[bjt]

At work we're in the midst of rolling out a sharded Postgres platform based on http://www.craigkerstiens.com/2012/11/30/sharding-your-datab..., with the sharding implemented at the application level. The biggest piece of complexity in that post is around designing the sharding in such a way that you can gracefully add more shards later.

Having read the pg_shard readme, it's not clear to me how it addresses that issue. I'd need to have a really clear idea how to handle scaling my cluster before committing to a sharding solution.

[ozgune]

We'll update our FAQ with a detailed answer to this question.

As a summary, the user specifies the shard and replication count as they are sharding their table. For example, if you have 4 nodes, you may pick 256 as the initial shard count. That way, you'll have ample room to grow as you add new nodes to your cluster.

When you pick 256 shards over 4 worker nodes, pg_shard will create them in a round-robin fashion. Shard #1 will go to node A and B, shard #2 will go to node B and C, and so forth. This has the advantage that when one of the worker nodes fail, the remaining 3 nodes evenly take the additional work. Also, when you add a new node to the cluster, you can gradually rebalance some of the shards by moving them to the new node.

[scanr]

We have used sharding for managing time series data e.g. 1 shard per day. Is there a way this could work i.e. where the number of shards continually grows?

[jasonmp85]

That use case isn't yet handled by pg_shard: the plugin currently supports only hash partitioning and what you've described is range partitioning. This is certainly on our immediate feature list, as range and hash partitioning cover a variety of use cases.

However, CitusDB does support range partitioning and has a \stage command that will create new shards from incoming data. If you periodically load data that corresponds to a particular time range (hour, day, week), CitusDB can easily handle creation of additional shards during each load.

[Spoutingshite]

In our experience, sharding at the application level is to be avoided.

We implemented application level sharding a few years ago when we had 1Tb of data. We now have over 20Tb of data and application level business logic is killing us.

Our pain is not DB speed, but application complexity. All of our applications (backup, web, maintenance, archiving etc) all need the business logic to traverse the sharding set.

We are planning to move to DB level sharding in January (as part of a larger application and infrastructure refresh).