gorilla4j

Implementation of time series compression method based on the Facebook Gorilla paper

License

License

GroupId

GroupId

com.jarslab.ts
ArtifactId

ArtifactId

gorilla4j
Last Version

Last Version

0.4
Release Date

Release Date

Type

Type

jar
Description

Description

gorilla4j
Implementation of time series compression method based on the Facebook Gorilla paper
Project URL

Project URL

https://github.com/milpol/gorilla4j
Source Code Management

Source Code Management

https://github.com/milpol/gorilla4j

Download gorilla4j

How to add to project

<!-- https://jarcasting.com/artifacts/com.jarslab.ts/gorilla4j/ -->
<dependency>
    <groupId>com.jarslab.ts</groupId>
    <artifactId>gorilla4j</artifactId>
    <version>0.4</version>
</dependency>
// https://jarcasting.com/artifacts/com.jarslab.ts/gorilla4j/
implementation 'com.jarslab.ts:gorilla4j:0.4'
// https://jarcasting.com/artifacts/com.jarslab.ts/gorilla4j/
implementation ("com.jarslab.ts:gorilla4j:0.4")
'com.jarslab.ts:gorilla4j:jar:0.4'
<dependency org="com.jarslab.ts" name="gorilla4j" rev="0.4">
  <artifact name="gorilla4j" type="jar" />
</dependency>
@Grapes(
@Grab(group='com.jarslab.ts', module='gorilla4j', version='0.4')
)
libraryDependencies += "com.jarslab.ts" % "gorilla4j" % "0.4"
[com.jarslab.ts/gorilla4j "0.4"]

Dependencies

test (2)

Group / Artifact Type Version
junit : junit jar 4.13.1
org.assertj : assertj-core jar 3.18.1

Project Modules

There are no modules declared in this project.

Travis CI Maven Central

What is all about

It is all about storing data in a efficient way.

Stop! two things. First: it is not about any data, but a very special kind: time series. Sounds scary but all in all it is just a value (numerical) in time (epoch). Second: but they said that storage is cheap! Well, so the bubble gum, it is just a buck. Million packs do the million bucks though. Also, what they don't say that we store enormous load of data which we write once and read once never.

Give me the numbers

As mentioned, we are considering here a time series data (value in time). Let's say we want to store stock price valuation of single company, single day, sampled every 10 second. 8 hours gives 2880 samples, sample is a time (Java long, 8 bytes) and a value (Java double, 8 bytes). Math is simple:

8 * 60 * 6 * 16 = 46080B = 45KB Phew. That's nothing you'll say. Sure, the bubble gum is just a buck, blah, blah... How about Gorilla format, can it do any better?

From ad-hoc test:

~8465B ~= 8,3KB (We could compare that to JSON format... but it would not make any sense.) Just to be clear: we are talking about exact same data, no rounding or data losses, but... Well, in wise algorithms there is almost always but, the one here is how the data is distributed.

But how?

All answers and technical guts can be found in great paper from the Facebook engineers Gorilla: A Fast, Scalable, In-Memory Time Series Database

Usage

Maven coords

<dependency>
  <groupId>com.jarslab.ts</groupId>
  <artifactId>gorilla4j</artifactId>
  <version>0.4</version>
</dependency>

Examples

Building basic Gorilla block

TSG tsg = new TSG(1546300800, new OutBitSet());
tsg.put(1546300800, 4.0);
tsg.put(1546300860, 4.1);
tsg.put(1546300920, 4.2);
tsg.close(); // at this point no more points are accepted

Dump block and re-create

TSG tsg = new TSG(1546300800, new OutBitSet());
tsg.put(1546300800, 4.2);
byte[] tsgBytes = tsg.toBytes();
TSG recreatedTsg = TSG.fromBytes(tsgBytes); // block is still open and can accept points

Extract iterator from block

TSG tsg = new TSG(1546300800, new OutBitSet());
tsg.put(1546300800, 4.2);
Iterator<DataPoint> tsgIterator = tsg.toIterator(); // iterator works on copied bytes, tsg accepts points

Open block in iterator

TSG tsg = new TSG(1546300800, new OutBitSet());
tsg.put(1546300800, 4.2);
tsg.close();
byte[] tsgBytes = tsg.getDataBytes();
Iterator<DataPoint> tsgIterator = new TSGIterator(new InBitSet(tsgBytes));

Other Java implementation?

Please check excellent Michael Burman implementation: gorilla-tsc.

Changelog

0.4

  • Bump test libs

0.2

  • Use long for time values (start and current).
  • Move DataPoint to abstraction
  • Add JavaDocs

Versions

Version
0.4
0.2
0.1