All about Logical Replication in Postgres.

What is Logical Replication

Logical replication in PostgreSQL is a feature that allows you to replicate data from one database to another, in a flexible and customizable way. Unlike physical replication, which replicates the entire database and its structure, logical replication allows you to specify which tables and columns you want to replicate, and how you want the data to be replicated. This means that you can replicate only the data that is relevant to your application, and you can apply custom transformations or filters to the replicated data to meet your specific needs.

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Vacuuming in PostgreSQL

Is Vacuuming important in Postgresql

PostgreSQL is a database management system, and as such, vacuuming is an important maintenance task to help keep the database running efficiently. Vacuuming helps to clean up dead tuples (rows that have been deleted or obsoleted) and free up space on the database, which can help to improve performance and prevent the database from becoming bloated or fragmented. It’s generally recommended to vacuum a PostgreSQL database regularly to help maintain its health and performance.

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All about Table Partitioning in PostgreSQL

What is Partitioning in PostgreSQL

Partitioning in PostgreSQL is a technique for dividing a large table into smaller, more manageable pieces called partitions. Each partition contains a subset of the data in the table, and the data in each partition is organized and stored in a way that is optimized for the specific subset of data it contains.

Overall, partitioning is a useful technique for dividing large tables into smaller, more manageable pieces in PostgreSQL. It can improve the performance and manageability of the table, and can make it easier to work with large datasets in your database.

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How do you identify high load tables/indexes in Postgres

To identify high-load tables or indexes in Postgres, you can use the pg_stat_all_tables or pg_stat_all_indexes views. These views provide information about the access statistics for each table and index in a database. To access these views, you can connect to your Postgres database using a tool like psql and then run a query like the following:

This will return a list of all tables in the database, along with information about their size, the number of reads and writes performed on the table, and the amount of time spent reading and writing to the table. You can then sort the results by the number of reads or writes to identify the tables that are receiving the most traffic.

For indexes, you can use a similar query to pg_stat_all_indexes:

This will return a list of all indexes in the database, along with information about the number of reads and writes performed on each index, and the amount of time spent reading and writing to the index. You can then sort the results by the number of reads or writes to identify the indexes that are receiving the most traffic.

It’s worth noting that these views only provide information about the activity on the tables and indexes since the last time the database was started. To get a more comprehensive view of the activity on your tables and indexes, you can collect this information over time and analyze it using tools like Graphite or Grafana. This will allow you to see trends and patterns in the usage of your tables and indexes, and identify any potential performance bottlenecks.

How can you find the top 5 most heavily used tables for the last week in a postgres database?

To find the top 5 most heavily used tables in a Postgres database for the last week, you would need to collect usage statistics for each table over time and then analyze the data to identify the tables that received the most traffic. Here are the steps you would need to follow to do this:

  1. First, connect to your Postgres database using a tool like psql.
  2. Next, create a new table to store the usage statistics for each table. You can do this by running a CREATE TABLE statement like the following:

CREATE TABLE table_usage_stats (

    table_name text,

    reads bigint,

    writes bigint,

    read_time double precision,

    write_time double precision,

    date timestamp

);

  1. Next, you need to create a script that will collect the usage statistics for each table in the database and insert them into the table_usage_stats table. You can do this by running a SELECT query against the pg_stat_all_tables view, which provides information about the access statistics for each table in the database. The query might look something like this:

INSERT INTO table_usage_stats (table_name, reads, writes, read_time, write_time, date)

SELECT relname, heap_blks_read, heap_blks_hit, heap_blks_hit * avg_blk_read_time, heap_blks_hit * avg_blk_write_time, NOW()

FROM pg_stat_all_tables;

  1. To collect the usage statistics on a regular basis, you can set up a cron job or use a tool like pg_cron to run the script at regular intervals (e.g. once per hour).
  2. After collecting the usage statistics for a week, you can then query the table_usage_stats table to identify the top 5 most heavily used tables. You can do this by running a SELECT query that uses the SUM function to total the number of reads and writes for each table, and then sorts the results by the total number of reads and writes. The query might look something like this:

SELECT table_name, SUM(reads) + SUM(writes) AS total_activity

FROM table_usage_stats

WHERE date >= NOW() – INTERVAL ‘1 week’

GROUP BY table_name

ORDER BY total_activity DESC

LIMIT 5;

This query will return the names of the top 5 tables that received the most reads and writes over the past week, along with the total number of reads and writes performed on each table.

It’s worth noting that this approach assumes that you are collecting usage statistics for each table on a regular basis and storing them in a dedicated table. If you are not doing this, you would need to modify the steps above to collect the usage statistics for the last week and then analyze them to identify the top 5 most heavily used tables.

Are there any tools that can automate this?

There are tools that can automate the process of collecting usage statistics for tables and indexes in a Postgres database and analyzing the data to identify potential performance bottlenecks. One example of such a tool is pgwatch2, which is an open-source monitoring and alerting tool for Postgres databases.

pgwatch2 can collect a wide range of metrics from a Postgres database, including metrics on table and index usage, and then display the data in easy-to-read charts and graphs. It can also alert you if certain metrics exceed specified thresholds, so you can quickly identify and address any potential performance issues.

To use pgwatch2, you would need to install it on a server that has access to your Postgres database, and then configure it to collect the metrics you are interested in. You can find more information about pgwatch2, including installation instructions, on the project’s GitHub page: https://github.com/cybertec-postgresql/pgwatch2.

There are many other tools that can automate the process of collecting and analyzing usage statistics for tables and indexes in a Postgres database. Some other examples of tools that you could use for this purpose include:

  • pgbadger: pgbadger is an open-source tool that analyzes the log files generated by a Postgres database and generates reports on the database’s activity. It can provide detailed information on the queries that are executed on the database, including the tables and indexes that are used in each query.
  • pgmetrics: pgmetrics is an open-source tool that collects a wide range of metrics from a Postgres database, including metrics on table and index usage. It can display the data in easy-to-read charts and graphs, and can also alert you if certain metrics exceed specified thresholds.
  • pg_activity: pg_activity is an open-source tool that provides a real-time view of the activity on a Postgres database, including information on table and index usage. It can display the data in easy-to-read tables and graphs, and can also alert you if certain metrics exceed specified thresholds.

You can find more information about these and other tools on the Postgres website: https://www.postgresql.org/docs/current/monitoring.html.

How can I secure a Postgres Database

There are several steps you can take to secure a Postgres database. Here are a few:

  1. Use a strong password for the database administrator account to prevent unauthorized access.
  2. Enable SSL connections to encrypt data transmitted between the database server and client applications.
  3. Configure your database server to listen on a private network interface or localhost, instead of all interfaces. This will prevent remote connections to the database server.
  4. Use the built-in Postgres role-based access control (RBAC) system to grant specific users access to specific database objects and operations.
  5. Regularly back up your database to protect against data loss and enable recovery in case of a disaster.
  6. Use Postgres’ built-in auditing capabilities to track and log access to the database, so you can monitor for suspicious activity and quickly identify and respond to security threats.
  7. Keep your Postgres installation and all database extensions up to date with the latest security patches to protect against known vulnerabilities.
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When and How do you use pg_rewind() in PostgreSQL?

The pg_rewind utility in PostgreSQL is used to synchronize a standby server with a new master server in a scenario where the old master server has been promoted to a new master server and the standby server needs to catch up with the new master server’s state. This utility can be used when the old master server still has the transaction logs (WAL) that were generated after the promotion, and it can be used to “rewind” the state of the standby server to the point in time where the promotion occurred.

To use the pg_rewind utility, you need to perform the following steps:

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How do I Re-Attach a Master once a Replica has been Promoted in Postgres

To reattach a master server once a replica has been promoted in PostgreSQL, you need to perform the following steps:

  1. On the new master server, edit the postgresql.conf configuration file and set the wal_level parameter to hot_standby. This will enable the new master server to keep a sufficient amount of transaction log data (WAL) to allow the old master server to connect and apply the changes.
  2. Restart the new master server for the changes to take effect.
  3. On the old master server, edit the postgresql.conf configuration file and set the hot_standby parameter to on. This will enable the old master server to connect to the new master server and start receiving the changes.
  4. In the recovery.conf file on the old master server, add the following lines to specify the connection details for the new master server:
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