Performanсe tuning should be performed during application development and include accurate choice of hardware (for example, estimating number of CPUs and memory per Shardman cluster node, tuning your storage), OS tuning (for example, tuning swappiness or network-related behavior) and DBMS tuning (choosing efficient configuration). But first of all application should be tested and tuned for distributed DBMS. This includes designing distributed schema (or translating existing schema to distributed one), tuning queries, using connection poolers, caching and even checking performance issues related to possible serialization errors or Shardman node outage. The design of schema should include accurate selection of sharding key, decision, which tables should become global. Usually you select sharding key so that

These rules allow Shardman to efficiently exclude shards from queries and to push down joins to replication groups where data reside.

Each Shardman node operates as a usual DBMS server, so all standard recommendations for tuning PostgreSQL for production load remain in place. You should select shared_buffers, work_mem, effective_cache_size depending on resources, available to DBMS. Take in mind that Repfactor instances run on a single node. When all cluster nodes are on-line, replicas shouldn't utilize a lot of CPUs. However, in case of node failure masters for Repfactor replication groups can become running on one server, which can create significant load on it. While tuning max_connections parameter note that each transaction can initiate n-1 connections, where n is the number of replication groups in the cluster. In case when Silk is enabled, it's still true for transactions, containing DML. In case when Silk is disabled, it's also true for read-only transactions.

Another parameters, which you perhaps whould like to tune are foreign server options. They can be set in FDWOptions section of Shardman configuration file. Parameters, which significantly affect Shardman performance are fetch_size, batch_size and async_capable. When Silk transport is not enabled, fetch_size determines the number of records, which are fetched from a remote server at once. When Silk transport is enabled, fetch_size currently doesn't have significant impact on the query execution. batch_size specifies how many rows can be combined in a single remote INSERT operation. async_capable allows asynchronous execution and should always be turned on (which is the default).