This commit changes the configuration interface from job configs to scrape
configs. This includes allowing multiple ways of target definition at once
and moving DNS SD to its own config message. DNS SD can now contain multiple
DNS names per configured discovery.
This commit shifts responsibility for maintaining targets from providers and
pools to the target manager. Target groups have a source name that identifies
them for updates.
/api/targets was undocumented and never used and also broken.
Showing instance and job labels on the status page (next to targets)
does not make sense as those labels are set in an obvious way.
Also add a doc comment to TargetStateToClass.
The one central sample ingestion channel has caused a variety of
trouble. This commit removes it. Targets and rule evaluation call an
Append method directly now. To incorporate multiple storage backends
(like OpenTSDB), storage.Tee forks the Append into two different
appenders.
Note that the tsdb queue manager had its own queue anyway. It was a
queue after a queue... Much queue, so overhead...
Targets have their own little buffer (implemented as a channel) to
avoid stalling during an http scrape. But a new scrape will only be
started once the old one is fully ingested.
The contraption of three pipelined ingesters was removed. A Target is
an ingester itself now. Despite more logic in Target, things should be
less confusing now.
Also, remove lint and vet warnings in ast.go.
The current wording suggests that a target is not reachable at all,
although it might also get set when the target was reachable, but there
was some other error during the scrape (invalid headers or invalid
scrape content). UNHEALTHY is a more general wording that includes all
these cases.
For consistency, ALIVE is also renamed to HEALTHY.
This is now not even trying to throttle in a benign way, but creates a
fully-fledged error. Advantage: It shows up very visible on the status
page. Disadvantage: The server does not really adjusts to a lower
scraping rate. However, if your ingestion backs up, you are in a very
irregulare state, I'd say it _should_ be considered an error and not
dealt with in a more graceful way.
In different news: I'll work on optimizing ingestion so that we will
not as easily run into that situation in the first place.
- Move CONTRIBUTORS.md to the more common AUTHORS.
- Added the required NOTICE file.
- Changed "Prometheus Team" to "The Prometheus Authors".
- Reverted the erroneous changes to the Apache License.
The "Address" is actually a URL which may contain username and
password. Calling this Address is misleading so we rename it.
Change-Id: I441c7ab9dfa2ceedc67cde7a47e6843a65f60511
Essentially:
- Remove unused code.
- Make it 'go vet' clean. The only remaining warnings are in generated code.
- Make it 'golint' clean. The only remaining warnings are in gerenated code.
- Smoothed out same minor things.
Change-Id: I3fe5c1fbead27b0e7a9c247fee2f5a45bc2d42c6
Having metrics with variable timestamps inconsistently
spaced when things fail will make it harder to write correct rules.
Update status page, requires some refactoring to insert a function.
Change-Id: Ie1c586cca53b8f3b318af8c21c418873063738a8
So far we've been using Go's native time.Time for anything related to sample
timestamps. Since the range of time.Time is much bigger than what we need, this
has created two problems:
- there could be time.Time values which were out of the range/precision of the
time type that we persist to disk, therefore causing incorrectly ordered keys.
One bug caused by this was:
https://github.com/prometheus/prometheus/issues/367
It would be good to use a timestamp type that's more closely aligned with
what the underlying storage supports.
- sizeof(time.Time) is 192, while Prometheus should be ok with a single 64-bit
Unix timestamp (possibly even a 32-bit one). Since we store samples in large
numbers, this seriously affects memory usage. Furthermore, copying/working
with the data will be faster if it's smaller.
*MEMORY USAGE RESULTS*
Initial memory usage comparisons for a running Prometheus with 1 timeseries and
100,000 samples show roughly a 13% decrease in total (VIRT) memory usage. In my
tests, this advantage for some reason decreased a bit the more samples the
timeseries had (to 5-7% for millions of samples). This I can't fully explain,
but perhaps garbage collection issues were involved.
*WHEN TO USE THE NEW TIMESTAMP TYPE*
The new clientmodel.Timestamp type should be used whenever time
calculations are either directly or indirectly related to sample
timestamps.
For example:
- the timestamp of a sample itself
- all kinds of watermarks
- anything that may become or is compared to a sample timestamp (like the timestamp
passed into Target.Scrape()).
When to still use time.Time:
- for measuring durations/times not related to sample timestamps, like duration
telemetry exporting, timers that indicate how frequently to execute some
action, etc.
*NOTE ON OPERATOR OPTIMIZATION TESTS*
We don't use operator optimization code anymore, but it still lives in
the code as dead code. It still has tests, but I couldn't get all of them to
pass with the new timestamp format. I commented out the failing cases for now,
but we should probably remove the dead code soon. I just didn't want to do that
in the same change as this.
Change-Id: I821787414b0debe85c9fffaeb57abd453727af0f
This includes required refactorings to enable replacing the http client (for
testing) and moving the NotificationReq type definitions to the "notifications"
package, so that this package doesn't need to depend on "rules" anymore and
that it can instead use a representation of the required data which only
includes the necessary fields.
This commit employs explicit memory freeing for the in-memory storage
arenas. Secondarily, we take advantage of smaller channel buffer sizes
in the test.
Instead of externally handling timeouts when scraping a target, we set
timeouts on the HTTP connection. This ensures that we don't leak
goroutines on timeouts.
[fixes#181]
Right now, futureState is only used to give hints to the health scheduler, but
nowhere is this future state persisted into the target's state field, so we
don't actually track a target's state over time.
