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Alex Petrov
Scalable time-series applications with Cassandra
Scalable Time Series
with Cassandra
@ifesdjeen
Cyanite
past, present and future
Requirements
Throughput
Incoming data
Aggregates
Read queries
Scalability
Paths / metrics
Historical data
Readers / writers
vs raw
Aggregated
Predefined list of reports to aggregate
Reducing amount of data points
Faster queries
Precision loss due to aggregation
“servers.*.workers.busyWorkers”: “10s:7d,1m:21d,15m:5y”
Graphite
Store numeric time-series data
Render graphs of this data on demand
https://graphiteapp.org/
Ecosystem
carbon: listens for time-series data
whisper: DB for storing TS data
graphite-web: UI & API for graphs
Downsides
Single-host solution
Plenty of disk seeks
Optimised for space
Sharding / replication is “manual”
Scaling
Cluster topology stored on every node
Manual replication
Changing cluster topology non-trivial
Scaling
Stateless service
Automatic shard assignment
Replication
Easy management
Easy topology changes
reminds you of anything?
Cassandra
perfect match
Cyanite
Stateless
Async I/O
Custom scheduler
No Whisper files
Distributed
Horizontally scalable
Cyanite responsibilities
Carbon-compatible listener
Aggregate data in-memory
Flush aggregates to Cassandra
Path storage
Retrieve paths for query
Aggregate the query results
Cassandra features
Metric expiry, TTL
User Defined Types
SASI Indexes, LIKE queries
Aggregate Functions
Offline data loading
Clustering range queries
IN partition key locking
Globbing
index
app.cluster.server.subsystem.metric
Built with SASI indexes
Fast, scalable queries
Optimised for glob
CREATE TABLE segment (
parent text,
segment text,
pos int,
length int,
leaf boolean,
PRIMARY KEY (parent, segment))
app.cluster.server.subsystem.metric
SELECT * FROM segments WHERE
parent = 'root' AND
pos = 1
Wildcard: *
SELECT * FROM segments WHERE
pos = 4
Postfix: *.*.*.*.metric
SELECT * FROM segments WHERE
parent = 'app' AND
pos = 2 ALLOW FILTERING
Prefix: app.*
SELECT * FROM SEGMENTS
WHERE
pos = 3 AND
segment LIKE 'abc.%'
ALLOW FILTERING
Suffix: abc.*.metric
engine
Query
2-Step transformations:
Inner
Outer
Cross metric
engineQuery
a.b.c1 a.b.c2
scale(a.b.*, 10.0)
{"a.b.c1" [1 2 3]
"a.b.c2" [5 6 7]}
{"a.b.c1" [10 20 30]
"a.b.c2" [50 60 70]}
a.b.c
derivative(a.b.c)
{"a.b.c" [1 3 6]}
{“derivative(a.b.c)" [nil 2 3]}
a.b.c1 a.b.c2
sumSeries(a.b.c1,a.b.c2)
{"a.b.c1" [1 1 1]
"a.b.c2" [2 2 2]}
{“sumSeries(a.b.c,a.b.d)" [3 3 3]}
model
Data
CREATE TYPE IF NOT EXISTS metric_resolution (
precision int,
period int
);
CREATE TYPE IF NOT EXISTS metric_id (
path text,
resolution frozen<metric_resolution>
);
CREATE TYPE IF NOT EXISTS metric_point (
max double,
mean double,
min double,
sum double
);
Cassandra
Aggregates
Cassandra aggregates
Locked partition
Flexible query definition
Local aggregation
Reduced raw data chatter
CREATE OR REPLACE FUNCTION sumState
(state map<bigint, double>, ts bigint, val metric_point)
CALLED ON NULL INPUT RETURNS map<bigint, double>
LANGUAGE java AS $$
if (state.containsKey(ts)) {
state.put(ts, state.get(ts) + val.getDouble("mean"));
} else {
state.put(ts, val.getDouble("mean"));
}
return state;
$$;
CREATE OR REPLACE FUNCTION sumFinal
(state map<bigint, double>)
CALLED ON NULL INPUT RETURNS map<bigint, double>
LANGUAGE java AS
'return state;';
SELECT sum(time, point) FROM metric WHERE id
IN ({path: 'a', resolution: {precision: 1, period: 3600}},
{path: 'b', resolution: {precision: 1, period: 3600}})
AND time > 0
AND time < 5;
Scaling
Scaling cyanite
Use DTSC
Cyanite is stateless
For high loads, split readers and writers
Load-balance with HAProxy
Colocate Cyanite & Cassandra
soon
Coming
P-Square histograms
T-Digest quantiles
Gorilla Compression
Cassandra aggregates
Custom glob indexes
Scheduler
soonComing
Kafka ingester
Statsd protocol support
Standalone cyanite
Dynamic thresholds
soonComing
Building a Fast, Resilient Time Series Store with Cassandra (Alex Petrov, DataStax) | C* Summit 2016

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