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bdr-benchmark-kit

BDR Benchmark Kit based on Terraform and Ansible

Prerequisites

The following components must be installed on the system:

  • Python3
  • AWS CLI
  • Ansible
  • Terraform
  • TPAexec

Prequisites installation on Debian11

Python3/pip3

$ sudo apt install python3 python3-pip -y
$ sudo pip3 install pip --upgrade

AWS CLI

$ sudo pip3 install awscli

AWS Access Key and Secret Access Key configuration:

$ aws configure

Ansible

$ sudo pip3 install ansible-core

Terraform

$ sudo apt install unzip -y
$ wget https://releases.hashicorp.com/terraform/1.1.9/terraform_1.1.9_linux_amd64.zip
$ unzip terraform_1.1.9_linux_amd64.zip
$ sudo install terraform /usr/bin

TPAexec

Please refer to TPAexec installation guide.

BDR environment provisioing and deployment on AWS

In order to provision the required resources on AWS, this repository should be cloned:

$ cd $HOME
$ git clone https://github.com/EnterpriseDB/bdr-benchmark-kit.git
$ cd bdr-benchmark-kit

Target AWS infrastructure

Depending on the choice of the BDR Always-ON architecture (gold or silver), 2 files are used to describe the target AWS infrastrures:

Default Silver Infrastructure

Object Configuration
Region us-east-1
OS Rocky 8.4
BDR servers 2 x c5d.12xlarge in us-east-1b, 1 x c5d.12xlarge in us-east-1c
Proxy servers 2 x c5.9xlarge in us-east-1b and us-east-1c
Barman server 1 x c5.2xlarge + 3TB of additional storage in us-east-1b
DBT2 client 1 x c5.18xlarge in us-east-1b
DBT2 driver 1 x c5.18xlarge in us-east-1b

Default Gold Infrastructure

Object Configuration
Regions us-east-1, us-east-2 and us-west-1
OS Rocky 8.4
BDR servers 4 x c5d.12xlarge in us-east-1b, us-east-1c, us-east-2a and us-east-2b
BDR witness 1 x c5.4xlarge in us-west-1b
Proxy servers 4 x c5.9xlarge in us-east-1b, us-east-1c, us-east-2a, us-east-2b
Barman server 1 x c5.2xlarge + 3TB of additional storage in us-east-1b
DBT2 client 1 x c5.18xlarge in us-east-1b
DBT2 driver 1 x c5.18xlarge in us-east-1b

Deployment Configuration

The most important settings are accessible through the configuration.yml file. This file should be updated according to your requirements. The following variables must be set:

  • repo_username: EDB package repository username
  • repo_password: EDB package repository password
  • tpa_subscription_token: EDB subscription token

Depending on how and where TPAexec has been installed, tpa_bin_path could be updated.

By default, the number of TPC-C warehouses is set to 5000, leading to produce around 500GB of data and indexes. To change the number of warehouses, the variable dbt2_warehouse should be used.

Default Postgres flavour is EPAS in version 14.

Cloud Resources Creation and Deployment

Once the configuration.yml file has been updated and the target infrastructure file adapted (not mandatory), it's now time to proceed with cloud resources creation:

  1. A new project must be created with the help of the new-project.py script. This script is in charge of creating a dedicated directory for the project, generating SSH keys, building Terraform configuration based on the infrastructure file, copying Ansible and Terraform code into the project directory.

    First argument is the project path, second argument is the path to the infrastructure file:

    $ python3 ~/bdr-benchmark-kit/scripts/new-project.py \
      ~/my_benchmark \
      ~/bdr-benchmark-kit/gold-infrastructure.yml

  2. Terraform initialisation of the project:

    $ cd ~/my_benchmark
$ terraform init

  3. Apply resources creation:

    $ cd ~/my_benchmark
$ terraform apply \
      -var-file=./terraform_vars.json \
      -auto-approve

When the Cloud resources are ready, the next step is software deployment.

  1. Execute pre-deployment operations: building TPAexec config.yml and Ansible inventory file, generate the deploy.sh script, etc.. Depending on the target BDR architecture, the -a option must be set to silver or gold.

    First argument is the project path, second argument is the configuration.yml path.

    Usage example:

    $ python3 ~/bdr-benchmark-kit/scripts/pre-deploy.py \
      -a gold \
      ~/my_benchmark \
      ~/bdr-benchmark-kit/configuration.yml

  2. Execute the deployment script:

    $ cd ~/my_benchmark
$ ./deploy.sh

SSH access to the machines

Once the deployment is completed, machines public and private IPs are stored in the servers.yml file.

Example:

---
servers:
  barman1:
    type: barman
    region: us-east-1
    az: us-east-1b
    public_ip: 54.166.46.2
    private_ip: 10.0.0.103
    public_dns: ec2-54-166-46-2.compute-1.amazonaws.com
  bdr1:
    type: bdr
    region: us-east-1
    az: us-east-1b
    public_ip: 3.80.202.134
    private_ip: 10.0.0.148
    public_dns: ec2-3-80-202-134.compute-1.amazonaws.com
[...]

SSH keys are stored in ssh-id_rsa and ssh-id_rsa.pub.

Replication modes

Asynchronous replication

By default, data replication is configured in asynchronous mode.

Quorum based synchronous replication

Enabling quorum based synchronous replication, in the Gold layout, can be achieved by updating Postgres configuration on bdr1 as following:

edb=# ALTER SYSTEM SET synchronous_standby_names TO 'FIRST 1 (bdr_edb_bdrdb_group_bdr1_bdr2, bdr_edb_bdrdb_group_bdr1_bdr3, bdr_edb_bdrdb_group_bdr1_bdr4)';
edb=# SELECT pg_reload_conf();

Asynchronous replication with BDR Lag Control

The following configuration enables BDR Lag Control:

edb=# ALTER SYSTEM SET bdr.lag_control_min_conforming_nodes TO '4';
edb=# ALTER SYSTEM SET bdr.lag_control_max_commit_delay TO '60000';
edb=# ALTER SYSTEM SET bdr.lag_control_max_lag_size TO '1.5GB';
edb=# SELECT pg_reload_conf();

Data Replication - Benchmark Execution

Operations on dbt2-driver

Start a new SSH session to the dbt2-driver machine:

$ ssh -i ssh-id_rsa rocky@<dbt2-driver-public-ip>

Start the script in charge of generating the database load:

$ sudo su - dbt2
$ screen
$ python3 ./dbt2-driver-rampup.py \
      -c <dbt2-client-private-ip> \
      -d 120 \
      -w 5000 \
      -s 1 \
      -m 71 \
      -S 3 \
      -P <proxy1-private-ip> \
      --pg "host=<bdr1-private-ip> port=5444 user=dbt2 dbname=edb"

Notes

dbt2-driver-rampup.py arguments:

  • -c: private IP address of the DBT2 client machine
  • -d: test duration in second, for each iteration
  • -w: number of wharehouses. Must match the value defined in configuration.yml
  • -s: starting number of terminals
  • -m: maximum number of terminals
  • -S: increment the number of terminals by this value, for each iteration
  • -P: Harp proxy node private IP
  • --pg: PG connection string to bdr1

Results

dbt2-driver-rampup.py displays on its output the results, for each number of terminals, in CSV format.

Example:

timestamp,terminals,notpm,bdr2_catchup_time,bdr2_sustainable_notpm,bdr3_catchup_time,bdr3_sustainable_notpm,bdr4_catchup_time,bdr4_sustainable_notpm
2022-05-19T07:09:28.333929,1,10813.61,0.005883,10813.079888758453,0.005964,10813.072590292262,0.005973,10813.071779352184
2022-05-19T07:11:40.596692,4,33318.31,0.001876,33317.78913189657,0.001897,33317.78330137564,0.001906,33317.78080258158
2022-05-19T07:14:05.854092,7,52895.29,0.005094,52893.0446902529,0.005150,52893.02000789133,0.005159,52893.01604108537
2022-05-19T07:16:39.027928,10,70323.03,0.001565,70322.11288244449,0.001589,70322.09881820815,0.001597,70322.0941301306
2022-05-19T07:19:15.204003,13,85270.68,1.023976,84549.21031515274,0.310567,85050.5641786228,0.002855,85268.65131667076

Failover & Switchover - Benchmark Execution

Downtime, from an application point of view, is measured by the downtime-checker.py script. This script is in charge of inserting records into a table containing the name of BDR node the script is connected to, and the current timestamp. In addition, it's able to generate additional traffic based on the DBT2 kit.

This script must be executed from the dbt2-driver machine:

$ sudo su - dbt2
$ screen
# With additional traffic/load generated by DBT2
$ python3 downtime-checker.py \
  --pg "host=<proxy1-private-ip> dbname=edb port=6432" \
  -T -c <dbt2-client-private-ip> \
  -P <proxy1-private-ip> \
  -w 5000
# Or without additional traffic
$ python3 downtime-checker.py \
  --pg "host=<proxy1-private-ip> dbname=edb port=6432"

Once the script is running, we can proceed with the following to trigger a switchover or a failover.

Switchover

To trigger a switchover operation, on the bdr1 machine:

$ sudo harpctl promote bdr2

Example:

[dbt2@ip-10-0-0-199 ~]$ python3 downtime-checker.py \
    --pg "host=10.0.0.123 dbname=edb port=6432" \
    -T \
    -c 10.0.0.250 \
    -P 10.0.0.123 \
    -w 5000
2022-05-19 20:49:44.140803 INFO: Starting DBT2 client...
2022-05-19 20:49:49.147031 INFO: Starting DBT2 driver...
2022-05-19 20:49:49.148906 INFO: Connecting to PostgreSQL...
2022-05-19 20:49:49.172649 INFO: Connected.
2022-05-19 20:50:04.702920 ERROR: Connection lost
2022-05-19 20:50:04.702956 INFO: Connecting to PostgreSQL...
2022-05-19 20:50:04.728276 INFO: Connected.
Downtime: 0:00:00.061442

Once the measurement is done, bdr1 can re-promoted as the BDR lead master node with:

$ sudo harpctl promote bdr1

Failover - Postgres crash

To simulate a Postgres crash incident, the follwing can be executed on the bdr1 machine:

$ sudo killall -9 harp-manager edb-postmaster

Once the measurement is done, bdr1 can be reintegrated to the BDR cluster:

$ sudo systemctl start harp-manager
# Wait for the end of Postgres recovery
$ sudo harpctl promote bdr1

Failover - System crash

To simulate a System crash incident, the follwing can be executed on the bdr1 machine:

$ sudo -i
$ echo c > /proc/sysrq-trigger
# SSH connection to bdr1 will freeze and the machine is rebooting

Once the measurement is done and the machine is up andrunning, bdr1 can be reintegrated to the BDR cluster:

$ sudo harpctl promote bdr1

Cloud resources destruction

$ cd ~/my_benchmark
$ terraform destroy \
    -var-file=./terraform_vars.json \
    -auto-approve