NetworkXum

NetworkXum is NetworkX-like interface for large persistent graphs stored inside DBMS. This lets you upscale from Megabyte-Gigabyte graphs to Terabyte-Petabyte graphs (that won't fit into RAM), without changing your code. We provide wrappers for following DBs:

• MongoDB - modern (yet mature) distributed document DB with good performance in most workloads
• Neo4J - disturbingly slow and unstable DBMS positioned as the go-to Graph database,
• SQLite3 - ubiquitous compact relational DBMS with basic SQL support,
• PostgreSQL - most feature-rich open-source relational DB,
• MySQL - the most commonly-used relational DB.

Project Structure

• networkxum - Python wrappers for Graph (Network) datastructures backed by persistent DBs.
• benchmarks - benchmarking tools and performance results.
• assets - tiny datasets for testing purposes.
• regexum - Python wrappers for search-able containers backed by persistent DBs.
• benchmarks - benchmarking tools and performance results.
• assets - tiny datasets for testing purposes.

Implementation Details & Included DBs

Some common databases have licences that prohibit sharing of benchmark results, so they were excluded from comparisons.

Name	Purpose	Implementation Language	Lines of Code (in /src/)
MongoDB	Documents	C++	3'900'000
Postgre	Tables	C	1'300'000
Neo4J	Graphs	Java	800'000
ElasticSearch	Text	Java	730'000
Unum	Graphs, Table, Text	C++	80'000

MongoDB

• A distributed ACID document store.
• Internally uses the BSON binary format.
• Very popular open-source project backed by the $MDB publicly traded company.
• Provides bindings for most programming languages (including PyMongo for Python).

ElasticSearch

• Java-based document store built on top of Lucene text index.
• Widely considered high-performance solutions due to the lack of competition.
• Lucene was ported to multiple languages including projects like: CLucene and LucenePlusPlus.
• Very popular open-source project backed by the $ESTC publicly traded company.

SQLite3

• Embedded tabular single-file SQL database with an extreme level of adoption.
• Provides a more direct C API in addition to the SQL interface.
• We use SQLAlchemy for Object-Relational-Mapping, which is by far the most common Python ORM tool.
• We overwrite the page size, Write-Ahead-Log format and concurrency settings for higher performance.

Postgre, MySQL and other SQLs

• Most common open-source SQL databases.
• Work well in single-node environment, but scale poorly out of the box.
• Mostly store search indexes in a form of a B-Tree. They generally provide good read performance, but are slow to update.

Neo4J

• The best known graph database with over 10 year of history.
• Instead of SQL provides Cyper DSL for queries, which are transmitted using Bolt protocol.
• Some of the essential indexing capabilities are not availiable in the free version.
• There are some compatiability issues between API versions 3.5 and 4.
• In our experience, Neo4J is extremely unstable and doesn't scale beyond tiny datasets. Generally crashes due to Java VM heap management issues.

TODO

• Benchmark on small & mid graphs.
• Session management in SQL and Neo4J.
• Duration constraints for benchmarks.
• Mixed Multithreaded Read/Write benchmarks.