The `cumulative_sum()` function can be used to sum each new point and
output the current total. For the following points:
cpu value=2 0
cpu value=4 10
cpu value=6 20
This would output the following points:
> SELECT cumulative_sum(value) FROM cpu
time value
---- -----
0 2
10 6
20 12
As can be seen, each new point adds to the sum of the previous point and
outputs the value with the same timestamp.
The function can also be used with an aggregate like `derivative()`.
> SELECT cumulative_sum(mean(value) FROM cpu WHERE time >= now() - 10m GROUP BY time(1m)
First Pass at implementing sample
Add sample iterators for all types
Remove size from sample struct
Fix off by one error when generating random number
Add benchmarks for sample iterator
Add test and associated fixes for off by one error
Add test for sample function
Remove NumericLiteral from sample function call
Make clear that the counter is incr w/ each call
Rename IsRandom to AllSamplesSeen
Add a rng for each reducer that is created
The default rng that comes with math/rand has a global lock. To avoid
having to worry about any contention on the lock, each reducer now has
its own time seeded rng.
Add sample function to changelog
Clean up template for fill average
Change fill(average) to fill(linear)
Update average to linear in infuxql spec
Add Integer Tests and associated fixes
Update CHANGELOG for fill(linear)
The subscriber write goroutine would drop points if the write load
was higher than it could process. This could happen with a just
a few writers to the server.
Instead, process the channel with multiple writers to avoid dropping
writes so easily. This also adds some config options to control how
large the channel buffer is as well as how many goroutines are started.
Fixes#7330
The FieldIterator is used to scan over the fields of a point, providing
information, and delaying parsing/decoding the value until it is needed.
This change uses this new type to avoid the allocation of a map for the
fields which is then thrown away as soon as the points get converted
into columns within the datastore.
The decoders were held onto each iterator to avoid creating them all
the time. Some of them have use quite a bit of memory so they can
be expensive to create when querying across many series.
Intead, more them to a re-usable pool where we create the minimum that
could active be in use. This reduces garbage as well as makes the iterators
less expensive to create.
When the planner runs, it needs to determine if any files have tombstones.
The code to determine if a tombstone existed involved stating the .tombstone
file. Since the planner runs very frequently when there are many shards, this
causea a lot of system calls that are unnecessary.
Instead, cache the results of the stats calls and only refresh them when we
haven't checked at least once or we write new tombstone data.
This also caches the results of the TSMReader.Stats call to avoid creating
garbage.
Integer blocks that were run length encoded could produce the wrong
value when read back out because the deltas were not zig zag decoded
before scaling the final value. If the deltas were negative, as would
be seen in a counter that decrements by a constant value, the results
would be random with som negative and positive values.
Fixes#7391
Integer blocks that were run length encoded could produce the wrong
value when read back out because the deltas were not zig zag decoded
before scaling the final value. If the deltas were negative, as would
be seen in a counter that decrements by a constant value, the results
would be random with som negative and positive values.
Fixes#7391
The TagSets function was creating a lot of intermediate maps and
slices to calculate the sorted tag sets. It first creates a map
to group tag sets with their series, it then created an equally
sized slice of the tag keys and sorted then. Finally, it created
a new slice and added the tag sets in the original map by the ordering
of the sorted keys. It was also recreating the tags map multiple time
creating extra garbage in the loop.
This simplifies the code to create one map for grouping and than adding
the distinct sets to a slice which is then sorted. It also fixes the
multple tag maps getting created.
Michael Desa
Jonathan A. Sternberg
Jason Wilder
Jason Wilder
Nathaniel Cook
Mike Glazer
Joe LeGasse
Edd Robinson
Mark Rushakoff
Mark Rushakoff