 1:
TTLs are linked to tenants and are
not known when metrics are
ingested
A separate API must be consulted

56. Metadata & Cache
Example 2:
Units are valuable only at query
time, but are not included with
metrics
Heuristically guess and store these

57. Rollup Schedule Service
Metadata + Cache
Rollup Scheduler
State Management
Java Ingestion
Library
Java Rollup
Library
Database
(Cassandra)
Java Query
Library

58. Rollup Schedule Service
Problem:
Divide time into buckets without
scratching at infinity
Identify them using a finite set of keys

59. Rollup Schedule Service
Solution:
Order preserving consistent hashing
for timestamps

60. Rollup Schedule Service
Imagine a two week period divided
into slots the size of each granularity

61. Rollup Schedule Service
4032 5m slots
1008 20m slots
336 60m slots
84 240m slots
14 1440m slots

62. Rollup Schedule Service
Gives us a way of consistently
addressing and bucketing time ranges
As time increases, so does the slot it
hashes to (until it wraps to zero)

63. Rollup Schedule Service
When do we roll up?
Whenever an active slot
a) has not been updated in N seconds
b) is M seconds old

64. Rollup Schedule Service
What about late data?
Late data can be ingested
for 24 hours

65. Ingestion
Processors
Ingestion
Transform
Metadata + Cache
Rollup Scheduler
State Management
Java Ingestion
Library
Java Rollup
Library
Database
(Cassandra)
Java Query
Library

66. Ingestion
Processors
Every metric is not built the same way
They come from different places
Processors allow you to make them consistent
Can be synchronous or asynchronous

67. API Endpoints
Ingestion API
Ingestion
Transform
Query API
Metadata + Cache
Rollup Scheduler
State Management
Java Ingestion
Library
Java Rollup
Library
Database
(Cassandra)
Java Query
Library

68. API Endpoints
Why not ship it with API endpoints?
External forces

69. API Endpoints
Decided to make them
Modular

70. API Endpoints
We do ship reference API endpoints
UDP Ingestion
HTTP Ingestion
HTTP Query

71. API Endpoints
Downside?
More work for you

72. API Endpoints
Upside?
We ♥ Pull Requests

73. How Does It Scale?
Ingestion scales linearly
Add ingestion nodes until
Cassandra is the bottleneck

74. How Does It Scale?
Two ingestors per DC
Only one per DC is active
Double ingest

75. How Does It Scale?
Rollups scale [almost] linearly by
spreading out shard ownership
Shards are currently pegged at
128
Ok to have multiple nodes own a
shard
Zookeeper is a soft-dependency

76. Future Stuff
Local ingestion durability

77. Future Stuff
Richer metadata API
Example: tag metrics and then use
those tags as a query facet
Will require an index
Experimenting with ElasticSearch
Home-rolled bitmap indexes

78. Future Stuff
Pre-aggregated Metrics
Histograms (partially
implemented)
Counters, Timers, Gauges, Sets

79. Future Stuff
Deep statsd and graphite
integration (active work)
Statsd is hard because counts get
reset after a flush

80. Future Stuff
Graphite is just involved
(new rollup types)
Whisper DB interface
Then hack carbon to support it
Already pluggable, just needs
integration

81. Thanks!
http://blueflood.io
blueflood-discuss@googlegroups.com
Freenode: #blueflood
gitub.com/rackerlabs/blueflood
Twitter: @gdusbabek

82. Image Credits
All images for this presentation come from the Flickr commons collection
http://www.flickr.com/commons/
flood
guide
motivation
cows
jet
apartments
groups
lipstick
elephant
containers
anatomy
columns
library
cache money
railyard
processors
terminal
fish
future
thanks
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