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Features

Just-in-time Compiled

JuliaDB leverages Julia’s just-in-time compiler (JIT) so that table operations – even custom ones – are fast.

Compute in Parallel

Process data in parallel or even calculate statistical models out-of-core through integration with OnlineStats.jl.

Store Any Data Type

JuliaDB supports Strings, Dates, Float64… and any other Julia data type, whether built-in or defined by you.

Fast UDFs

JuliaDB is written 100% in Julia. That means user-defined functions are JIT compiled.

Open Source

MIT License.

Fast CSV Parser

CSVs are loaded extremely fast. Many files can be read at the same time to create a single table.

Benchmarks

  • Reproducible benchmarks are available here.
  • A big data example (74GB of CSVs) is available here.

JuliaDB vs. Pandas

Parallel vs. Serial Processing

Compare

Feature comparison between time series packages

FeatureJuliaDBPandasxtsTimeArrays
Distributed Computing
Data larger than memory
Multiple Indexes
Index Type(s)AnyBuilt-insTimeTime
Value Type(s)AnyBuilt-insBuilt-insAny
Compiled UDFs

Talks

JuliaDB integrates with OnlineStats to provide scalable single pass algorithms (that can run in parallel) for statistics and modeling …
Aug 8, 2018 JuliaCon 2018, London
Video

JuliaDB is a pure Julia analytical database. It makes loading large datasets and playing with them easy and fast. JuliaDB needs to …
Aug 8, 2018 JuliaCon 2018, London
Video

Modern data analysis pipelines routinely involve gluing together multiple systems and languages: SQL, Python, R, C++, unix tools, and …
Oct 1, 2016 PyData NYC 2016, New York
Video

Resources

JuliaDB is a project of