When a meta query does not include a time component then it can be
answered exclusively by the index. This should result in a much faster
query execution that if the TSM engine was engaged.
This commit rewrites the following queries such that they make use
of the index where no time component is present:
- SHOW MEASUREMENTS
- SHOW SERIES
- SHOW TAG KEYS
- SHOW FIELD KEYS
* 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 allows the query:
SELECT mean(value) FROM cpu GROUP BY time(1d)
To function in some way that makes sense. The upper limit is implicitly
the `now()` starting time and the lower limit will be whichever interval
the lowest point falls into.
When no lower bound is specified and `max-select-buckets` is specified,
the query will only consider points that would satisfy
`max-select-buckets`. So if you have one point written in 1970, have
another point within the last minute, and then do the above query with
`max-select-buckets` being equal to 10, the older point from 1970 will
not be considered.
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.
Now, the prepared statement keeps the open resource and closing the open
resource created from `Prepare` is the responsibility of the prepared
statement.
This also nils out the local shard mapping after it is closed to prevent
it from being used after it is closed.
The statement rewriting logic should be in the query engine as part of
preparing a query. This creates a shard mapper interface that the query
engine expects and then passes it to the query engine instead of
requiring the query to be preprocessed before being input into the query
engine. This interface is (mostly) the same as the old interface, just
moved to a different package.
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.
With the new shard mapper implementation, regexes were just ignored so
it attempted to look up the field type inside of a measurement with no
name (which cannot possibly exist) so it would think the field didn't
exist and map it as the unknown type.
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
Stuart Carnie
Jonathan A. Sternberg