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Gain up to 36% performance benefits for deploying Elasticsearch on Alibaba Cloud Yitian 710 instances

Zhengjun Xing
2 minute read time.

Elasticsearch is a powerful, open-source, distributed search and analytics engine known for its speed, scalability, and flexibility. It is widely used for various purposes, including full-text search, logging and log analysis, real-time analytics, and more. Its popularity extends significantly to cloud environments.

The http_logs track is a standard dataset and workload used in Elasticsearch's Rally benchmarking tool. It simulates typical web server log data, making it ideal for evaluating Elasticsearch performance in scenarios involving log data ingestion and analysis.

In this blog post, we compare the performance of the Elasticsearch http_logs track test case on two types of Alibaba Cloud Elastic Compute Service (ECS) instances: ECS g8y (powered by Yitian 710 processors based on Arm Neoverse N2 architecture) and g7 (powered by 3rd Generation Intel Xeon Scalable processors). Our findings reveal that the Elasticsearch http_logs track test case on g8y instances can achieve up to a 36% performance advantage over g7 instances. The following sections detail our testing methodology and results.

Test environment:

The test requires three xlarge instances running the Elasticsearch server and one 4xlarge instance running the esrally benchmark. Both g8y and g7 cloud instances are deployed with the same vCPUs and memory, running the same OS and Elasticsearch/esrally software versions and configurations.

Hardware Information:

Alibaba instances Processor vCPUs  DRAM(GB)  Disk(GB)
g8y.xlarge Yitian 710 4 16 200
g8y.4xlarge Yitian 710 16 64 200
g7.xlarge Intel Ice Lake 4 16 200
g7.4xlarge Intel Ice Lake 16 64 200

Software Versions and Settings:

OS Alibaba Cloud Linux 3.2104 LTS 64-bit
Elastic Search 8.7.1
esrally 2.11.0
Java openjdk-17.0.10
JVM “-Xms8g -Xmx8g -XX:+UseG1GC”

Benchmark esrally Settings:

track                      http_logs
pipeline benchmark-only
target-hosts node1:9200,node2:9200,node3:9200
challenge append-no-conflicts
include-tasks delete-index,create-index,check-cluster-health,index-append,hourly_agg
track-params "index.number_of_shards":12
"index.translog.durability":"async"
"index.refresh_interval":"10s"
"index.translog.flush_threshold_size":"1024mb"
"bulk_indexing_clients":20
"bulk_size":5000

Test results

To minimize testing errors, we performed multiple tests (approximately 10 times), and the final data presented is the average of these results.

The test results show that the G8Y index-append throughput is over 36% higher than G7 across the min, mean, median, and max metrics, with p50 and p90 service times approximately 30% shorter. The p99 to p100 service times are like to those of G7. Furthermore, for hourly_agg at the same 0.20 ops/s throughput, G8Y's service time is more than 27% shorter than G7 across the p50, p90, p99, and p100 metrics.

track Metric Task unit G8Y   G7 Performance improvement
http_logs Min Throughput index-append docs/s 507769.07 361915.36 40.30%
Mean Throughput index-append docs/s 532449.65 381351.57 39.62%
Median Throughput index-append docs/s 532328.58 375495.82 41.77%
Max Throughput index-append docs/s 559808.89 410070.47 36.52%
50th percentile latency index-append ms 182.75 258.71 29.36%
90th percentile latency index-append ms 282.82 408.34 30.74%
99th percentile latency index-append ms 670.78 699.52 4.11%
99.9th percentile latency index-append ms 2786.91 2773.83 -0.47%
99.99th percentile latency index-append ms 3580.11 3706.09 3.40%
100th percentile latency index-append ms 4291.42 4411.63 2.72%
50th percentile service time index-append ms 182.75 258.71 29.36%
90th percentile service time index-append ms 282.82 408.34 30.74%
99th percentile service time index-append ms 670.78 699.52 4.11%
99.9th percentile service time index-append ms 2786.91 2773.83 -0.47%
99.99th percentile service time index-append ms 3580.11 3706.09 3.40%
100th percentile service time index-append ms 4291.42 4411.63 2.72%
error rate index-append % 0.00 0.00 NA
Min Throughput hourly_agg ops/s 0.20 0.20 NA
Mean Throughput hourly_agg ops/s 0.20 0.20 NA
Median Throughput hourly_agg ops/s 0.20 0.20 NA
Max Throughput hourly_agg ops/s 0.20 0.20 NA
50th percentile latency hourly_agg ms 746.29 1035.48 27.93%
90th percentile latency hourly_agg ms 772.10 1069.32 27.80%
99th percentile latency hourly_agg ms 791.22 1095.67 27.79%
100th percentile latency hourly_agg ms 801.38 1103.35 27.37%
50th percentile service time hourly_agg ms 744.38 1033.88 28.00%
90th percentile service time hourly_agg ms 770.35 1067.63 27.85%
99th percentile service time hourly_agg ms 789.17 1093.97 27.86%
100th percentile service time hourly_agg ms 799.56 1101.41 27.41%
error rate hourly_agg % 0.00 0.00 NA

G8Y's index-append throughput is more than 36% higher than G7's across the min, mean, median, and max metrics.

This graph shows the index-append throughput improvement report for g7 and g8

Index-append Throughput Improvement Report

G8Y's index-append service times at the p50 and p90 levels are approximately 30% shorter than G7's, while the p99 to p100 service times are similar to those of G7.

This graph shows the index-append service time improvement report for g7 and g8.

Index-append Service Time Improvement Report

When hourly_agg operates at the same 0.20 ops/s throughput, G8Y's service time is more than 27% shorter than G7's across the p50, p90, p99, and p100 metrics.

This graph shows the Hourly_agg service time improvement report for g7 and g8.

Hourly_agg Service Time Improvement Report

Conclusion

In the Elasticsearch benchmark esrally http_logs track test case, G8Y outperforms G7 with over 36% higher throughput for index-append, along with approximately 30% shorter p50 and p90 service times. Additionally, G8Y reduces service time by more than 27% across the p50, p90, p99, and p100 metrics for hourly_agg.

References

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