PG Counter Metrics ( PGCM ) is a tool for publishing PostgreSQL performance data to CloudWatch. By publishing to CloudWatch, dashboards and alarming can be used on the collected data.
Pg Counter Metrics ( PGCM ) components :
- Lambda
- CloudWatch dashboard
- CloudWatch metrics
- CloudWatch alarms
- RDS IAM database authentication
- AWS Secrets Manager
- python
Note: PG Counter Metrics support only one database in the PostgreSQL instance , in the future release it will suppport more than one Database and it will provide database performance metrics per database .
- PostgreSQL has no any historical performance data
- PG Counter Metrics’s CloudWatch metrics will be used with CloudWatch alarms to be the advanced alarm system
- PGCM provide comprehensive dashbaord and metrics
PGCM support three authentication types
1- IAM Database Authentication
2- AWS Secrets Manager
3- clear password ( it should be used for TEST ENV and debuing mode only )
There are three type of metrics
1- Database performance metrics
2- Table metrics (per table)
3- pg_stat_statements (only for the top 20 query that consume the DB time / per query id)
| # | Counter Metric Name | Description |
|---|---|---|
| 1 | Xid_Percent_Towards_Wraparound | Percent Towards Wraparound |
| 2 | percent_towards_emergency_autovacuum | Percent towards emergency autovacuum, when XID reach autovacuum_freeze_max_age parameter value |
| 3 | queries_canceled_due_to_lock_timeouts | number of queries canceled due to lock timeouts |
| 4 | queries_canceled_due_to_lock_deadlocks | number of queries canceled due to dead locks |
| 5 | idle_in_transaction_sessions | number of idle in transaction sessions |
| 6 | idle_sessions | number of idle sessions |
| 7 | idle_in_transaction_aborted_sessions | number of idle in transaction aborted sessions |
| 8 | active_sessions | number of active sessions |
| 9 | Inactive_replication_slots | number of Inactive replication slots |
| 10 | Active_replication_slots | number of Active replication slots |
| 11 | invalid_indexes | number of invalid indexes |
| 12 | deadlocks | number of deadlocks |
| 13 | total_connections | total number of connection |
| 14 | max_connections | max_connections parameter value |
| 15 | autovacuum_freeze_max_age | autovacuum_freeze_max_age parameter value |
| 16 | oldest_xid | oldest Transaction ID |
| 17 | autovacuum_count_per_min | how many autovacuum excuted per min |
| 18 | autovacuum_count_per_hour | how many autovacuum excuted per hour |
| 19 | autovacuum_count_per_day | how many autovacuum excuted per day |
| 20 | autoanalyze_count_per_min | how many autoanalyze excuted per min |
| 21 | autoanalyze_count_per_hour | how many autoanalyze excuted per hour |
| 22 | autoanalyze_count_per_day | how many autoanalyze excuted per day |
| 23 | total_DB_size_in_GB | total databases size in the Postgresql instance |
| 24 | blocked_sessions | number of blocked sessions |
| 25 | wait_event | wait_event / session count, how many sessions waiting on each wait event ,this metric is dynamic and it will create new cloud watch metric every time there is new wait event appear in the DB |
| 26 | oldest_open_transaction | this metic will show the longest running transaction for the active sessions only |
| 27 | oldest_open_idl_in_transaction | this metic will show the longest running idl in transaction for the sessions with idl in transaction status |
| 28 | n_tables_eligible_for_autovacuum | number of tables eligible for autovacuum |
| 29 | not_granted_lock | number of not granted lock |
| 30 | lock_mode | lock_mode column in pg_locks / session count,how many sessions waiting on each lock mode |
| 31 | lock_type | lock_type column in pg_locks / session count,how many sessions waiting on each lock_type |
| 32 | xact_commit | number of commits |
| 33 | xact_rollback | number of rollback |
| 34 | xact_commit_ratio | commit ratio |
| 35 | tup_returned | Number of rows returned by all queries |
| 36 | tup_fetched | Number of rows fetched by all queries |
| 37 | tup_updated | Number of rows updated by all queries |
| 38 | tup_deleted | Number of rows deleted by all queries |
| 39 | tup_inserted | Number of rows inserted by all queries |
| 40 | checkpoints_requested | Number of requested checkpoints that have been performed |
| 41 | checkpoints_timed | Number of scheduled checkpoints that have been performed |
| 42 | connections_utilization | 100*( total_connections / max_connections) |
| 43 | Oldest_Replication_Slot_Lag_gb_behind | The lagging size of the Oldest Replication Slot |
| 44 | Oldest_Replication_Slot_Lag_gb_behind_per_slot_(slot_name) | The lagging size for each Replication Slot |
| 45 | DBLoadCPU | total number of active sessions waiting on CPU |
| 46 | DBLoadNoneCPU | total number of active sessions waiting on None CPU wait event |
| 47 | bgwriter_buffers_backend | Number of buffers written directly by a backend |
| 48 | bgwriter_maxwritten_clean | Number of times the background writer stopped a cleaning scan because it had written too many buffers |
| 49 | bgwriter_buffers_clean | Number of buffers written by the background writer |
| 50 | oldest_mxid | the oldest Multixact IDs (MXID) |
| 51 | autovacuum_multixact_freeze_max_age | autovacuum_multixact_freeze_max_age parameter value |
Table Metric Name= < metric_name > _ < Table_Name >
| # | Counter Metric Name | Description |
|---|---|---|
| 1 | table_stat_n_tup_upd_ < Table_Name > | number of rows (tuples) updated |
| 2 | table_stat_n_tup_del_ < Table_Name > | number of rows (tuples) deleted |
| 3 | table_stat_n_tup_ins_ < Table_Name > | number of rows (tuples) inserted |
| 4 | table_stat_n_mod_since_analyze_ < Table_Name > | Estimated number of rows modified since this table was last analyzed |
| 5 | table_stat_n_tup_hot_upd_ < Table_Name > | number of hot update |
| 6 | table_stat_tup_ins_precent_ < Table_Name > | percent of rows (tuples) inserted |
| 7 | table_stat_tup_upd_precent_ < Table_Name > | percent of rows (tuples) updated |
| 8 | table_stat_tup_del_precent_ < Table_Name > | percent of rows (tuples) deleted |
| 9 | table_stat_total_idx_scan_ < Table_Name > | total number of index scan |
| 10 | table_stat_total_fts_scan_ < Table_Name > | total number of full table scan (seq scan ) |
| 11 | table_stat_n_live_tup_ < Table_Name > | number of the live rows (tuples) |
| 12 | table_stat_n_dead_tup_ < Table_Name > | number of the dead rows (tuples) |
| 13 | table_stat_dead_tup_percent_ < Table_Name > | percent of dead rows (tuples) |
| 14 | table_stat_autovacuum_count_ < Table_Name > | autovacuum count |
| 15 | table_stat_autoanalyze_count_ < Table_Name > | autoanalyze count |
Note: pg_stat_statements extension need to be enabled
the below query to list the top 20 query that consume the DB time
select queryid,substring(query,1,60) as query , calls,
round(total_time::numeric, 2) as total_time_Msec,
round((total_time::numeric/1000), 2) as total_time_sec,
round(mean_time::numeric,2) as avg_time_Msec,
round((mean_time::numeric/1000),2) as avg_time_sec,
round(stddev_time::numeric, 2) as standard_deviation_time_Msec,
round((stddev_time::numeric/1000), 2) as standard_deviation_time_sec,
round(rows::numeric/calls,2) rows_per_exec,
round((100 * total_time / sum(total_time) over ())::numeric, 4) as percent
from pg_stat_statements
order by percent desc limit 20;
| # | Counter Metric Name | Description |
|---|---|---|
| 1 | pg_stat_statements_calls_queryid_< queryid > | Number of times executed |
| 2 | pg_stat_statements_total_time_msec_queryid_< queryid > | Total time spent in the statement, in milliseconds |
| 3 | pg_stat_statements_min_time_msec_queryid_< queryid > | Minimum time spent in the statement, in milliseconds |
| 4 | pg_stat_statements_max_time_msec_queryid_< queryid > | Maximum time spent in the statement, in milliseconds |
| 5 | pg_stat_statements_avg_time_msec_queryid_< queryid > | Mean time spent in the statement, in milliseconds |
| 6 | pg_stat_statements_stddev_time_msec_queryid_< queryid > | Population standard deviation of time spent in the statement, in milliseconds |
| 7 | pg_stat_statements_rows_per_exec_queryid_< queryid > | number of rows retrieved or affected per execution |
| 8 | pg_stat_statements_rows_queryid_< queryid > | Total number of rows retrieved or affected by the statement |
| 9 | pg_stat_statements_db_time_percent_queryid_< queryid > | DB time percent consumed by this query |
| 10 | pg_stat_statements_shared_blks_hit_queryid_< queryid > | Total number of shared block cache hits by the statement |
| 11 | pg_stat_statements_shared_blks_read_queryid_< queryid > | Total number of shared blocks read by the statement |
| 12 | pg_stat_statements_shared_blks_dirtied_queryid_< queryid > | Total number of shared blocks dirtied by the statement |
| 13 | pg_stat_statements_shared_blks_written_queryid_< queryid > | Total number of shared blocks written by the statement |
| 14 | pg_stat_statements_local_blks_hit_queryid_< queryid > | Total number of local block cache hits by the statement |
| 15 | pg_stat_statements_local_blks_read_queryid_< queryid > | Total number of local blocks read by the statement |
| 16 | pg_stat_statements_local_blks_dirtied_queryid_< queryid > | Total number of local blocks dirtied by the statement |
| 17 | pg_stat_statements_local_blks_written_queryid_< queryid > | Total number of local blocks written by the statement |
| 18 | pg_stat_statements_temp_blks_read_queryid_< queryid > | Total number of temp blocks read by the statement |
| 19 | pg_stat_statements_temp_blks_written_queryid_< queryid > | Total number of temp blocks written by the statement |
| 20 | pg_stat_statements_blk_read_time_msec_queryid_< queryid > | Total time the statement spent reading blocks, in milliseconds (if track_io_timing is enabled, otherwise zero) |
| 21 | pg_stat_statements_blk_write_time_msec_queryid_< queryid > | Total time the statement spent writing blocks, in milliseconds (if track_io_timing is enabled, otherwise zero) |
There are three type of Dashboard same like the metrics
1- Dashboard for Database performance
2- Dashboard for Table metrics Dashboard (per table)
3- Dashboard for pg_stat_statements (per query id)
the deafult option is to create the dashboard by using the could formation that already provided in the below steps but you still have the option to create custom dashboard please refer to AWS cloud watch documentation
Note: Some metrics use CloudWatch’s Rate Metric Math
CloudWatch’s Rate Metric Math : Returns the rate of change of the metric per second. This is calculated as the difference between the latest data point value and the previous data point value, divided by the time difference in seconds between the two values.
PGCM provide cloud formation template that will provide basics alarms , you can edit the template to add more alarms, customize alarm Threshold and Period, add Notification etc. please refer to AWS cloudwatch Alarms documentation and AWS cloudFormation documentation
| # | Alarm Name | Description |
|---|---|---|
| 1 | PGCM_Status_Alarm | PGCM is not working for 3 Min |
| 2 | invalid_indexes_PGCM_Alarm | invalid indexes count Equal or over 1 for 5 Min |
| 3 | nactive_Replication_Slot_PGCM_Alarm | Inactive Replication Slot count Equal or over 1 for 15 Min |
| 4 | Xid_Wraparound_PGCM_Alarm | Xid Percent Towards Wraparound is 50 % for 15 Min |
cd pgcm
sh pgcm_build.sh
Example of the output
[Mohamed@dev-dsk pgcm]$ sh build.sh
Removeing existing pgcm_1.8.zip file
zip -r pgcm_1.8.zip pgcm.py rds_config.py tables_config.py scramp/ pg8000/ certs/ asn1crypto/
adding: pgcm.py (deflated 91%)
adding: rds_config.py (deflated 52%)
adding: tables_config.py (deflated 53%)
adding: scramp/ (stored 0%)
adding: scramp/__init__.py (deflated 40%)
adding: scramp/core.py (deflated 76%)
adding: scramp/_version.py (deflated 71%)
adding: scramp/utils.py (deflated 56%)
adding: pg8000/ (stored 0%)
adding: pg8000/native.py (deflated 68%)
adding: pg8000/legacy.py (deflated 75%)
adding: pg8000/exceptions.py (deflated 65%)
adding: pg8000/converters.py (deflated 74%)
adding: pg8000/__init__.py (deflated 57%)
adding: pg8000/core.py (deflated 74%)
adding: pg8000/_version.py (deflated 72%)
adding: pg8000/dbapi.py (deflated 76%)
adding: certs/ (stored 0%)
adding: certs/commercial/ (stored 0%)
adding: certs/commercial/rds-ca-2019-root.pem (deflated 29%)
adding: asn1crypto/ (stored 0%)
adding: asn1crypto/algos.py (deflated 82%)
adding: asn1crypto/version.py (deflated 21%)
adding: asn1crypto/pem.py (deflated 71%)
adding: asn1crypto/cms.py (deflated 81%)
adding: asn1crypto/_errors.py (deflated 50%)
adding: asn1crypto/crl.py (deflated 79%)
adding: asn1crypto/__init__.py (deflated 65%)
adding: asn1crypto/core.py (deflated 83%)
adding: asn1crypto/pkcs12.py (deflated 67%)
adding: asn1crypto/_types.py (deflated 55%)
adding: asn1crypto/_ordereddict.py (deflated 64%)
adding: asn1crypto/_inet.py (deflated 74%)
adding: asn1crypto/csr.py (deflated 63%)
adding: asn1crypto/_int.py (deflated 47%)
adding: asn1crypto/util.py (deflated 79%)
adding: asn1crypto/x509.py (deflated 80%)
adding: asn1crypto/parser.py (deflated 74%)
adding: asn1crypto/_iri.py (deflated 70%)
adding: asn1crypto/_teletex_codec.py (deflated 77%)
adding: asn1crypto/tsp.py (deflated 76%)
adding: asn1crypto/pdf.py (deflated 66%)
adding: asn1crypto/keys.py (deflated 81%)
adding: asn1crypto/ocsp.py (deflated 83%)
Generated new Lambda file pgcm_1.8.zip
Archive: pgcm_1.8.zip
Length Date Time Name
--------- ---------- ----- ----
123698 03-26-2021 18:25 pgcm.py
935 03-26-2021 18:25 rds_config.py
343 03-26-2021 18:25 tables_config.py
0 03-26-2021 18:25 scramp/
176 03-26-2021 18:25 scramp/__init__.py
18205 03-26-2021 18:25 scramp/core.py
18516 03-26-2021 18:25 scramp/_version.py
655 03-26-2021 18:25 scramp/utils.py
0 03-26-2021 18:25 pg8000/
7569 03-26-2021 18:25 pg8000/native.py
24407 03-26-2021 18:25 pg8000/legacy.py
940 03-26-2021 18:25 pg8000/exceptions.py
17071 03-26-2021 18:25 pg8000/converters.py
4118 03-26-2021 18:25 pg8000/__init__.py
33529 03-26-2021 18:25 pg8000/core.py
15795 03-26-2021 18:25 pg8000/_version.py
27190 03-26-2021 18:25 pg8000/dbapi.py
0 03-26-2021 18:25 certs/
0 03-26-2021 18:25 certs/commercial/
1456 03-26-2021 18:25 certs/commercial/rds-ca-2019-root.pem
0 03-26-2021 18:25 asn1crypto/
35611 03-26-2021 18:25 asn1crypto/algos.py
152 03-26-2021 18:25 asn1crypto/version.py
6145 03-26-2021 18:25 asn1crypto/pem.py
27294 03-26-2021 18:25 asn1crypto/cms.py
1070 03-26-2021 18:25 asn1crypto/_errors.py
16104 03-26-2021 18:25 asn1crypto/crl.py
1219 03-26-2021 18:25 asn1crypto/__init__.py
170559 03-26-2021 18:25 asn1crypto/core.py
4566 03-26-2021 18:25 asn1crypto/pkcs12.py
939 03-26-2021 18:25 asn1crypto/_types.py
4533 03-26-2021 18:25 asn1crypto/_ordereddict.py
4661 03-26-2021 18:25 asn1crypto/_inet.py
2142 03-26-2021 18:25 asn1crypto/csr.py
494 03-26-2021 18:25 asn1crypto/_int.py
21873 03-26-2021 18:25 asn1crypto/util.py
93421 03-26-2021 18:25 asn1crypto/x509.py
8853 03-26-2021 18:25 asn1crypto/parser.py
8733 03-26-2021 18:25 asn1crypto/_iri.py
5053 03-26-2021 18:25 asn1crypto/_teletex_codec.py
7827 03-26-2021 18:25 asn1crypto/tsp.py
2250 03-26-2021 18:25 asn1crypto/pdf.py
36788 03-26-2021 18:25 asn1crypto/keys.py
19024 03-26-2021 18:25 asn1crypto/ocsp.py
--------- -------
773914 44 files
[Mohamed@dev-dsk pgcm]$
-> Set AWS_PROFILE as environment variable
export AWS_PROFILE= < >
-> Create S3 bucket called pgcm
aws s3 mb s3://pgcm --profile ${AWS_PROFILE} --output table
-> upload pgcm_< version >.zip to the S3 bucket
aws s3 cp <>.zip s3://pgcm/ --profile ${AWS_PROFILE} --output table
EX:
aws s3 cp pgcm_1.8.zip s3://pgcm/ --profile ${AWS_PROFILE} --output table
-> check the uploaded zip file
aws s3 ls s3://pgcm/ --human-readable --profile ${AWS_PROFILE} --output table
Note:
- To be able to finish the next steps you need to get some information about the database like VPC , Security Group , Port and DB Resource Id.
- you will need the DB Resource Id if you will use the IAM DB Auth
set environment variables:
-> Set AWS_PROFILE as environment variable
export AWS_PROFILE= < >
-> set the RDS PostgreSQL DB INSTANCE IDENTIFIER as environment variable
export DB_INSTANCE_IDENTIFIER=
-> set AWS Region as environment variable
export REGION=
-> run below AWS cli to get the infomation
aws rds describe-db-instances --db-instance-identifier ${DB_INSTANCE_IDENTIFIER} --profile ${AWS_PROFILE} | grep VpcId
aws rds describe-db-instances --profile ${AWS_PROFILE} | grep VpcSecurityGroupId
aws rds describe-db-instances --db-instance-identifier ${DB_INSTANCE_IDENTIFIER} --profile ${AWS_PROFILE} | grep SubnetIdentifier
aws rds describe-db-instances --db-instance-identifier ${DB_INSTANCE_IDENTIFIER} --profile ${AWS_PROFILE} | grep -w "Port"
aws rds describe-db-instances --db-instance-identifier ${DB_INSTANCE_IDENTIFIER} --profile ${AWS_PROFILE} | grep DbiResourceId
-> use the above information to set below environment variables
export VPC_ID= < >
export DB_PORT= < >
export SUBNET_ID_1= < >
export SUBNET_ID_2= < >
export SUBNET_ID_3= < >
export SUBNET_ID_4= < >
export SECURITYGROUPID= < >
aws ec2 create-vpc-endpoint \
--vpc-endpoint-type Interface \
--vpc-id ${VPC_ID} \
--subnet-ids ${SUBNET_ID_1} ${SUBNET_ID_2} ${SUBNET_ID_3} \
--security-group-id ${SECURITYGROUPID} \
--service-name com.amazonaws.${REGION}.monitoring \
--private-dns-enabled \
--profile ${AWS_PROFILE} --output table
aws ec2 describe-vpc-endpoint-services \
--service-names com.amazonaws.${REGION}.monitoring \
--profile ${AWS_PROFILE} --output table
1.4- Create VPC Endpoint for AWS Secrets Manager if you will use Secrets Manager as authentication type
aws ec2 create-vpc-endpoint \
--vpc-endpoint-type Interface \
--vpc-id ${VPC_ID} \
--subnet-ids ${SUBNET_ID_1} ${SUBNET_ID_2} ${SUBNET_ID_3} \
--security-group-id ${SECURITYGROUPID} \
--service-name com.amazonaws.${REGION}.secretsmanager \
--private-dns-enabled \
--profile ${AWS_PROFILE} --output table
aws ec2 describe-vpc-endpoint-services \
--service-names com.amazonaws.${REGION}.secretsmanager \
--profile ${AWS_PROFILE} --output table
To allow lambda to connect the DB and also allow lambda to connect to cloud watch from the same VPC .
Note: Assuming that you have one Security Group for all the databases in same region and using same port for all the databases , if you are using diffrent port or Security Group you have to update each Security Group with the DB port
-> get the VPC CIDR
aws ec2 describe-vpcs \
--vpc-ids ${VPC_ID} \
--query "Vpcs[*].[CidrBlock]" \
--profile ${AWS_PROFILE} --output table
-> set below env variable
export VPC_CIDR= < >
echo $VPC_CIDR
-> Add cloud watch port
aws ec2 authorize-security-group-ingress \
--group-id ${SECURITYGROUPID} \
--protocol tcp \
--port 443 \
--cidr ${VPC_CIDR} \
--profile ${AWS_PROFILE} --output table
-> Add the DB port
aws ec2 authorize-security-group-ingress \
--group-id ${SECURITYGROUPID} \
--protocol tcp \
--port ${DB_PORT} \
--cidr ${VPC_CIDR} \
--profile ${AWS_PROFILE} --output table
aws ec2 describe-security-groups --group-ids ${SECURITYGROUPID} \
--profile ${AWS_PROFILE} --output table
Notes:
- The default database user will be user_pgcm if you want to change it you need to edit the DB user creation script
- you need psql to be able to connect to the postgresql DB and run DB user creation script
--> If the AWS Secrets Manager or username/password will be used as authentication type then use create_database_user_pwd_pgcm.sql to create the DB user .
login to the DB using the master user then execute below script
cd scripts
psql -h [hostname or RDS endpoint] -p [Port] -d [Database name ] -U [user name]
\i create_database_user_pwd_pgcm.sql
--> If IAM Database Authentication will be used as authentication type then use create_database_user_iam_pgcm.sql script to create the DB user .
1- Enabling IAM database authentication
you have to Enable the IAM database authentication to use the IAM Database Authentication. please refere to RDS Doc for how to enable IAM DB authentication
Also you you can use AWS CLI to check and enable the IAMDB DB authentication
-> Set AWS_PROFILE as environment variable
export AWS_PROFILE= < >
For Aurora PostgreSQL:
-> set Aurora PostgreSQL CLUSTER IDENTIFIER as environment variable
export APG_CLUSTER_IDENTIFIER= < >
-> To check the IADM DB authentication stattus
aws rds describe-db-clusters --db-cluster-identifier ${APG_CLUSTER_IDENTIFIER} \
--profile ${AWS_PROFILE} --output table | grep IAMDatabaseAuthenticationEnabled
-> Enable the IAMDB DB authentication
aws rds modify-db-cluster --db-cluster-identifier ${APG_CLUSTER_IDENTIFIER} \
--enable-iam-database-authentication --apply-immediately \
--profile ${AWS_PROFILE} --output table
aws rds describe-db-clusters --db-cluster-identifier ${APG_CLUSTER_IDENTIFIER} --profile ${AWS_PROFILE} --output table | grep available
For RDS PostgreSQL:
-> Set RDS PostgreSQL DB INSTANCE IDENTIFIER as environment variable
export DB_INSTANCE_IDENTIFIER= < >
-> Enable the IAMDB DB authentication
aws rds describe-db-instances --db-instance-identifier ${DB_INSTANCE_IDENTIFIER} \
--profile ${AWS_PROFILE} --output table | grep IAMDatabaseAuthenticationEnabled
aws rds modify-db-instance --db-instance-identifier ${DB_INSTANCE_IDENTIFIER} \
--enable-iam-database-authentication --apply-immediately \
--profile ${AWS_PROFILE} --output table
2- Create DB user :
login to the DB using the master user then execute below script
cd scripts
psql -h [hostname or RDS endpoint] -p [Port] -d [Database name ] -U [user name]
\i create_database_user_iam_pgcm.sql
This cloudformation will create below
1- lambda function
2- IAM role and policies
3- AWS secret if you AWS Secrets Manager got selected as authentication type
4- CLoud watch Dashbord for the database Metrics
cloud formation template location and name : CF/PGCM_lambda_CF.yaml
Creating a stack using the AWS CloudFormation console
1- Open the AWS CloudFormation console at https://console.aws.amazon.com/cloudformation and select Create a new stack
2- choose a stack template:
On the Specify template page, choose a stack template by Uploading a template file Select a CloudFormation template on your local computer.
3- on Specify stack details
Enter the stack name : < DB_INSTANCE_IDENTIFIER >-PGCM
then update the Parameters
This cloudformation will create CLoud watch Dashbord for table metric. You need to use this cloudformation for each table you need to creat dashboard of it .
cloud formation template location and name : CF/PGCM_Table_metrics_Dashboard.yaml
1- Open the AWS CloudFormation console at https://console.aws.amazon.com/cloudformation and select Create a new stack
2- choose a stack template: On the Specify template page, choose a stack template by Uploading a template file Select a CloudFormation template on your local computer.
3- on Specify stack details Enter the stack name : < DB_INSTANCE_IDENTIFIER >-< Table_Name >-Table-PGCM
then update the Parameters
use below query to list the top 20 query that consume the DB time and select the query ID that you want to create Dashboared for
select queryid,substring(query,1,60) as query , calls,
round(total_time::numeric, 2) as total_time_Msec,
round((total_time::numeric/1000), 2) as total_time_sec,
round(mean_time::numeric,2) as avg_time_Msec,
round((mean_time::numeric/1000),2) as avg_time_sec,
round(stddev_time::numeric, 2) as standard_deviation_time_Msec,
round((stddev_time::numeric/1000), 2) as standard_deviation_time_sec,
round(rows::numeric/calls,2) rows_per_exec,
round((100 * total_time / sum(total_time) over ())::numeric, 4) as percent
from pg_stat_statements
order by percent desc limit 20;
cloud formation template location and name : CF/PGCM_QueryId_metrics_Dashboard.yaml
1- Open the AWS CloudFormation console at https://console.aws.amazon.com/cloudformation and select Create a new stack
2- choose a stack template: On the Specify template page, choose a stack template by Uploading a template file Select a CloudFormation template on your local computer.
3- on Specify stack details
Enter the stack name : < DB_INSTANCE_IDENTIFIER >- < query id >-queryid-PGCM
then update the Parameters
Notes: this cloud formation template will provide basics alarms , you can edit the template to add more alarms, customize alarm Threshold and Period, add Notification etc.
cloud formation template location and name : CF/pgcm_alarm_cf.yaml
1- Open the AWS CloudFormation console at https://console.aws.amazon.com/cloudformation and select Create a new stack
2- choose a stack template: On the Specify template page, choose a stack template by Uploading a template file Select a CloudFormation template on your local computer.
3- on Specify stack details
Enter the stack name : < DB_INSTANCE_IDENTIFIER >-PGCM-Alarm
then update the Parameters
- will PGCM change or access my data ?
No, PGCM will not change or access your data, PGCM will read postgresql performance data only as PCGM DB user have only pg_monitor role pg_monitor Read/execute various monitoring views and functions. This role is a member of pg_read_all_settings, pg_read_all_stats and pg_stat_scan_tables.
- will PGCM flood my DB with many connection ?
No, PGCM DB user is limited by two session only so PGCM will not flood your DB with connection PGCM will start to fail if there is 2 session in the DB
- will PGCM cause any long running query ?
No , PGCM will set statement_timeout = 10sec in session level , if there is any query take more than 10 sec it will be terminated automatically
- Dose PGCM support/use SSL ?
Yes it does, PGCM use RDS SSL by default and it is using the following certificate https://s3.amazonaws.com/rds-downloads/rds-ca-2019-root.pem.
Root cert location and name : pgcm/certs/commercial/rds-ca-2019-root.pem
if you want to use different certificate please refer to RDS docs https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/UsingWithRDS.SSL.html
please update pgcm/rds_config.py with the new certificate name
# RDS CA CERT
# for more info https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/UsingWithRDS.SSL.html
CA_CERT="certs/commercial/rds-ca-2019-root.pem"
See CONTRIBUTING for more information.
This project is licensed under the Apache-2.0 License.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent