TPC-C

Benchmark YSQL performance using TPC-C

TPC-C is a popular online transaction processing benchmark that provides metrics you can use to evaluate the performance of YugabyteDB for concurrent transactions of different types and complexity, and which are either executed online or queued for deferred execution.

Results overview

All the nodes in the cluster were located in AWS-west in the same zone. The benchmark VM was the same type as the cluster nodes and was deployed in the same zone. Each test was run for 30 minutes after loading the data.

All benchmarks were run using YugabyteDB v2.18.1, except 150K warehouses, which was run on v2.11.

Horizontal scaling

The following table shows how YugabyteDB scales horizontally, providing increased throughput with the same efficiency when the number of nodes in the cluster is increased.

Warehouses TPMC Efficiency(%) Nodes Connections New Order Latency Machine Type (vCPUs)
500 25646.4 99.71 3 200 54.21 ms m6i.2xlarge (8)
1000 34212.57 99.79 4 266 53.92 ms m6i.2xlarge (8)
2000 42772.6 99.79 5 333 51.01 ms m6i.2xlarge (8)
4000 51296.9 99.72 6 400 62.09 ms m6i.2xlarge (8)

Vertical scaling

The following table shows how YugabyteDB scales vertically, providing increased throughput when the power of the machines is increased while keeping the number of nodes in the cluster the same.

Warehouses TPMC Efficiency(%) Nodes Connections New Order Latency Machine Type (vCPUs)
500 6415.7 99.78 3 50 64.08 ms m6i.large (2)
1000 12829.93 99.77 3 100 73.97 ms m6i.xlarge (4)
2000 25646.4 99.78 3 200 54.21 ms m6i.2xlarge (8)
4000 51343.5 99.81 3 400 39.46 ms m6i.4xlarge (16)

100K warehouses

Warehouses TPMC Efficiency(%) Nodes Connections New Order Latency Machine Type (vCPUs)
100,000 1283804.18 99.83 59 1000 51.86 ms c5d.9xlarge (36)

150K warehouses

Warehouses TPMC Efficiency(%) Nodes Connections New Order Latency Machine Type (vCPUs)
150,000 1M 99.30 75 9000 123.33 ms c5d.12xlarge (96)

Benchmark setup

Run a TPC-C workload against YugabyteDB YSQL using the following steps.

Get TPC-C binaries

To download the TPC-C binaries, run the following commands.

$ wget https://github.com/yugabyte/tpcc/releases/download/2.0/tpcc.tar.gz
$ tar -zxvf tpcc.tar.gz
$ cd tpcc

Start YugabyteDB

Start your YugabyteDB cluster by following the steps for a manual deployment.

Tip

You will need the IP addresses of the nodes in the cluster for the next step.

Configure DB connection parameters (optional)

You can configure the workload, including the IP addresses of the nodes, number of warehouses, and number of loader threads, using command line arguments.

Other options like username, password, port, and so on, can be changed using the configuration file at config/workload_all.xml, if needed.

<port>5433</port>
<username>yugabyte</username>
<password></password>

Other considerations

When running tests, be sure to do the following:

  • Run the latest TPCC code. Use the latest enhancements to the Yugabyte TPCC application by downloading the latest released version, or clone the repository and build from the source to get the very latest changes.

  • Pre-compact tables using the yb-admin utility's compact_table command.

  • Warm the database using the --warmup-time-secs flag when you call the execute phase of the TPCC benchmark.

Run TPC-C

Load phase

Before starting the workload, you need to load the data. Make sure to replace the IP addresses with that of the nodes in the cluster.

$ ./tpccbenchmark --create=true --nodes=127.0.0.1,127.0.0.2,127.0.0.3

$ ./tpccbenchmark --load=true --nodes=127.0.0.1,127.0.0.2,127.0.0.3
Cluster Loader threads Loading time Data set size
3 nodes, type c5d.large 10 ~13 minutes ~20 GB

The loading time for ten warehouses on a cluster with 3 nodes of type c5d.4xlarge is approximately 3 minutes.

Before starting the workload, you need to load the data. Make sure to replace the IP addresses with that of the nodes in the cluster. Loader threads allow you to configure the number of threads used to load the data. For a 3-node c5d.4xlarge cluster, loader threads value of 48 was optimal.

$ ./tpccbenchmark --create=true --nodes=127.0.0.1,127.0.0.2,127.0.0.3

$ ./tpccbenchmark --load=true \
  --nodes=127.0.0.1,127.0.0.2,127.0.0.3 \
  --warehouses=100 \
  --loaderthreads 48
Cluster Loader threads Loading time Data set size
3 nodes, type c5d.4xlarge 48 ~20 minutes ~80 GB

Tune the --loaderthreads parameter for higher parallelism during the load, based on the number and type of nodes in the cluster. The specified 48 threads value is optimal for a 3-node cluster of type c5d.4xlarge (16 vCPUs). For larger clusters or computers with more vCPUs, increase this value accordingly. For clusters with a replication factor of 3, a good approximation is to use the number of cores you have across all the nodes in the cluster.

Before starting the workload, you need to load the data first. Make sure to replace the IP addresses with that of the nodes in the cluster. Loader threads allow you to configure the number of threads used to load the data. For a 3-node c5d.4xlarge cluster, loader threads value of 48 was optimal.

$ ./tpccbenchmark --create=true --nodes=127.0.0.1,127.0.0.2,127.0.0.3

$ ./tpccbenchmark --load=true \
  --nodes=127.0.0.1,127.0.0.2,127.0.0.3 \
  --warehouses=1000 \
  --loaderthreads 48
Cluster Loader threads Loading time Data set size
3 nodes, type c5d.4xlarge 48 ~3.5 hours ~420 GB

Tune the --loaderthreads parameter for higher parallelism during the load, based on the number and type of nodes in the cluster. The specified 48 threads value is optimal for a 3-node cluster of type c5d.4xlarge (16 vCPUs). For larger clusters or computers with more vCPUs, increase this value accordingly. For clusters with a replication factor of 3, a good approximation is to use the number of cores you have across all the nodes in the cluster.

Before starting the workload, you need to load the data. In addition, you need to ensure that you exported a list of all IP addresses of all the nodes involved.

For 10k warehouses, you would need ten clients of type c5.4xlarge to drive the benchmark. For multiple clients, you need to perform three steps.

First, you create the database and the corresponding tables. Execute the following command from one of the clients:

./tpccbenchmark  --nodes=$IPS  --create=true

After the database and tables are created, you can load the data from all ten clients:

Client Command
1 ./tpccbenchmark --load=true --nodes=$IPS --warehouses=1000 --start-warehouse-id=1 --total-warehouses=10000 --loaderthreads 48
2 ./tpccbenchmark --load=true --nodes=$IPS --warehouses=1000 --start-warehouse-id=1001 --total-warehouses=10000 --loaderthreads 48
3 ./tpccbenchmark --load=true --nodes=$IPS --warehouses=1000 --start-warehouse-id=2001 --total-warehouses=10000 --loaderthreads 48
4 ./tpccbenchmark --load=true --nodes=$IPS --warehouses=1000 --start-warehouse-id=3001 --total-warehouses=10000 --loaderthreads 48
5 ./tpccbenchmark --load=true --nodes=$IPS --warehouses=1000 --start-warehouse-id=4001 --total-warehouses=10000 --loaderthreads 48
6 ./tpccbenchmark --load=true --nodes=$IPS --warehouses=1000 --start-warehouse-id=5001 --total-warehouses=10000 --loaderthreads 48
7 ./tpccbenchmark --load=true --nodes=$IPS --warehouses=1000 --start-warehouse-id=6001 --total-warehouses=10000 --loaderthreads 48
8 ./tpccbenchmark --load=true --nodes=$IPS --warehouses=1000 --start-warehouse-id=7001 --total-warehouses=10000 --loaderthreads 48
9 ./tpccbenchmark --load=true --nodes=$IPS --warehouses=1000 --start-warehouse-id=8001 --total-warehouses=10000 --loaderthreads 48
10 ./tpccbenchmark --load=true --nodes=$IPS --warehouses=1000 --start-warehouse-id=9001 --total-warehouses=10000 --loaderthreads 48

Tune the --loaderthreads parameter for higher parallelism during the load, based on the number and type of nodes in the cluster. The value specified, 48 threads, is optimal for a 3-node cluster of type c5d.4xlarge (16 vCPUs). For larger clusters or computers with more vCPUs, increase this value accordingly. For clusters with a replication factor of 3, a good approximation is to use the number of cores you have across all the nodes in the cluster.

When the loading is completed, execute the following command to enable the foreign keys that were disabled to aid the loading times:

./tpccbenchmark  --nodes=$IPS  --enable-foreign-keys=true
Cluster Loader threads Loading time Data set size
30 nodes, type c5d.4xlarge 480 ~5.5 hours ~4 TB

TPC-C Execute Phase

You can run the workload against the database as follows:

$ ./tpccbenchmark --execute=true \
  --nodes=127.0.0.1,127.0.0.2,127.0.0.3

You can run the workload against the database as follows:

$ ./tpccbenchmark --execute=true \
  --nodes=127.0.0.1,127.0.0.2,127.0.0.3 \
  --warehouses=100

You can run the workload against the database as follows:

$ ./tpccbenchmark --execute=true \
  --nodes=127.0.0.1,127.0.0.2,127.0.0.3 \
  --warehouses=1000

Before starting the execution, you have to move all the tablet leaders out of the node containing the master leader by running the following command:

./yb-admin --master_addresses <master-ip1>:7100,<master-ip2>:7100,<master-ip3>:7100 change_leader_blacklist ADD <master-leader-ip>

Make sure that the IP addresses used in the execution phase don't include the master-leader-ip. You can then run the workload against the database from each client:

Client Command
1 ./tpccbenchmark --nodes=$IPS --execute=true --warehouses=1000 --num-connections=300 --start-warehouse-id=1 --total-warehouses=10000 --warmup-time-secs=900
2 ./tpccbenchmark --nodes=$IPS --execute=true --warehouses=1000 --num-connections=300 --start-warehouse-id=1001 --total-warehouses=10000 --warmup-time-secs=900
3 ./tpccbenchmark --nodes=$IPS --execute=true --warehouses=1000 --num-connections=300 --start-warehouse-id=2001 --total-warehouses=10000 --warmup-time-secs=900
4 ./tpccbenchmark --nodes=$IPS --execute=true --warehouses=1000 --num-connections=300 --start-warehouse-id=3001 --total-warehouses=10000 --warmup-time-secs=900
5 ./tpccbenchmark --nodes=$IPS --execute=true --warehouses=1000 --num-connections=300 --start-warehouse-id=4001 --total-warehouses=10000 --warmup-time-secs=900
6 ./tpccbenchmark --nodes=$IPS --execute=true --warehouses=1000 --num-connections=300 --start-warehouse-id=5001 --total-warehouses=10000 --warmup-time-secs=720 --initial-delay-secs=180
7 ./tpccbenchmark --nodes=$IPS --execute=true --warehouses=1000 --num-connections=300 --start-warehouse-id=6001 --total-warehouses=10000 --warmup-time-secs=540 --initial-delay-secs=360
8 ./tpccbenchmark --nodes=$IPS --execute=true --warehouses=1000 --num-connections=300 --start-warehouse-id=7001 --total-warehouses=10000 --warmup-time-secs=360 --initial-delay-secs=540
9 ./tpccbenchmark --nodes=$IPS --execute=true --warehouses=1000 --num-connections=300 --start-warehouse-id=8001 --total-warehouses=10000 --warmup-time-secs=180 --initial-delay-secs=720
10 ./tpccbenchmark --nodes=$IPS --execute=true --warehouses=1000 --num-connections=300 --start-warehouse-id=9001 --total-warehouses=10000 --warmup-time-secs=0 --initial-delay-secs=900

TPC-C Benchmark Results

Cluster: 3 nodes of type c5d.large

TPMC: 127

Efficiency: 98.75%

Latencies:

  • New Order Avg: 66.286 ms, p99: 212.47 ms
  • Payment Avg: 17.406 ms, p99: 186.884 ms
  • OrderStatus Avg: 7.308 ms, p99: 86.974 ms
  • Delivery Avg: 66.986 ms, p99: 185.919 ms
  • StockLevel Avg: 98.32 ms, p99: 192.054 ms

After the execution is completed, the TPM-C number along with the efficiency is printed, as follows:

21:09:23,588 (DBWorkload.java:955) INFO  - Throughput: Results(nanoSeconds=1800000263504, measuredRequests=8554) = 4.752221526539232 requests/sec reqs/sec
21:09:23,588 (DBWorkload.java:956) INFO  - Num New Order transactions : 3822, time seconds: 1800
21:09:23,588 (DBWorkload.java:957) INFO  - TPM-C: 127
21:09:23,588 (DBWorkload.java:958) INFO  - Efficiency : 98.75%
21:09:23,593 (DBWorkload.java:983) INFO  - NewOrder, Avg Latency: 66.286 msecs, p99 Latency: 212.47 msecs
21:09:23,596 (DBWorkload.java:983) INFO  - Payment, Avg Latency: 17.406 msecs, p99 Latency: 186.884 msecs
21:09:23,596 (DBWorkload.java:983) INFO  - OrderStatus, Avg Latency: 7.308 msecs, p99 Latency: 86.974 msecs
21:09:23,596 (DBWorkload.java:983) INFO  - Delivery, Avg Latency: 66.986 msecs, p99 Latency: 185.919 msecs
21:09:23,596 (DBWorkload.java:983) INFO  - StockLevel, Avg Latency: 98.32 msecs, p99 Latency: 192.054 msecs
21:09:23,597 (DBWorkload.java:792) INFO  - Output Raw data into file: results/oltpbench.csv

Cluster: 3 nodes of type c5d.4xlarge

TPMC: 1271.77

Efficiency: 98.89%

Latencies:

  • New Order Avg: 68.265 msecs, p99: 574.339 msecs
  • Payment Avg: 19.969 msecs, p99: 475.311 msecs
  • OrderStatus Avg: 13.821 msecs, p99: 571.414 msecs
  • Delivery Avg: 67.384 msecs, p99: 724.67 msecs
  • StockLevel Avg: 114.032 msecs, p99: 263.849 msecs

Once the execution is completed, the TPM-C number along with the efficiency is printed, as follows:

04:54:54,560 (DBWorkload.java:955) INFO  - Throughput: Results(nanoSeconds=1800000866600, measuredRequests=85196) = 47.33108832382159 requests/sec reqs/sec
04:54:54,560 (DBWorkload.java:956) INFO  - Num New Order transactions : 38153, time seconds: 1800
04:54:54,560 (DBWorkload.java:957) INFO  - TPM-C: 1,271.77
04:54:54,560 (DBWorkload.java:958) INFO  - Efficiency : 98.89%
04:54:54,596 (DBWorkload.java:983) INFO  - NewOrder, Avg Latency: 68.265 msecs, p99 Latency: 574.339 msecs
04:54:54,615 (DBWorkload.java:983) INFO  - Payment, Avg Latency: 19.969 msecs, p99 Latency: 475.311 msecs
04:54:54,616 (DBWorkload.java:983) INFO  - OrderStatus, Avg Latency: 13.821 msecs, p99 Latency: 571.414 msecs
04:54:54,617 (DBWorkload.java:983) INFO  - Delivery, Avg Latency: 67.384 msecs, p99 Latency: 724.67 msecs
04:54:54,618 (DBWorkload.java:983) INFO  - StockLevel, Avg Latency: 114.032 msecs, p99 Latency: 263.849 msecs
04:54:54,619 (DBWorkload.java:792) INFO  - Output Raw data into file: results/oltpbench.csv

Cluster: 3 nodes of type c5d.4xlarge

TPMC: 12,563.07

Efficiency: 97.69%

Latencies:

  • New Order Avg: 325.378 ms, p99: 3758.859 ms
  • Payment Avg: 277.539 ms, p99: 12667.048 ms
  • OrderStatus Avg: 174.173 ms, p99: 4968.783 ms
  • Delivery Avg: 310.19 ms, p99: 5259.951 ms
  • StockLevel Avg: 652.827 ms, p99: 8455.325 ms

When the execution is completed, the TPM-C number along with the efficiency is displayed, as follows:

17:18:58,728 (DBWorkload.java:955) INFO  - Throughput: Results(nanoSeconds=1800000716759, measuredRequests=842216) = 467.8975914612168 requests/sec reqs/sec
17:18:58,728 (DBWorkload.java:956) INFO  - Num New Order transactions : 376892, time seconds: 1800
17:18:58,728 (DBWorkload.java:957) INFO  - TPM-C: 12,563.07
17:18:58,728 (DBWorkload.java:958) INFO  - Efficiency : 97.69%
17:18:59,006 (DBWorkload.java:983) INFO  - NewOrder, Avg Latency: 325.378 msecs, p99 Latency: 3758.859 msecs
17:18:59,138 (DBWorkload.java:983) INFO  - Payment, Avg Latency: 277.539 msecs, p99 Latency: 12667.048 msecs
17:18:59,147 (DBWorkload.java:983) INFO  - OrderStatus, Avg Latency: 174.173 msecs, p99 Latency: 4968.783 msecs
17:18:59,166 (DBWorkload.java:983) INFO  - Delivery, Avg Latency: 310.19 msecs, p99 Latency: 5259.951 msecs
17:18:59,182 (DBWorkload.java:983) INFO  - StockLevel, Avg Latency: 652.827 msecs, p99 Latency: 8455.325 msecs
17:18:59,183 (DBWorkload.java:792) INFO  - Output Raw data into file: results/oltpbench.csv

When the execution is completed, you need to copy the csv files from each of the nodes to one of the nodes and run merge-results to display the merged results.

After copying the csv files to a directory such as results-dir, you can merge the results as follows:

./tpccbenchmark --merge-results=true --dir=results-dir --warehouses=10000

Cluster: 30 nodes of type c5d.4xlarge

TPMC: 125193.2

Efficiency: 97.35%

Latencies:

  • New Order Avg: 114.639 ms, p99: 852.183 ms
  • Payment Avg: 114.639 ms, p99 : 852.183 ms
  • OrderStatus Avg: 20.86 ms, p99: 49.31 ms
  • Delivery Avg: 117.473 ms, p99: 403.404 ms
  • StockLevel Avg: 340.232 ms, p99: 1022.881 ms

The output after merging should look similar to the following:

15:16:07,397 (DBWorkload.java:715) INFO - Skipping benchmark workload execution
15:16:11,400 (DBWorkload.java:1080) INFO - Num New Order transactions : 3779016, time seconds: 1800
15:16:11,400 (DBWorkload.java:1081) INFO - TPM-C: 125193.2
15:16:11,401 (DBWorkload.java:1082) INFO - Efficiency : 97.35%
15:16:12,861 (DBWorkload.java:1010) INFO - NewOrder, Avg Latency: 114.639 msecs, p99 Latency: 852.183 msecs
15:16:13,998 (DBWorkload.java:1010) INFO - Payment, Avg Latency: 29.351 msecs, p99 Latency: 50.8 msecs
15:16:14,095 (DBWorkload.java:1010) INFO - OrderStatus, Avg Latency: 20.86 msecs, p99 Latency: 49.31 msecs
15:16:14,208 (DBWorkload.java:1010) INFO - Delivery, Avg Latency: 117.473 msecs, p99 Latency: 403.404 msecs
15:16:14,310 (DBWorkload.java:1010) INFO - StockLevel, Avg Latency: 340.232 msecs, p99 Latency: 1022.881 msecs