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Time Series Data with Apache Cassandra

Date post: 15-Jan-2015
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Whether it's statistics, weather forecasting, astronomy, finance, or network management, time series data plays a critical role in analytics and forecasting. Unfortunately, while many tools exist for time series storage and analysis, few are able to scale past memory limits, or provide rich query and analytics capabilities outside what is necessary to produce simple plots; For those challenged by large volumes of data, there is much room for improvement. Apache Cassandra is a fully distributed second-generation database. Cassandra stores data in key-sorted order making it ideal for time series, and its high throughput and linear scalability make it well suited to very large data sets. This talk will cover some of the requirements and challenges of large scale time series storage and analysis. Cassandra data and query modeling for this use-case will be discussed, and Newts, an open source Cassandra-based time series store under development at The OpenNMS Group will be introduced.
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Time Series Data With Apache Cassandra Berlin Buzzwords May 27, 2014 Eric Evans [email protected] @jericevans
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Page 1: Time Series Data with Apache Cassandra

Time Series Data With Apache Cassandra

Berlin BuzzwordsMay 27, 2014

Eric [email protected]

@jericevans

Page 2: Time Series Data with Apache Cassandra

Open

Page 3: Time Series Data with Apache Cassandra

Open

Page 4: Time Series Data with Apache Cassandra

Open

Page 5: Time Series Data with Apache Cassandra

Open

Page 6: Time Series Data with Apache Cassandra

Network

Management

System

Page 7: Time Series Data with Apache Cassandra

OpenNMS: What It Is

● Network Management System○ Discovery and Provisioning○ Service monitoring○ Data collection○ Event management, notifications

● Java, open source, GPLv3● Since 1999

Page 8: Time Series Data with Apache Cassandra

Time series: RRDTool

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

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… and oh yeah, graphing

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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

Page 11: Time Series Data with Apache Cassandra
Page 12: Time Series Data with Apache Cassandra

Hmmm

We collect and write a great deal; We read (graph) relatively little.

So why are we aggregating everything?

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Also

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

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TIL

Metrics typically appear in groups that are accessed together.

Optimizing storage for grouped access is a great idea!

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What OpenNMS needs:

● High throughput● High availability● Late aggregation● Grouped storage/retrieval

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Cassandra

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

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SSTables

Writes

Commitlog

Memtable

SSTable

DiskMemory

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Write Properties

● Optimized for write throughput● Sorted on disk● Perfect for time series!

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Partitioning

A

B

C

Key: Apple

...

AZ

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Placement

A

B

C

Key: Apple

...

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Replication

A

B

C

Key: Apple

...

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CAP Theorem

Consistency

Availability

Partition tolerance

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Consistency

A

B

?

W=2

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Consistency

?

B

C

R=2

R+W > N

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Distribution Properties

● Symmetrical● Linearly scalable● Redundant● Highly available

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D ata odelM

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Data Modelresource

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Data Modelresource

T1 T2 T3

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Data Modelresource

T1

M1 M2

V1 V2

M3

V3

T2

M1 M2

V1 V2

M3

V3

T3

M1 M2

V1 V2

M3

V3

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Data Model

CREATE TABLE samples ( T timestamp,

M text,

V double,

resource text,

PRIMARY KEY(resource, T, M));

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Data model

V1T1 M1 V2T1 M2 T1 V3M3resource

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Data model

SELECT * FROM samplesWHERE resource = ‘resource’AND T = ‘T1’;

V1T1 M1 V2T1 M2 T1 V3M3resource

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Data model

T1 M1 V1resource

V1T1 M1 V2T1 M2 T1 V3M3resource

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Data model

T1 M1 V1

T1 M2 V2

resource

resource

V1T1 M1 V2T1 M2 T1 V3M3resource

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Data model

T1 M1 V1

T1 M2 V2

T1 M3 V3

resource

resource

resource

V1T1 M1 V2T1 M2 T1 V3M3resource

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Data model

SELECT * FROM samplesWHERE resource = ‘resource’AND T >= ‘T1’ AND T <= ‘T3’;

V1T1 M1 V1T2 M1 T3 V1M1resource

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Newts

● Standalone time series data-store● Raw sample storage and retrieval● Flexible aggregations (computed at read)

○ Rate (counter types)○ Functions pluggable○ Arbitrary calculations

● Cassandra-speed

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Newts

● Java API● REST interface● Apache licensed● Github (http://github.com/OpenNMS/newts)

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Fin


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