Scaling PostgreSQL with Pgpool and PgBouncer
Deploying PostgreSQL in a high-demand environment requires reliability and scalability. PostgreSQL’s ecosystem offers the tools you need to build out a robust database system. This guide offers a high-level description of tools used to build a high-availability, scalable, fault-tolerant service.
These tools are explained:
- PostgreSQL Database with Streaming Replication
- Pgpool-II multipurpose connect proxy for PostgreSQL
- PgBouncer connection pooler for PostgreSQL
- Repmgr - Replication Manager for PostgreSQL Clusters
A fault-tolerant system does not just deploy a single PostgreSQL server, rather an PostgresSQL cluster is deployed.
A PostgreSQL Cluster consists of a master PostgreSQL server, and one or more replication slaves. Writes are send to the master, and all slave servers are available to serve read requests. If the master fails, one slave can be promoted to be the new master.
Software like Pgpool can be deployed to take advantage of the cluster:
- sending writes (create, update, delete) to the master
- load balancing read operations across all servers
- detecting a failure of any cluster.
When you deploy your cluster, should provision as many slaves as can handle the load should a server go off-line, a “n+1” scenario. When you lose a server, you need enough capacity to handle the load with the remaining servers.
The master sends the WAL to each slave over the network. The slaves apply the WAL feed to stay current, and in a “hot standby” state, should the master fail. The slaves can’t perform any updates of the own, but can serve read-only queries to its clients.
PostgreSQL has many replication solutions with different approaches. In fact, until release 9.0, PostgreSQL did not offer a built-in, official version. The reason? PostgreSQL core team member Bruce Momjian spoke at the Philadelphia Linux User’s Group a couple years back and explained that replication is not a “one size fits all” approach. There are many uses of replication, from salesmen out in the field wielding laptops, to application-specific requirements to a “Hot Standby” failover server.
PostgreSQL 9.0 introduced the built-in Streaming Replication (SR). SR transmits changes from the WAL to multiple slave PostgreSQL servers. Eash slave applies that stream to their data, keeping it an exact copy of the data, and staying in a “Hot Standby” mode, ready at a moment’s notice to be promoted to a master, should the master fail.
As a bonus, slave servers can also serve read-only requests from its database because of its relative low-latency, and thus can be used in load balancing.
The WAL, or Write Ahead Log, is the feature of PostgreSQL that allows it to recover data, usually up to the point where the server stopped (from hardware, software, or human error). As you make changes to your data, PostgreSQL aggressively writes those changes to the WAL. This is not a human-readable log like a web server would produce. The WAL is an internal, binary log of all committed changes to the database.
When a maximum time limit has passed, or the buffer limit is reached, PostgreSQL issues a Checkpoint. During the checkpoint, dirty buffers (containing changed data pages) are flushed to disk to ensure against data loss, without overloading I/O devices with constant writes.
This design allows recovery, should a failure (power, server, human error) occur. When PostgreSQL restarts, it replays the changes from the WAL since the last Checkpoint, to bring the database back to the state of the last completed commit.
Under SR, the master database feeds your slave database(s) a live stream of changes from the Write Ahead Log (WAL). The slaves apply this data and stay “up to date” within a reasonable latency.
Read more about setting up replication on the PostgreSQL Streaming Replication wiki page.
Pgpool, the hardest to grasp. It can be configured to perform connection pooling and management, simple replication, load balancing, and parallel query processing.
Pgpool is a middleware database utility that can perform several functions, including:
- Connection Pooling
- Pgpool Replication
- Load Balancing
- Failover Handling
- Parallel Query
Middleware is software siting between the PostgreSQL client and server. It mimics the PostreSQL server API to the clients, and speaks the client API to the actual server, thus adding a level of intelligence in the middle of the call chain. Layers of services can be chained between the two endpoint, each providing a new feature.
PgPool with PostgreSQL Clusters
For use in a PostgreSQL Cluster, a PgPool server sits between the clients and the master and slave servers.
Clients connect to Pgpool instead of the database server and send database requests through Pgpool to the servers in the cluster. Configure with “Connection Pooling.”
Pgpool sends all data mutation requests (update, create, delete, etc.) through to the master server, and sends read requests (select) to any available server, master or slave. Configure with “load balancing.”
When Pgpool detects the master server has failed, it can issue a command to promote a slave to be the next master. (Regmgr has better features for managing this.) Configure with “failover.”
For connection pooling, Pgpool is configured to point to a specific PostgreSQL host (and port).
Connections are unique by database name and user. A Pgpool server (hostname + port) can connect only to one PostgreSQL server, but can “pool” connections to all databases within that cluster.
Strictly speaking, this is really connection caching, and will reuse a previous connection or open a new one. Older, unused connections are closed to use the resources for a newer connection. Unused connections timeout after a given time, and are closed to conserve resources.
Pooling usually indicates a set of connections to a database, reused by the application or client. PgPool does not commit to a per-database set of connection, but caches requested access. If there is only one hosted database on a host, and an application uses a single user to connect to it, then it may appear as such a pool.
Pgpool intercepts all connection requests for that server and opens a connection to the backend server with that database name and user (Dbname + User). After the client closes the connection, Pgpool keeps it open, waiting for another client to connect.
PgPool Replication sends all write requests to all servers in the cluster. This is an alternative to Streaming Replication, and can be used where SR is not available. However, it has a higher overhead, and large write transactions will reduce performance.
Using PgPool Replication, all servers in the cluster run in normal mode, and there is no need for a failover event should one server fail. The other servers will pick up the load.
Streaming Replication is still recommended for high-volume applications.
PgPool load balancing splits read requests between all servers in the replicated cluster. Some replication schemes do not make the slave available for reads, so are not candidates for this feature.
To setup, you define your master database (named backend_host0) and and slaves (backend_host1..n), then enable load balancing. You must also list any functions you use that will back data changes, to pgpool can identify write requests and read requests.
Database triggers, stored procedures that execute automatically when an insert, update, or delete operation to a given table, execute only on the master. Those changed are streamed down to the slaves; the slaves do not run any triggers themselves, only apply any trigger changes from upstream.
For streaming replication, postgresql will send all write requests to the master server. (For simple replication, it would send them to all servers.)
Read requests are balanced between the master and the slave servers. If a slave server falls behind on its WAL stream updates, it will be temporarily removed from the load balance set until it catches up. This ensures data is as up-to-date as you need.
Pgpool can also be configured to detect a failure on the master postgresql in the cluster and take an action that can promote a slave to be the new master. After that point, it will forget the old master and talk to the new master instead.
This can be tricky to have pgpool make this decision itself. Perhaps you want to have a manual intervention depending on your operations. Once a master is demoted, its data must be rebuilt from the new master to become a master again.
Parallel Query for Sharding and Partitioning
For large data sets that span servers, you can “shard” the tables, splitting them up by a key column. Pgpool uses a configuration table containing mapping the values of this column to a postgres server or cluster.
When the client makes a data request, Pgpool inspects the query, looks up the home location of the record, and forwards the request to that server.
Configuring for use in a cluster
The Pgpool-II User Manual contains details of setting up Pgpool for use with a cluster. You will need to set up:
- Connection Pooling mode (optional)
- Streaming Replication
- Master/Slave mode
- Load balancing with streaming replication
- Failover with Streaming Replication
PgBouncer is an alternative connection pooling (again, caching) middleware to Pgpool. It is smaller in footprint and only does pooling, so it conserves resources and can be more efficient. It can cache connections to different databases, servers, or clusters (with Pgpool).
In PgBouncer, you configure pgbouncer as a postgresql connector. It maps the dbnames you connect to locally into real databases that can live on multiple hosts thoughout your system.
A database connection is configured as:
app_db = host=pg1 port=nnnn dbname=app user=uuuu ... other_db = host=pg2 port=nnnn dbname=whatever user=uuuu ...
When a client connects to “app_db” it will proxy and forward the request to the real location of the database. After the client closes the connection, it will keep it open for reuse from another client, or until it times out. Pgbouncer can also maintain an actual pool of connections for each database entry, limiting the amount of outbound connections to a database.
PgBouncer is a great choice when you want to pool connections to multiple postgresql servers. If you still need the load balancing, replication, or failover features of Pgpool, you can use both middlewares in series.
- Application Client Connects to PgBouncer
- PgBouncer forwards request to a PgPool for that cluster
- PgPool forwards Request to the PostreSQL server (master or slave)
- PostgreSQL responds to the request
Pgpool vs. PgBouncer
Sometimes, a full-blown, streaming replication service isn’t what you need. A good question is “Which shall I deploy with PostgreSQL?”
Use connection pooling if:
- You want to eliminate the overhead of creating and tearing down new connections, particularly over a network interface. If that is all new need, PgBouncer is a better choice.
- You have a lot of short-lived application runs that open/run/close the connections.
Use PgBouncer if:
- You only need connection pooling.
- Your resources are constrained. Pgbouncer does not fork a new process (it is event-based).
- You want to connect to a PostgreSQL cluster, but also other PostgreSQL servers or clusters
- You want to move database connection credential from the application configuration into the middleware
- Allow you to move databases around more transparently without changing every application configuration
- You want to query it for statistics instrumentation.
Use Pgpool if:
- you want to leverage your cluster for load balancing and failover
- you want Pgpool’s replication or parallel query features
- you still need connection pooling on top of this.
Use both if:
- You have clusters deployed, but also want to control multi-db access
- Have lots of connections to pool (use PgBouncer for less resource usage) and want to connect to a Pgpool-facing cluster.
Repmgr sets up your cluster replication and provides a daemon that monitors the nodes in your cluster for failure.
First, you create replicated nodes from your original master. It copies the underlying files from that server to another, which is run as a slave. You designate the master and standby (or slave) nodes.
On failure, it can promote a slave to be the next master, takes the old master out of the cluster until it can be repaired, and tells the other slaves to follow the newly promoted master node.
You can re-provision a new (or old master) node to and introduce it to the cluster.