TPC-C
Overview
Follow the steps below to run the TPC-C workload against YugabyteDB YSQL. 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 that are either executed online or queued for deferred execution.
Results at a glance (Amazon Web Services)
| Warehouses | TPMC | Efficiency (approx) | Cluster Details |
|---|---|---|---|
| 10 | 127 | 98.75% | 3 nodes of type c5d.large (2 vCPUs) |
| 100 | 1,271.77 | 98.89% | 3 nodes of type c5d.4xlarge (16 vCPUs) |
| 1,000 | 12563.07 | 97.90% | 3 nodes of type c5d.4xlarge (16 vCPUs) |
| 10,000 | 125163.2 | 97.35% | 30 nodes of type c5d.4xlarge (16 vCPUs) |
All the nodes in the cluster were in the same zone. The benchmark VM was the same type as the nodes in the cluster and was deployed in the same zone as the DB cluster. Each test was run for 30 minutes after the loading of the data.
Results at a glance (Microsoft Azure)
| Warehouses | TPMC | Efficiency (approx) | Cluster details |
|---|---|---|---|
| 50 | 639.4 | 99.44% | 3 nodes of type D16 v3 (16 vCPUs) with P40 disks |
| 100 | 1,271.37 | 98.86% | 3 nodes of type D16 v3 (16 vCPUs) with P40 disks |
| 1000 | 12,523.97 | 97.39% | 3 nodes of type D16 v3 (16 vCPUs) with P40 disks |
| 2000 | 25,407.43 | 98.78% | 3 nodes of type D16 v3 (16 vCPUs) with P40 disks |
All nodes in the cluster were in the same zone. The benchmark VM was the same type as the nodes in the cluster, and was deployed in the same zone as the DB cluster. Each test was run for 30 minutes after the loading of the data.
Prerequisites
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 the database
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)
Workload configuration like IP addresses of the nodes, number of warehouses and number of loader threads can be controlled by command line arguments.
Other options like username, password, port, etc. can be changed using the configuration file at config/workload_all.xml, if needed.
<port>5433</port>
<username>yugabyte</username>
<password></password>
Best practices
Latest TPCC code: Use the latest enhancements to the Yugabyte TPCC application. You can either download the latest released version, or you can clone the repository and build from source to get the very latest changes.
Pre-compacting tables: Pre-compact tables with the yb-admin utility's compact_table command.
Warming the database: Use the --warmup-time-secs flag when you call the execute phase of the TPCC benchmark.
Run the TPC-C benchmark
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=$IPS --create=true
Once the database and tables are created, you can load the data from all ten clients:
| Client | Command |
|---|---|
| 1 | ./tpccbenchmark --load=true --nodes=$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=$IPS --warehouses=1000 --start-warehouse-id=9001 --total-warehouses=10000 --loaderthreads 48 |
Tune the --loaderthreads parameter for higher parallelism during the load, based on the number and type of nodes in the cluster. The value specified here, 48 threads, is optimal for a 3-node cluster of type c5d.4xlarge (16 vCPUs). For larger clusters, or computers with more vCPUs, increase this value accordingly. For clusters with a replication factor of 3, a good approximation is to use the number of cores you have across all the nodes in the cluster.
Once the loading is completed, execute the following command to enable the foreign keys that were disabled to aid the loading times:
./tpccbenchmark --nodes=$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 msecs, p99: 212.47 msecs
- Payment Avg: 17.406 msecs, p99: 186.884 msecs
- OrderStatus Avg: 7.308 msecs, p99: 86.974 msecs
- Delivery Avg: 66.986 msecs, p99: 185.919 msecs
- StockLevel Avg: 98.32 msecs, p99: 192.054 msecs
Once 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 msecs, p99: 3758.859 msecs
- Payment Avg: 277.539 msecs, p99: 12667.048 msecs
- OrderStatus Avg: 174.173 msecs, p99: 4968.783 msecs
- Delivery Avg: 310.19 msecs, p99: 5259.951 msecs
- StockLevel Avg: 652.827 msecs, p99: 8455.325 msecs
Once the execution is completed, the TPM-C number along with the efficiency is printed, 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.
Once you copied 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 msecs, p99: 852.183 msecs
- Payment Avg: 114.639 msecs, p99 : 852.183 msecs
- OrderStatus Avg: 20.86 msecs, p99: 49.31 msecs
- Delivery Avg: 117.473 msecs, p99: 403.404 msecs
- StockLevel Avg: 340.232 msecs, p99: 1022.881 msecs
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