Performance of Phrag

Load Test

Tests are performed with k6.

Objectives

Load tests were performed to:

• Get reference numbers of performance for simple resource setups. (Stringent JVM/OS/DB tuning is excluded from the scope.)

• Verify performance improves linear with additional resources, which means there's no obvious bottleneck.

• Compare performance of resolver models between bucket queue model vs recursion model.

Measurement

How many users can be served under request duration of 500ms while each user is constantly sending a request every 2s?

• Duration: 60s with 2 stages:
  • 30s of ramping up to a target number of users.
  • 30s of staying at a target number of users.
• Metrics used is http_req_duration for p95.
• HTTP error & response error rate must be less than 1%.

Tests

• 3 queries with different nest levels were tested to see performance difference of additional queries and serialization (1 nest = 1 SQL).

• Each test was performed with limit: 50 and limit: 100 to see performance difference of overall serialization workload.

• All tables had 100,000+ pre-generated records as performance tests in small number of records often do not represent performance accurately.

• Parameters of $offset and $id_gt were randomized between 0 to 100,000 for each request.

Query without nest: simple object listing

query queryVenues($limit: Int!, $offset: Int!, $id_gt: Int!) {
  venues(limit: $limit, offset: $offset, where: { id: { gt: $id_gt } }) {
    id
    name
    postal_code
  }
}

Query with 1 nest: has-many

query queryVenueMeetups($limit: Int!, $offset: Int!, $id_gt: Int!) {
  venues(limit: $limit, offset: $offset, where: { id: { gt: $id_gt } }) {
    id
    name
    postal_code
    meetups(limit: $limit, sort: { id: desc }) {
      id
      title
    }
  }
}

Query with 2 nests: has-many and has-one (often referred as many-to-many)

query queryMeetupsWithMembers($limit: Int!, $offset: Int!, $id_gt: Int!) {
  meetups(limit: $limit, offset: $offset, where: { id: { gt: $id_gt } }) {
    id
    title
    meetups_members(limit: $limit) {
      member {
        id
        email
      }
    }
  }
}

Results

• Platform: AWS ECS Single Task Container
• Database: AWS RDS PostgreSQL 2vCPU + 1GB RAM (Free-tier db.t3.micro)

1vCPU + 4GB RAM

Limit: 50

Query	MAX VUs	p(95)	Min	Max	Reqs	Reqs/s
No nest	1300	427ms	7ms	845ms	27510	442
1 nest	700	461ms	9ms	860ms	14750	237
2 nests	500	326ms	8ms	758ms	10919	175

Limit: 100

Query	MAX VUs	p(95)	Min	Max	Reqs	Reqs/s
No nest	900	395ms	8ms	895ms	19515	313
1 nest	400	257ms	10ms	703ms	8872	143
2 nests	300	274ms	10ms	599ms	6602	106

2vCPU + 8GB RAM

Limit: 50

Query	MAX VUs	p(95)	Min	Max	Reqs	Reqs/s
No nest	1900	353ms	6ms	697ms	41174	662
1 nest	1400	365ms	7ms	721ms	29668	477
2 nests	1200	447ms	8ms	1069ms	25088	402

Limit: 100

Query	MAX VUs	p(95)	Min	Max	Reqs	Reqs/s
No nest	2000	489ms	7ms	846ms	41693	669
1 nest	900	291ms	10ms	663ms	19695	316
2 nests	700	294ms	9ms	774ms	15312	246

Observations

• Resource allocation: Performance seems to have increased linear with the resource allocation overall and improvement was even more significant for nested queries.

• Nest levels: Considering additional SQL queries and serialization required per nest level, 20% to 50% less performance per nest levels seems reasonable. However, with increased resources, performance drop with nested queries seems to be smaller. It was also observed that querying nested objects for has-many relationship affects performance more than has-one relationship, which indicates serialization and validation of retrieved records as per GraphQL schema is possibly the factor for larger latency and resource consumption.

• Resolver model: Bucket queue model with Superlifter vs recursion model were compared for more performant resolver implementation. Though results are not included in this page, recursion model was more performant in a case of Phrag which has simple resolution requirements. The overhead of adding queue and resolving them through Promise (CompletableFuture) seemed to affect performance.