EventQL

EventQL is a distributed, columnar database built for large-scale data collection
and analytics workloads. It can handle a large volume of streaming writes and
runs super-fast SQL and MapReduce queries.

More information:
Documentation,
Download,
Architecture,
Getting Started

Features

This is a quick run-through of EventQL's key features to get you excited. For
more detailed information on these topics and their caveats you are kindly
referred to the documentation.

• Automatic partitioning. Tables are transparently split into partitions using
  a primary key and distributed among many machines. You don't have to configure
  the number of shards upfront. Just insert your data and EventQL handles the rest.

• Idempotent writes. Supports primary-key based INSERT, UPSERT and DELETE
  operations. You can use the UPSERT operation for easy exactly-once ingestion
  from streaming sources.

• Compact, columnar storage. The columnar storage engine allows EventQL to
  drastically reduce its I/O footprint and execute analytical queries orders of
  magnitude faster than row-oriented systems.

• Standard SQL support. (Almost) complete SQL 2009 support. (It does JOINs!)
  Queries are also automatically parallelized and executed on many machines in
  parallel

• Scales to petabytes. EventQL distributes all table partitions and queries
  among a number of equally privileged servers. Given enough machines you can store
  and query thousands if terrabytes of data in a single table.

• Streaming, low-latency operations. You don't have to batch-load data
  into EventQL - it can handle large volumes of streaming insert and update
  operations. All mutations are immediately visible and minimal SQL query latency
  is ~0.1ms.

• Timeseries and relational data. The automatic partitioning supports
  timeseries as well as relational and key value data, as long as there is a good
  primary key. The storage engine also supports REPEATED and RECORD types so
  arbitrary JSON objects can be inserted into rows.

• HTTP API. The HTTP API allows you to use query results in any application
  and easily send data from any application or device. EventQL also supports a
  native TCP-based protocol.

• Fast range scans. Table partitions in EventQL are ordered and have a
  defined keyrange, so you can perform efficient range scans on parts of the
  keyspace.

• Hardware efficient. EventQL is implemented in modern C++ and tries to
  achieve maximal performance on commodity hardware by using vectorized execution
  and SSE instructions.

• Highly Available. The shared-nothing architecture of EventQL is highly
  fault tolerant. A cluster consists of many, equally privileged nodes
  and has no single point of failure.

• Self-contained. You can set up a new cluster in minutes. The EventQL server
  ships as a single binary and has no external dependencies except Zookeeper or a
  similar coordination service.

Use Cases

Here are a few example scenarios that are particularly well suited to EventQL's
design:

• Storage and analysis of streaming event, timeseries or relational data
• High volume event and sensor data logging
• Joining and correlating of timeseries data with relational tables

Non-goals

Note that EventQL is built around specific design choices that make it an
excellent fit for real-time data analytics processing (OLAP) tasks, but also
mean it's not well suited for most transactional (OLTP) workloads.

Build

Before we can start we need to install some build dependencies. Currently
you need a modern c++ compiler, libz, autotools and python (for spidermonkey/mozbuild)

# Ubuntu
$ apt-get install clang make automake autoconf libtool zlib1g-dev

# OSX
$ brew install automake autoconf libtool

To build EventQL from a distribution tarball:

$ ./configure
$ make
$ sudo make install

To build EventQL from a git checkout:

$ git clone git@github.com:eventql/eventql.git
$ cd eventql
$ ./autogen.sh
$ ./configure
$ make V=1
$ src/evql -h

To run the full (world) test suite:

$ make test

To run the quick (smoke) test suite:

$ make smoketest