Simple workloads
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 |
| API | YSQL |
| 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.
The total dataset was 3.2TB across the two nodes. Each node had just over 1TB of data.
