As of master branch, Citus does all the modifications to replicated tables
(e.g., reference tables and distributed tables with replication factor > 1),
via 2PC and avoids any shardstate=3. As a side-effect of those changes,
handling node failures for replicated tables change.
With this PR, when one (or multiple) node failures happen, the users would
see query errors on modifications. If the problem is intermitant, that's OK,
once the node failure(s) recover by themselves, the modification queries would
succeed. If the node failure(s) are permenant, the users should call
`SELECT citus_disable_node(...)` to disable the node. As soon as the node is
disabled, modification would start to succeed. However, now the old node gets
behind. It means that, when the node is up again, the placements should be
re-created on the node. First, use `SELECT citus_activate_node()`. Then, use
`SELECT replicate_table_shards(...)` to replicate the missing placements on
the re-activated node.
When Citus needs to parallelize queries on the local node (e.g., the node
executing the distributed query and the shards are the same), we need to
be mindful about the connection management. The reason is that the client
backends that are running distributed queries are competing with the client
backends that Citus initiates to parallelize the queries in order to get
a slot on the max_connections.
In that regard, we implemented a "failover" mechanism where if the distributed
queries cannot get a connection, the execution failovers the tasks to the local
execution.
The failover logic is follows:
- As the connection manager if it is OK to get a connection
- If yes, we are good.
- If no, we fail the workerPool and the failure triggers
the failover of the tasks to local execution queue
The decision of getting a connection is follows:
/*
* For local nodes, solely relying on citus.max_shared_pool_size or
* max_connections might not be sufficient. The former gives us
* a preview of the future (e.g., we let the new connections to establish,
* but they are not established yet). The latter gives us the close to
* precise view of the past (e.g., the active number of client backends).
*
* Overall, we want to limit both of the metrics. The former limit typically
* kics in under regular loads, where the load of the database increases in
* a reasonable pace. The latter limit typically kicks in when the database
* is issued lots of concurrent sessions at the same time, such as benchmarks.
*/
- Lots of detail is in src/test/regress/mitmscripts/README
- Create a new target, make check-failure, which runs tests
- Tells travis how to install everything and run the tests
Onder Kalaci
Brian Cloutier