Skip to content

rchapin/go-in-mem-datastore

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

3 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Go In-Memory Datastore

Overview

An in-memory data store that is also persisted to disk.

alt text for screen readers

Supports concurrent Get(key string) and Put(key string, val map[string]interface{}).

All reads are directly from an in-memory map[string]interface{} data store. The test records are a 100 field Avro record with 50 doubles and 50 strings. Each record is approximately 102800 bytes. Since most of the data is strings, in order to easily "bulk" up the test payloads, as it does not gain as much benefit from the default avro encoding.

For each incoming record, the writer first validates that this is the most recent record for the given key in the in-memory store before overwriting they key. Otherwise, it is possible that we might overwrite the value in the cache with an older record as we cannot guarantee the ordering of writes to the cache.

All writes are persisted to disk at the time of write whether or not they are the most recent value. In order to increase performance that data store is split into a configurable number of shards. Further, once the write to the in-memory shard is complete and the mutex unlocked the incoming data is written to a channel. That channel is read by multiple Persister go routines that each write to separate files to parallelize I/O operations.

It should be possible to use this as-is in an embedded environment as long as it is connected to a network and can ship the data files to another store for analytics.

To Dos

  1. Implement cache population with existing data on start-up. Currently, it does not yet load the persisted data from disk on start-up. The plan is to implement the functionality such that the cache is pre-populated with the most recent record from each key on start-up by reading all of the data in the output directory.

  2. Rotate the output files based on size or number of records. The Persisters do not yet do any file rotation.

Performance

There is nothing particularly complicated about the program and it should not require any special hardware. The following stats were gleaned from running on the following system:

  • Intel(R) Core(TM) i7 CPU 920 @ 2.67GHz
  • 24GB RAM
  • 1TB WDC WD1003FZEX-0, 7200RPM a quick test of the disk indicates about 2.3GB/s

With the following test:

  • 40 writers, each writing 2400 records at 1 record per millisecond
  • 40 readers, each reading every 2 milliseconds

Writes/s=2232.558140, Reads/s=18090.348837

I would like to see this data as a histogram to see the percentile distribution but just went with the simple thing first to get an idea of how it would perform.

Running the Tests

The tests include a performance test that will require about 3.2GB of RAM and write approximately 10GB to /var/tmp/inmemdatastore-inttest dir.*

Currently, it will leave the data in the /var/tmp/inmemdatastore-inttest for analysis.

  1. Change dirs into the integration_tests dir and execute the tests.
    cd integration_tests/ && INTEGRATION=1 go test -count=1 -v ./...
    

About

A Go in-memory, key/value data store library that also persists the data to disk to survive restarts, reboots, and crashes

Resources

License

Stars

Watchers

Forks

Packages

No packages published

Languages