Product Recommendations in RedisGraph, Part 3: Query Load Testing

This post is part of a series on leveraging RedisGraph for product recommendations.

In this post we’ll cover the script developed for concurrent load testing of product recommendation queries detailed in this post.

Concurrent RedisGraph

First let’s talk about Redis – it’s single threaded. End of story, go home, right? Well, sort of. RedisGraph is able to take advantage of the same techniques that other Redis Labs projects like RediSearch to speed up concurrent processing of requests.

Though commands/operations in Redis happen atomically, RedisGraph internally uses a thread pool to offload some of the linear algebra done to convert openCypher queries, engage in matrix multiplication on non-critical pathways, and (presumably) by frequently swapping contexts – resulting in greater performance overall and preventing long-running queries from generating large scale bottlenecks.

Queries in the Load Testing Tool

The tool written to strain and squeeze performance out of RedisGraph is the productRecommendationQueryRunner. The tool executes two queries; the first obtains the top 1,000 purchasers by default and then pumps their person ids into the second query; fetching all “products that share orders with products that user has ordered”.

match (p:person)-[:transact]->(o:order) return p.id, count(o) as orders order by orders desc limit 100

… returns a list of person-labeled node id property and pumps them into:

match (p:person { id: ${personId} })-[:transact]->(:order)-[:contain]->(prod:product)
match (prod)<-[:contain]-(:order)-[:contain]->(rec_prod:product)
where not (p)-[:transact]->(:order)-[:contain]->(rec_prod)
return rec_prod.id, recprod.name order by indegree(prod) desc

The resulting output is a bar showing progress and time to completion. One complete the tool will output the min, average, and max number of recommended products along with p50, p95, and p99 performance metrics.

Installation

This script requires the installation of Java 11 and Groovy. Dependencies are automatically pulled from Maven repositories based on the Grape’d annotations.

  • Java (11 OpenJDK)
  • Groovy 3
  • RedisGraph

Obtaining and Basic Usage

Pull down the productRecommendationQueryRunner script itself:

wget https://raw.githubusercontent.com/joshdurbin/redis-graph-commerce-poc/master/productRecommendationQueryRunner
chmod u+x productRecommendationQueryRunner

At this point, with RedisGraph available at localhost:6379, you can just run ./productRecommendationQueryRunner to load test against the graph.

./productRecommendationQueryRunner 
Progress 100% │███████████████████████████████████████████████████████████████████████████│ 1000/1000 (0:00:42 / 0:00:00) 
Found a min number of recommended products of 141, avg of 693, and a max of 1674 for 1000 with a query performance p50 230ms, p95 442ms, p99 531ms

RedisGraph concurrent performance during query

I’m running RedisGraph in docker and running ctop while the productRecommendationQueryRunner is running shows output like:

  ctop - 23:25:58 PDT   3 containers                                                                                                               

     NAME                  CID                   CPU                   MEM                   NET RX/TX             IO R/W                PIDS

   ◉  awesome_gauss         0aa2d5b45a8c                    0%               115M / 1.94G     3M / 12M              0B / 0B               10       
   ◉  festive_greider       b4b196d21e40                   520%               42M / 1.94G     1M / 46M              0B / 0B               14
   ◉  pedantic_colden       ac816b00fbe7                    0%                1M / 1.94G      0B / 0B               0B / 0B               1

…showing that during the test RedisGraph (identified by the docker instance name festive_greider) was able to easily leverage more than CPU core on my laptop.

Advanced Usage

There are many fewer knobs to spin here compared to the graph generation tooling, but nonetheless there are a few. Like the generation script, the list of options can be output requesting the help menu ./productRecommendationQueryRunner --help:

./productRecommendationQueryRunner --help
usage: productRecommendationQueryRunner <args>
Concurrent RedisGraph Query Runner
 -db,--database <arg>        The RedisGraph database to use for the query [defaults to prodrec]
 -h,--help                   Usage Information
 -l,--limitResults <arg>     The default results limit.
 -rh,--redisHost <arg>       The host of the Redis instance with the RedisGraph module installed to use for graph creation. [defaults to localhost]
 -rp,--redisPort <arg>       The port of the Redis instance with the RedisGraph module installed to use for graph creation. [defaults to 6379]
 -tc,--threadCount <arg>     The thread count to use [defaults to 6]
 -tp,--topPurchasers <arg>   The number of top purchasers to query for [defaults to 1000]

Query Tweaks

This tool is written to stress the Redis Graph instance and thus it returns a great amount of data – more than you’d probably really want in real life. There are some obvious optimizations / changes that you’d make if you were running a query like this in real life – like returning only the top N products from the recommendation query instead of all.

Next up

The next post will be a bit more detailed on performance where RedisGraph will be provisioned on cloud compute infrastructure and load tested along with various input parameters to the generateCommerceGraph and productRecommendationQueryRunner. Stay tuned!

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