The goal of this benchmark is to understand the performance, failure and scaling characterics of YugaByte DB with a massive dataset (multiple TB per node). In order to accomplish that, we will do the following:
- Load 30 billion key-value records
- Each write operation inserts a single record
- Perform a read-heavy workload that does random reads in the presence of some writes
- Perform a read-heavy workload that does reads of a subset of data in the presence of some writes
Each record is a key-value record of size almost 300 bytes.
- Key size: 50 Bytes
- Value size: 256 Bytes (chosen to be not very compressible)
Note that the load tester was run from a separate machine in the same AZ.
A machine in the AWS cloud with the following spec was chosen: 32-vcpus, 240 GB RAM, 4 x 1.9TB nvme SSD.
- Cloud: AWS
- Node Type: i3.8xlarge
Create a standard 4 node cluster, with replication factor of 3. Pass the following option to the YugaByte DB processes.
yb_num_shards_per_tserver was set to 20 (from the default value of 8). This is done because the i3.8xlarge nodes have 4 disks. In future, YugaByte DB will automatically pick better defaults for nodes with multiple disks.
Export the following environment variable:
$ export YCQL_ADDRS="<ip1>:9042,<ip2>:9042,<ip3>:9042,<ip4>:9042"
Initial Load Phase
The data was loaded at a steady rate over about 4 days using the CassandraKeyValue sample application. The command to load the data is shown below:
$ java -jar yb-sample-apps.jar \ --workload CassandraKeyValue \ --nouuid --nodes $YCQL_ADDRS \ --value_size 256 \ --num_unique_keys 30000000000 \ --num_writes 30000000000 \ --num_threads_write 256 \ --num_threads_read 1
You should see a steady 85K inserts/sec with the write latencies in the 2.5ms ballpark. This is shown graphically below.
Data set size growth rate
The graph below shows the steady growth in SSTables size at a node from Sep 4 to Sep 7th beyond which it stabilizes at 6.5TB.
Final data set size
The figure below is from the yb-master Admin UI shows the tablet servers, number of tablets on each, number of tablet leaders and size of the on-disk SSTable files.
The uncompressed dataset size per node is 8TB, while the compressed size is 6.5TB. This is because the load generator generates random bytes, which are not very compressible.
Real world workloads generally have much more compressible data.
The results you see should be in the same ballpark as shown below.
Load Phase Results
|Records inserted||30 Billion|
|Size of each record||~ 300 bytes|
|Time taken to insert data||4.4 days|
|Sustained insert Rate||85K inserts/second|
|Final dataset in cluster||26TB across 4 nodes|
|Final dataset size per node||6.5TB / node|
Read-Heavy Workload Results
|Random-data read heavy workload||185K reads/sec and 1K writes/sec|
|Recent-data read heavy Workload||385K reads/sec and 6.5K writes/sec|
Cluster Expansion and Induced Failures
- Expanded from 4 to 5 nodes in about 8 hours
- Deliberately rate limited at 200MB/sec
- New node takes traffic as soon the first tablet arrives
- Pressure relieved from old nodes very quickly
- Induced one node failure in 5 node cluster
- Cluster rebalanced in 2 hrs 10 minutes