graphql-engine/server/src-lib/Hasura/Server/Telemetry/Counters.hs
Samir Talwar 342391f39d Upgrade Ormolu to v0.5.
This upgrades the version of Ormolu required by the HGE repository to v0.5.0.1, and reformats all code accordingly.

Ormolu v0.5 reformats code that uses infix operators. This is mostly useful, adding newlines and indentation to make it clear which operators are applied first, but in some cases, it's unpleasant. To make this easier on the eyes, I had to do the following:

* Add a few fixity declarations (search for `infix`)
* Add parentheses to make precedence clear, allowing Ormolu to keep everything on one line
* Rename `relevantEq` to `(==~)` in #6651 and set it to `infix 4`
* Add a few _.ormolu_ files (thanks to @hallettj for helping me get started), mostly for Autodocodec operators that don't have explicit fixity declarations

In general, I think these changes are quite reasonable. They mostly affect indentation.

PR-URL: https://github.com/hasura/graphql-engine-mono/pull/6675
GitOrigin-RevId: cd47d87f1d089fb0bc9dcbbe7798dbceedcd7d83
2022-11-02 20:55:13 +00:00

203 lines
6.5 KiB
Haskell

{-# LANGUAGE DuplicateRecordFields #-}
{-# LANGUAGE TemplateHaskell #-}
-- |
-- Counters used in telemetry collection. Additional counters can be added here.and
-- serviced in "Hasura.Server.Telemetry".
module Hasura.Server.Telemetry.Counters
( -- * Service timing and counts, by various dimensions
-- ** Local metric recording
recordTimingMetric,
RequestDimensions (..),
RequestTimings (..),
-- *** Dimensions
QueryType (..),
Locality (..),
Transport (..),
-- ** Metric upload
dumpServiceTimingMetrics,
ServiceTimingMetrics (..),
ServiceTimingMetric (..),
RunningTimeBucket (..),
RequestTimingsCount (..),
)
where
import Data.Aeson qualified as A
import Data.Aeson.TH qualified as A
import Data.HashMap.Strict qualified as HM
import Data.IORef
import Data.Time.Clock.POSIX (POSIXTime, getPOSIXTime)
import GHC.IO.Unsafe (unsafePerformIO)
import Hasura.Prelude
-- | The properties that characterize this request. The dimensions over which
-- we collect metrics for each serviced request.
data RequestDimensions = RequestDimensions
{ telemQueryType :: !QueryType,
telemLocality :: !Locality,
telemTransport :: !Transport
}
deriving (Show, Generic, Eq, Ord)
instance Hashable RequestDimensions
-- | Accumulated time metrics.
data RequestTimings = RequestTimings
{ -- | Time spent waiting on PG/remote http calls
telemTimeIO :: !Seconds,
-- | Total service time for request (including 'telemTimeIO')
telemTimeTot :: !Seconds
}
-- | Sum
instance Semigroup RequestTimings where
RequestTimings a b <> RequestTimings x y = RequestTimings (a + x) (b + y)
-- | 'RequestTimings' along with the count
data RequestTimingsCount = RequestTimingsCount
{ telemTimeIO :: !Seconds,
telemTimeTot :: !Seconds,
-- | The number of requests that have contributed to the accumulated timings above.
-- So e.g. @telemTimeTot / count@ would give the mean service time.
telemCount :: !Word
}
deriving (Show, Generic, Eq, Ord)
-- | Sum
instance Semigroup RequestTimingsCount where
RequestTimingsCount a b c <> RequestTimingsCount x y z =
RequestTimingsCount (a + x) (b + y) (c + z)
-- | Internal. Counts and durations across many 'RequestDimensions'.
--
-- NOTE: We use the global mutable variable pattern for metric collection
-- counters for convenience at collection site (don't wear hairshirts that
-- discourage useful reporting).
requestCounters :: IORef (HM.HashMap (RequestDimensions, RunningTimeBucket) RequestTimingsCount)
{-# NOINLINE requestCounters #-}
requestCounters = unsafePerformIO $ newIORef HM.empty
-- | Internal. Since these metrics are accumulated while graphql-engine is
-- running and sent periodically, we need to include a tag that is unique for
-- each start of hge. This lets us e.g. query for just the latest uploaded
-- sample for each start of hge.
--
-- We use time rather than a UUID since having this be monotonic increasing is
-- convenient.
approxStartTime :: POSIXTime
{-# NOINLINE approxStartTime #-}
approxStartTime = unsafePerformIO getPOSIXTime
-- | Was this request a mutation (involved DB writes)?
data QueryType = Mutation | Query
deriving (Enum, Show, Eq, Ord, Generic)
instance Hashable QueryType
instance A.ToJSON QueryType
instance A.FromJSON QueryType
-- | Was this a PG local query, or did it involve remote execution?
data Locality
= -- | No data was fetched
Empty
| -- | local DB data
Local
| -- | remote schema
Remote
| -- | mixed
Heterogeneous
deriving (Enum, Show, Eq, Ord, Generic)
instance Hashable Locality
instance A.ToJSON Locality
instance A.FromJSON Locality
instance Semigroup Locality where
Empty <> x = x
x <> Empty = x
x <> y | x == y = x
_ <> _ = Heterogeneous
instance Monoid Locality where
mempty = Empty
-- | Was this a query over http or websockets?
data Transport = HTTP | WebSocket
deriving (Enum, Show, Eq, Ord, Generic)
instance Hashable Transport
instance A.ToJSON Transport
instance A.FromJSON Transport
-- | The timings and counts here were from requests with total time longer than
-- 'bucketGreaterThan' (but less than any larger bucket cutoff times).
newtype RunningTimeBucket = RunningTimeBucket {bucketGreaterThan :: Seconds}
deriving (Ord, Eq, Show, Generic, A.ToJSON, A.FromJSON, Hashable)
-- NOTE: an HDR histogram is a nice way to collect metrics when you don't know
-- a priori what the most useful binning is. It's not clear how we'd make use
-- of that here though. So these buckets are arbitrary, and can be adjusted as
-- needed, but we shouldn't have more than a handful to keep payload size down.
totalTimeBuckets :: [RunningTimeBucket]
totalTimeBuckets = coerce [0.000, 0.001, 0.050, 1.000, 3600.000 :: Seconds]
-- | Save a timing metric sample in our in-memory store. These will be
-- accumulated and uploaded periodically in "Hasura.Server.Telemetry".
recordTimingMetric :: MonadIO m => RequestDimensions -> RequestTimings -> m ()
recordTimingMetric reqDimensions RequestTimings {..} = liftIO $ do
let ourBucket =
fromMaybe (RunningTimeBucket 0) $ -- although we expect 'head' would be safe here
listToMaybe $
dropWhile (> coerce telemTimeTot) $
reverse $
sort totalTimeBuckets
atomicModifyIORef' requestCounters $
(,())
. HM.insertWith (<>) (reqDimensions, ourBucket) RequestTimingsCount {telemCount = 1, ..}
-- | The final shape of this part of our metrics data JSON. This should allow
-- reasonably efficient querying using GIN indexes and JSONB containment
-- operations (which treat arrays as sets).
data ServiceTimingMetrics = ServiceTimingMetrics
{ -- | This is set to a new unique value when the counters reset (e.g. because of a restart)
collectionTag :: Int,
serviceTimingMetrics :: [ServiceTimingMetric]
}
deriving (Show, Generic, Eq, Ord)
data ServiceTimingMetric = ServiceTimingMetric
{ dimensions :: RequestDimensions,
bucket :: RunningTimeBucket,
metrics :: RequestTimingsCount
}
deriving (Show, Generic, Eq, Ord)
$(A.deriveJSON hasuraJSON ''RequestTimingsCount)
$(A.deriveJSON hasuraJSON ''RequestDimensions)
instance A.ToJSON ServiceTimingMetric
instance A.FromJSON ServiceTimingMetric
instance A.ToJSON ServiceTimingMetrics
instance A.FromJSON ServiceTimingMetrics
dumpServiceTimingMetrics :: MonadIO m => m ServiceTimingMetrics
dumpServiceTimingMetrics = liftIO $ do
cs <- readIORef requestCounters
let serviceTimingMetrics = flip map (HM.toList cs) $
\((dimensions, bucket), metrics) -> ServiceTimingMetric {..}
collectionTag = round approxStartTime
return ServiceTimingMetrics {..}