This adds the capability to the engine to force a full compaction
to be scheduled. When called, it snapshots any data in the cache,
aborts running compactions and prevents level plans from returning
level plans.
The previous sha was taken from a revision on a devel branch that I
thought would continue staying in the tree after it was merged. That
revision was rebased away and the API was changed for the logger.
This updates the usage of the logger and adds a simple package for
constructing the base logger.
The 1.0 version of zap changed the format of the default console logger
so this change moves over to this new logger instead of attempting to
retain backwards compatibility with the old format.
* batch cursors return slices of timestamps and values to reduce call
overhead. Significantly improved iteration.
* added CreateCursor API to Shard, Engine
* moved build*Cursor to code gen
* Introduces EXPLAIN ANALYZE command, which
produces a detailed tree of operations used to
execute the query.
introduce context.Context to APIs
metrics package
* create groups of named measurements
* safe for concurrent access
tracing package
EXPLAIN ANALYZE implementation for OSS
Serialize EXPLAIN ANALYZE traces from remote nodes
use context.Background for tests
group with other stdlib packages
additional documentation and remove unused API
use influxdb/pkg/testing/assert
remove testify reference
This changes the compaction scheduling to better utilize the available
cores that are free. Previously, a level was planned in its own goroutine
and would kick off a number of compactions groups. The problem with this
model was that if there were 4 groups, and 3 completed quickly, the planning
would be blocked for that level until the last group finished. If the compactions
at the prior level are running more quickly, a large backlog could accumlate.
This now moves the planning to a single goroutine that plans each level in
succession and starts as many groups as it can. When one group finishes,
the planning will start the next group for the level.
This instructs the kernel that it can release memory used by mmap'd
TSM files when they are not actively being used. It the mappings are
use, the kernel will fault the pages back in. On linux, this causes
RES memory to drop immediately when run.
It prints the statistics of each iterator that will access the storage
engine. For each access of the storage engine, it will print the number
of shards that will potentially be accessed, the number of files that
may be accessed, the number of series that will be created, the number
of blocks, and the size of those blocks.
This change provides a clear separation between the query engine
mechanics and the query language so that the language can be parsed and
dealt with separate from the query engine itself.
Measurement name and field were converted between []byte and string
repetively causing lots of garbage. This switches the code to use
[]byte in the write path.
The monitor goroutine ran for each shard and updated disk stats
as well as logged cardinality warnings. This goroutine has been
removed by making the disks stats more lightweight and callable
direclty from Statisics and move the logging to the tsdb.Store. The
latter allows one goroutine to handle all shards.
Each shard has a number of goroutines for compacting different levels
of TSM files. When a shard goes cold and is fully compacted, these
goroutines are still running.
This change will stop background shard goroutines when the shard goes
cold and start them back up if new writes arrive.
This limit allows the number of concurrent level and full compactions
to be throttled. Snapshot compactions are not affected by this limit
as then need to run continously.
This limit can be used to control how much CPU is consumed by compactions.
The default is to limit to the number of CPU available.
They rebased a revision we were previously relying upon that allowed us
to use the vanity name so we are reverting back to an older version with
the old import path.
This adds query syntax support for subqueries and adds support to the
query engine to execute queries on subqueries.
Subqueries act as a source for another query. It is the equivalent of
writing the results of a query to a temporary database, executing
a query on that temporary database, and then deleting the database
(except this is all performed in-memory).
The syntax is like this:
SELECT sum(derivative) FROM (SELECT derivative(mean(value)) FROM cpu GROUP BY *)
This will execute derivative and then sum the result of those derivatives.
Another example:
SELECT max(min) FROM (SELECT min(value) FROM cpu GROUP BY host)
This would let you find the maximum minimum value of each host.
There is complete freedom to mix subqueries with auxiliary fields. The only
caveat is that the following two queries:
SELECT mean(value) FROM cpu
SELECT mean(value) FROM (SELECT value FROM cpu)
Have different performance characteristics. The first will calculate
`mean(value)` at the shard level and will be faster, especially when it comes to
clustered setups. The second will process the mean at the top level and will not
include that optimization.
Edd Robinson
Jason Wilder
Jonathan A. Sternberg
Stuart Carnie
Joe LeGasse
Ben Johnson