Simple workloads

Performance running simple workloads when scaling out

DocDB, YugabyteDB's underlying distributed document store, uses a heavily customized version of RocksDB for node-local persistence. It has been engineered ground up to deliver high performance at a massive scale. Several features have been built into DocDB to support this design goal, including the following:

  • Scan-resistant global block cache
  • Bloom/index data splitting
  • Global memstore limit
  • Separate compaction queues to reduce read amplification
  • Smart load balancing across disks

These features enable YugabyteDB to handle massive datasets with ease. The following scenario loads 1 billion rows into a 3-node cluster.

1 billion rows

This experiment uses the YCSB benchmark with the standard JDBC binding to load 1 billion rows. The expected dataset at the end of the data load is 1TB on each node (or a 3TB total data set size in each cluster).

Cluster configuration
Region AWS us-west-2
Release v2.1.5
Nodes 3
Instance type c5.4xlarge
Storage 2 x 5TB SSDs (gp2 EBS)
Sharding Range and Hash
Isolation level Snapshot

Results at a glance

1B row data load completed successfully for YSQL (using a range-sharded table) in about 26 hours. The following graphs show the throughput and average latency for the range-sharded load phase of the YCSB benchmark during the load phase.

Ops and Latency

The total dataset was 3.2TB across the two nodes. Each node had just over 1TB of data.

Total dataset

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