Nodes and tables in Azure Database for PostgreSQL – Hyperscale (Citus)
The Hyperscale (Citus) hosting type allows Azure Database for PostgreSQL servers (called nodes) to coordinate with one another in a "shared nothing" architecture. The nodes in a server group collectively hold more data and use more CPU cores than would be possible on a single server. The architecture also allows the database to scale by adding more nodes to the server group.
Coordinator and workers
Every server group has a coordinator node and multiple workers. Applications send their queries to the coordinator node, which relays it to the relevant workers and accumulates their results. Applications are not able to connect directly to workers.
Hyperscale (Citus) allows the database administrator to distribute tables, storing different rows on different worker nodes. Distributed tables are the key to Hyperscale (Citus) performance. Failing to distribute tables leaves them entirely on the coordinator node and cannot take advantage of cross-machine parallelism.
For each query on distributed tables, the coordinator either routes it to a single worker node, or parallelizes it across several depending on whether the required data lives on a single node or multiple. The coordinator decides what to do by consulting metadata tables. These tables track the DNS names and health of worker nodes, and the distribution of data across nodes.
There are three types of tables in a Hyperscale (Citus) server group, each stored differently on nodes and used for different purposes.
Type 1: Distributed tables
The first type, and most common, is distributed tables. They appear to be normal tables to SQL statements, but they're horizontally partitioned across worker nodes. What this means is that the rows of the table are stored on different nodes, in fragment tables called shards.
Hyperscale (Citus) runs not only SQL but DDL statements throughout a cluster. Changing the schema of a distributed table cascades to update all the table's shards across workers.
Hyperscale (Citus) uses algorithmic sharding to assign rows to shards. The assignment is made deterministically based on the value of a table column called the distribution column. The cluster administrator must designate this column when distributing a table. Making the right choice is important for performance and functionality.
Type 2: Reference tables
A reference table is a type of distributed table whose entire contents are concentrated into a single shard. The shard is replicated on every worker. Queries on any worker can access the reference information locally, without the network overhead of requesting rows from another node. Reference tables have no distribution column because there's no need to distinguish separate shards per row.
Reference tables are typically small and are used to store data that's relevant to queries running on any worker node. An example is enumerated values like order statuses or product categories.
Type 3: Local tables
When you use Hyperscale (Citus), the coordinator node you connect to is a regular PostgreSQL database. You can create ordinary tables on the coordinator and choose not to shard them.
A good candidate for local tables would be small administrative tables that don't participate in join queries. An example is a users table for application sign-in and authentication.
The previous section described how distributed tables are stored as shards on worker nodes. This section discusses more technical details.
pg_dist_shard metadata table on the coordinator contains a
row for each shard of each distributed table in the system. The row
matches a shard ID with a range of integers in a hash space
SELECT * from pg_dist_shard; logicalrelid | shardid | shardstorage | shardminvalue | shardmaxvalue ---------------+---------+--------------+---------------+--------------- github_events | 102026 | t | 268435456 | 402653183 github_events | 102027 | t | 402653184 | 536870911 github_events | 102028 | t | 536870912 | 671088639 github_events | 102029 | t | 671088640 | 805306367 (4 rows)
If the coordinator node wants to determine which shard holds a row of
github_events, it hashes the value of the distribution column in the
row. Then the node checks which shard's range contains the hashed value. The
ranges are defined so that the image of the hash function is their
Suppose that shard 102027 is associated with the row in question. The row
is read or written in a table called
github_events_102027 in one of
the workers. Which worker? That's determined entirely by the metadata
tables. The mapping of shard to worker is known as the shard placement.
The coordinator node
rewrites queries into fragments that refer to the specific tables
github_events_102027 and runs those fragments on the
appropriate workers. Here's an example of a query run behind the scenes to find the node holding shard ID 102027.
SELECT shardid, node.nodename, node.nodeport FROM pg_dist_placement placement JOIN pg_dist_node node ON placement.groupid = node.groupid AND node.noderole = 'primary'::noderole WHERE shardid = 102027;
┌─────────┬───────────┬──────────┐ │ shardid │ nodename │ nodeport │ ├─────────┼───────────┼──────────┤ │ 102027 │ localhost │ 5433 │ └─────────┴───────────┴──────────┘
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