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High performance, low latency, reactive processing framework

Chronicle Datagrid can control and manage access to your data and your services across a cluster of machines.

What is Chronicle Datagrid?

Chronicle Datagrid is a high performance, low latency, access gateway library. It can be used to control and manage access to your data and your services across a cluster of machines. In microservices terms, it is an API Gateway. Chronicle Datagrid runs embedded as an existing system, and as a container for the most performance critical part of your server application.

Chronicle Datagrid provides:

  • managed thread pools for processing events
  • persisted and distributed queues and maps
  • clustered configuration

Chronicle Datagrid can handle millions of events per second; with latencies of around one microsecond on a single machine, and below 25 microseconds across multiple machines.

More information

GitHub: Source Code

Source Code Documentation: JavaDoc

StackOverflow: Questions

Chronicle Datagrid Features

Low latency, durable, interprocess communication (IPC)

No data loss; stores every value instead of replacing them

No GC as everything is done off-heap

Persistence to disk through memory mapped files

IPC between Java processes or between threads in a Java process

Simple API for ease of use

Replay-ability of all your inputs and outputs

Concurrent writers across processes on the same machine

Concurrent readers across machines on your network using TCP replication

Embedded performance in all processes

All data stored off-heap, to reduce GC impact

Data is written synchronously to ensure no data is lost

Data is read less than a microsecond after it is written

Common Chronicle Datagrid Use Cases

There are many ways in which Chronicle Datagrid can be put to use. We've tried to bring to life some common ways in which your business can use the powerful Chronicle Datagrid.

Use Case: Data Grid and Microservices API Gateway

Chronicle Datagrid can access your back end services with a minimum of overhead and provide monitoring and control over which data and services a client can access.

The benefits are:

  • Chronicle Datagrid can tap into your services with a minimum of interruption, access the queues and maps that they use directly.
  • Abstraction of the underlying data storage supports changes in schema, and data storage solution.
  • Access control down to the key-level for maps, and topic-level for queues.
  • Present a single view for a distributed microservice architecture, for monitoring and control purposes.

Use Case: Database Cache for Reads and Writes

Chronicle Datagrid can access cached data at “in-memory” speeds, while keeping a copy on disk.

The benefits are:

  • Removes your database from the critical path, for reads and writes.
  • Persists writes locally, and then spooled to your database as fast as it allows.
  • Test your system, independently of the state of a database, in a deterministic and reproducible manner.
  • Add in-Java triggers for changes.
  • Record all changes which can then be replayed for testing purposes.

Use Case: Share Data Between JVMs at Bare Metal Speed

Chronicle Datagrid can share the same data between processes on the same machine, with only one copy in-memory.  Across machines it replicates the changes, and reconciles the differences automatically.

The benefits are:

  • Read/Write speeds of less than 10 ns for data which changes rarely.
  • Read/Write speeds of less than 60 ns for data which changes often using zero-copy data storage.
  • Access data between processes at the same speed as in-process.
  • Use data which is persisted to the disk cache synchronously at the same speed as in-memory.
  • Restart your caches in under 10 ms; even for hundreds of GB of data.

Use Case: Monitor Existing Applications

Chronicle Datagrid can replace your existing logging and map storage. This not only speeds up these operations but also makes them immediately available to other processes.

The benefits are:

  • Read/Write speeds of less than 10 ns for data which changes rarely.
  • Read/Write speeds of less than 60 ns for data which changes often through zero-copy data storage.
  • Access data between processes at the same speed as in-process.
  • Use data which is persisted to the disk cache synchronously at the same speed as in-memory.
  • Restart your caches in under 10 ms, even for hundreds of GB of data.

Use Case: Low Latency Event Driven System – Algorithmic Trading or Pricing System

Chronicle Datagrid can manage the relationships between critical threads in your system. Being able to reply the precise inputs with original timings allows you to have a highly deterministic system.

The benefits are:

  • Faithful reproduction of behaviour that is seen in production, to reproduce exotic errors and have confidence that you have fixed them.
  • Low latency end-to-end solution; as low as 25 microseconds tick-to-trade.
  • Ultra low GC infrastructure allows you to run all day without a minor or major collection, eliminating a key source of jitter.

Use Case: Graphical User Interfaces sharing larger, faster data sets

Distributing fast data to many clients can put a strain on your network. High numbers of rows in tables and high update rates can also slow down your client.

The benefits are:

  • You can throttle the rate of updates sent over UDP/TCP so that, while the server might be updating at rates of millions per second, the client will receive the latest update (constrained by your network latency).
  • Your clients can hold more data in a scalable way. All the data is stored off-heap and so has no impact on your garbage collector (GC) pause times.  This allows you to scroll around tables with millions of rows without GC pauses. Using off-heap storage you can reduce your heap requirement by 80%.

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