EventQL is a distributed, columnar database built for real-time data collection and analytics workloads. It can handle a large volume of streaming writes and runs super-fast SQL and MapReduce queries. http://eventql.io/
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.
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
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
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
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.
Here are a few example scenarios that are particularly well suited to EventQL's
- 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
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.
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 firstname.lastname@example.org: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