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YugaByte DB P99 latencies with Netflix Data Store Benchmark


Recently we shared YCSB benchmark results for YugaByte DB. Apart from YCSB, Netflix Data Store Benchmark (NDBench) is another publicly available, cloud-enabled benchmark tool for data store systems. We ran NDBench against YugaByte DB for 7 days and want to share our results:


YugaByte DB machines:

  • 6-node cluster in Amazon Web Service (AWS). Each node is a i3.2xlarge machine with:
    • 8 vCPUs Intel® Xeon® CPU E5-2686 v4 (Broadwell) @2.3 GHz
    • 61GB RAM
    • 1.9TB direct attached SSD
    • Same availability zone
  • Replication factor = 3
  • Default YugaByte DB configuration parameters were used.

NDBench machine:

  • 1 c3.xlarge machine
    • 4 vCPUs Intel® Xeon® CPU E5-2680 v4 (Ivy Bridge)
    • 7.5GB RAM
  • Same availability zone as YugaByte DB cluster.

NDBench configuration used:

# Data size
ndbench.config.numKeys=20000000 # Backfill keys
# Number of worker threads
# Consistency level
# Request per second (RPS)


  • Execution time: 7 days (163 hours)
  • Total successful reads = 4.11 billion, writes = 1.05 billion
  • Latencies (milliseconds) observed:
    • Read: Avg = 1.368ms, P95 = 3.311ms, P99 = 5.722ms, P995 = 6.866ms
    • Write: Avg = 1.803ms, P95 = 2.299ms, P99 = 3.311ms, P995 = 4.768ms

Throughout the entire run, compactions were enabled and running in the background. In spite of that, our P99 (99% percentile) read/write latencies were still under 6 ms. These contrast significantly to much higher P99s seen with Apache Cassandra - primarily due to architectural reasons (eventual consistency needs background anti-entropy/read-repairs; quorum reads to achieve higher consistency put multiple servers in the critical path of each request) as well as implementation choices (Java implementation, GC pauses, etc.).