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neo4j-arrow 🏹 -- Graph Data Science Workflows Accelerated

“When you want to do something, do it right away. Do it when you can. It’s the only way to live a life without regrets.” -- Sonic The Hedgehog

gotta-go-fast

tl;dr: neo4j-arrow in a nutshell 🥜

This project aims to step outside of traditional ways of integrating with Neo4j and expose things like the in-memory GDS graph projection via high-performance Arrow Flight APIs for streaming nodes, relationships, and their properties.

neo4j-arrow has been shown to:

  • ⏩ Move data out of Neo4j 20x faster than the Neo4j Java Driver
  • ⏩ Move data out of Neo4j 450x faster than the Neo4j Python Driver
  • 🏎️ Facilitate live-migration of GDS graph projections, moving 50m nodes, 200m relationship, and 50m 256-degree feature vectors between live Neo4j instances in different GCP regions in under 15 minutes.
  • Provide a faster integration with BigQuery, extracting and loading 50m nodes with 256-degree feature vector properties in under 2 minutes.
  • 😃 Make you happier and more at peace.

Building and Deploying 🛠️

Most users will want to use the plugin form of neo4j-arrow. The most recent tagged version is available on the releases page.

To build your own from source, you'll need:

  • A JDK 11 distribution
  • Internet access to the public Maven repo

Simply run: $ ./gradlew plugin:shadowJar

You should end up with a Neo4j plugin jar in ./plugin/build/libs/ that you can drop into a Neo4j system.

Given the plugin is the heart and soul of current development, I have plans to redesign the build system to make it the default project and target.

Plugin Operation 🔌

neo4j-arrow should work out of the box with Neo4j 4.3 and GDS v1.7. If you have earlier versions of either, you'll need to compile your own jars from source.

The neo4j_arrow.py PyArrow client requires Python 3 and PyArrow 6.0.0. (It may still work with v5.0.0, but I'm developing on v6.0.x.)

Any other Arrow Flight clients should use v6.0.0 of Arrow/Arrow Flight if possible.

Configuration ✔️

All configuration for neo4j-arrow is performed via environment variables. (Currently there is no support for property-based config in neo4j.conf.) This holds true for all subprojects of neo4j-arrow.

Available configuration options specific to the neo4j-arrow plugin:

Option Description Default
HOST Hostname or IP for neo4j-arrow to listen on "localhost"
PORT TCP Port for neo4j-arrow to listen on 9999
MAX_MEM_GLOBAL Global memory limit for Arrow memory allocator Long.MAX_VALUE
MAX_MEM_STREAM Per-stream memory limit Long.MAX_VALUE
ARROW_BATCH_SIZE Max number of rows to include when sending a vector batch to a client 1000
ARROW_MAX_PARTITIONS Max number of partitions to create when generating streams, roughly translates to # of CPU cores to utilize max(vcpus - 2, 1)
ARROW_FLUSH_TIMEOUT Max time (in seconds) to wait before flushing a stream to the client 1800
ARROW_TLS_CERTIFICATE Path to x.509 full-chain certifcate in PEM format ""
ARROW_TLS_PRIVATE_KEY Path to PEM private key file ""

See also: the org.neo4j.arrow.Config class.

Performance Tuning 🔧

The primary knobs available for tuning for read or write performance is the ARROW_BATCH_SIZE and ARROW_MAX_PARTITIONS value. Given the partition number defaults to a value based on the host cpu core count, in practice this means tuning the batch size.

Some general advice:

  • Reads tend to perform better with smaller batch sizes (such as the default)
  • Writes benefit from much larger (150-200x than the default) values.

In practice, the true performance will depend heavily on the type of vectors being created. Scalars generally outperform List-based vectors for multiple reasons: total buffer size and cpu instructions required to read/write a value.

As of v3.something, some jobs let you override the partition and batch size dynamically by adding extra parameters to the job message. It's experimental, so you'll need to look at the Python wrapper code (neo4j_arrow.py) to see what works so far.

The neo4j-arrow Protocol/Lifecycle 🗣️

neo4j-arrow uses the Arrow Flight RPC framework to expose read/write operations to Neo4j. While you can implement the protocol yourself using an Arrow/Arrow Flight client implementation in any language, a helpful wrapper using PyArrow is provided in python/neo4j_arrow.py.

The general lifecycle looks like:

  1. Create an Arrow client with authentication details for the Neo4j system, optionally using TLS.
  2. Submit a valid neo4j-arrow Job to the service. Current jobs include:
    • Cypher Read jobs (cypher.read)
    • GDS Read jobs (gds.read) with ability to specify nodes or relationships
    • GDS Write Jobs (gds.write.nodes & gds.write.relationships) for creating GDS graphs from Arrow vectors/tables
    • KHop Jobs (khop) (see KHOP.md for details)
    • Bulk Neo4j database import (import.bulk) support
    • Job Status reporting (job.status)
    • Server version inspection (info.version)
    • System-wide job status inspection (info.jobs)
  3. Take the given ticket returned by the server for the Job and either request a readable stream or submit a stream via a "put" operation.

In practice, the nuances of jobs, tickets, etc. are handled for you if you use the provided Python wrapper.

Using Python 🐍

Assuming you've using neo4j_arrow.py, a simple GDS read operation to request and stream computed node embeddings (for example) looks like:

import neo4j_arrow as na

# using the neo4j user and 'password' as the password
client = na.Neo4jArrow('neo4j', 'password', ('neo4-host', 9999))

# stream the 'mygraph' projection and include the 'fastRp' node properties
ticket = client.gds_nodes('mygraph', properties=['fastRp'])

# get a PyArrow Table from the stream, reading it all into the client
table = client.stream(ticket).read_all()

# convert to a Pandas dataframe and go have fun!
df = table.to_pandas()

Some Examples 🧑‍🏫

A few IPython notebooks are provided that demonstrate more complex usage of neo4j-arrow via PyArrow:

  1. Basic usage and lifecycle.
  2. Live migration of a graph between disparate Neo4j instances.
  3. Bulk import of a new Database showing how to bootstrap a new Neo4j database remotely using neo4j-arrow and PyArrow or Pandas compatible data sources.

Some code examples:

Known Issues or Incomplete Things ⚠️

Keep in mind neo4j-arrow is a work in progress. The following are areas still requiring some development cycles or help.

  • Cannot currently write relationship properties to a GDS graph.
    • On the todo list.
  • Cannot read string properties (not counting Labels or Relationship Types) from a GDS graph.
    • GDS doesn't natively support strings and reads them from the db.
    • This could be implemented, but may slow down streaming.
  • Only basic Cypher types are supported for cypher jobs (mostly scalars, Strings, and simple Lists of scalars).
    • The cardinality of the set of types Cypher supports is too damn high.
    • What, you don't like numbers? :-)
  • Error handling and cleanup for failed/incomplete jobs needs work.
  • Batch sizes are not dynamically chosen. Ideally they would be determined based on schema and host hardware.
    • As we develop and utilize neo4j-arrow a best practice should be baked in, but we're not there yet.
  • Cannot currently write to the database via Cypher jobs.
    • Requires more complex Cypher transaction jobs.
    • Not out of the realm of possibility, but not a priority.
  • GDS write jobs report completion to the client before completion of server-side processing. Client's may start writing relationships while nodes are still being post-processed, resulting in a "graph not found" error.
    • On the todo list.

Licensing & Copyright

Like other neo4j-labs and neo4j-contrib projects, this project is provided under the terms of the Apache 2.0 license.

All files and code are copyright 2022, Neo4j, Inc.