• Bloodhound
• Elasticsearch client and query DSL for Haskell
  • Why?
  • Endorsements
  • Version compatibility
  • Stability
• Hackage page and Haddock documentation
• Examples
  • Index Operations
    • Create Index
    • Delete Index
      • Code
      • Example Response
    • Refresh Index
      • Note, you have to do this if you expect to read what you just wrote
      • Example Response
  • Mapping Operations
    • Create Mapping
    • Delete Mapping
  • Document Operations
    • Indexing Documents
      • Example Response
    • Deleting Documents
    • Getting Documents
  • Bulk Operations
    • Bulk create, index
  • Search
    • Querying
      • Term Query
      • Match Query
      • Multi-Match Query
      • Bool Query
      • Boosting Query
      • Rest of the query/filter types
    • Sorting
    • Filtering
      • And, Not, and Or filters
      • Identity Filter
      • Boolean Filter
      • Exists Filter
      • Geo BoundingBox Filter
      • Geo Distance Filter
      • Geo Distance Range Filter
      • Geo Polygon Filter
      • Document IDs filter
      • Range Filter
        • Full Range
        • Half Range
      • Regexp Filter
    • Aggregations
      • Adding aggregations to search
      • Extracting aggregations from results
      • Terms Aggregation
      • Date Histogram Aggregation
• Possible future functionality
  • Span Queries
  • Function Score Query
  • Node discovery and failover
  • Support for TCP access to Elasticsearch
  • Bulk cluster-join merge
  • GeoShapeQuery
  • GeoShapeFilter
  • Geohash cell filter
  • HasChild Filter
  • HasParent Filter
  • Indices Filter
  • Query Filter
  • Script based sorting
  • Collapsing redundantly nested and/or structures
  • Runtime checking for cycles in data structures
• Photo Origin

Bloodhound

Bloodhound (dog)

Build Status

Elasticsearch client and query DSL for Haskell

Why?

Search doesn't have to be hard. Let the dog do it.

Endorsements

"Bloodhound makes Elasticsearch almost tolerable!" - Almost-gruntled user

Version compatibility

Elasticsearch >= 1.0 is recommended. Bloodhound mostly works with 0.9.x, but I don't recommend it if you expect everything to work. As of Bloodhound 0.3 all >=1.0 versions of Elasticsearch work.

Current versions we test against are 1.0.3, 1.1.2, 1.2.3, and 1.3.2. We also check that GHC 7.6 and 7.8 both build and pass tests. See our [TravisCI](https://travis-ci.org/bitemyapp/bloodhound) to learn more.

Stability

Bloodhound is beta at the moment. We've got a solid library tested across multiple ES versions, but coverage of Elasticsearch functionality isn't 100% yet.

Hackage page and Haddock documentation

http://hackage.haskell.org/package/bloodhound

Examples

Index Operations

Create Index

-- Formatted for use in ghci, so there are "let"s in front of the decls.

-- if you see :{ and :}, they're so you can copy-paste
-- the multi-line examples into your ghci REPL.

:set -XDeriveGeneric
import Database.Bloodhound
import Data.Aeson
import Data.Either (Either(..))
import Data.Maybe (fromJust)
import Data.Time.Calendar (Day(..))
import Data.Time.Clock (secondsToDiffTime, UTCTime(..))
import Data.Text (Text)
import GHC.Generics (Generic)
import Network.HTTP.Conduit
import qualified Network.HTTP.Types.Status as NHTS

-- no trailing slashes in servers, library handles building the path.
let testServer = (Server "http://localhost:9200")
let testIndex = IndexName "twitter"
let testMapping = MappingName "tweet"

-- defaultIndexSettings is exported by Database.Bloodhound as well
let defaultIndexSettings = IndexSettings (ShardCount 3) (ReplicaCount 2)

-- createIndex returns IO Reply

-- response :: Reply, Reply is a synonym for Network.HTTP.Conduit.Response
response <- createIndex testServer defaultIndexSettings testIndex

Delete Index

Code

-- response :: Reply
response <- deleteIndex testServer testIndex

Example Response

-- print response if it was a success
Response {responseStatus = Status {statusCode = 200, statusMessage = "OK"}
        , responseVersion = HTTP/1.1
        , responseHeaders = [("Content-Type", "application/json; charset=UTF-8")
                           , ("Content-Length", "21")]
        , responseBody = "{\"acknowledged\":true}"
        , responseCookieJar = CJ {expose = []}
        , responseClose' = ResponseClose}

-- if the index to be deleted didn't exist anyway
Response {responseStatus = Status {statusCode = 404, statusMessage = "Not Found"}
        , responseVersion = HTTP/1.1
        , responseHeaders = [("Content-Type", "application/json; charset=UTF-8")
                           , ("Content-Length","65")]
        , responseBody = "{\"error\":\"IndexMissingException[[twitter] missing]\",\"status\":404}"
        , responseCookieJar = CJ {expose = []}
        , responseClose' = ResponseClose}

Refresh Index

Note, you have to do this if you expect to read what you just wrote

resp <- refreshIndex testServer testIndex

Example Response

-- print resp on success
Response {responseStatus = Status {statusCode = 200, statusMessage = "OK"}
        , responseVersion = HTTP/1.1
        , responseHeaders = [("Content-Type", "application/json; charset=UTF-8")
                           , ("Content-Length","50")]
        , responseBody = "{\"_shards\":{\"total\":10,\"successful\":5,\"failed\":0}}"
        , responseCookieJar = CJ {expose = []}
        , responseClose' = ResponseClose}

Mapping Operations

Create Mapping

-- don't forget imports and the like at the top.

data TweetMapping = TweetMapping deriving (Eq, Show)

-- I know writing the JSON manually sucks.
-- I don't have a proper data type for Mappings yet.
-- Let me know if this is something you need.

:{
instance ToJSON TweetMapping where
  toJSON TweetMapping =
    object ["tweet" .=
      object ["properties" .=
        object ["location" .=
          object ["type" .= ("geo_point" :: Text)]]]]
:}

resp <- putMapping testServer testIndex testMapping TweetMapping

Delete Mapping

resp <- deleteMapping testServer testIndex testMapping

Document Operations

Indexing Documents

-- don't forget the imports and derive generic setting for ghci
-- at the beginning of the examples.

:{
data Location = Location { lat :: Double
                         , lon :: Double } deriving (Eq, Generic, Show)

data Tweet = Tweet { user     :: Text
                   , postDate :: UTCTime
                   , message  :: Text
                   , age      :: Int
                   , location :: Location } deriving (Eq, Generic, Show)

exampleTweet = Tweet { user     = "bitemyapp"
                     , postDate = UTCTime
                                  (ModifiedJulianDay 55000)
                                  (secondsToDiffTime 10)
                     , message  = "Use haskell!"
                     , age      = 10000
                     , location = Location 40.12 (-71.34) }

-- automagic (generic) derivation of instances because we're lazy.
instance ToJSON   Tweet
instance FromJSON Tweet
instance ToJSON   Location
instance FromJSON Location
:}

-- Should be able to toJSON and encode the data structures like this:
-- λ> toJSON $ Location 10.0 10.0
-- Object fromList [("lat",Number 10.0),("lon",Number 10.0)]
-- λ> encode $ Location 10.0 10.0
-- "{\"lat\":10,\"lon\":10}"

resp <- indexDocument testServer testIndex testMapping exampleTweet (DocId "1")

Example Response

Response {responseStatus =
  Status {statusCode = 200, statusMessage = "OK"}
    , responseVersion = HTTP/1.1, responseHeaders =
    [("Content-Type","application/json; charset=UTF-8"),
     ("Content-Length","75")]
    , responseBody = "{\"_index\":\"twitter\",\"_type\":\"tweet\",\"_id\":\"1\",\"_version\":2,\"created\":false}"
    , responseCookieJar = CJ {expose = []}, responseClose' = ResponseClose}

Deleting Documents

resp <- deleteDocument testServer testIndex testMapping (DocId "1")

Getting Documents

-- n.b., you'll need the earlier imports. responseBody is from http-conduit

resp <- getDocument testServer testIndex testMapping (DocId "1")

-- responseBody :: Response body -> body
let body = responseBody resp

-- you have two options, you use decode and just get Maybe (EsResult Tweet)
-- or you can use eitherDecode and get Either String (EsResult Tweet)

let maybeResult = decode body :: Maybe (EsResult Tweet)
-- the explicit typing is so Aeson knows how to parse the JSON.

-- use either if you want to know why something failed to parse.
-- (string errors, sadly)
let eitherResult = decode body :: Either String (EsResult Tweet)

-- print eitherResult should look like:
Right (EsResult {_index = "twitter"
               , _type = "tweet"
               , _id = "1"
               , _version = 2
               , found = Just True
               , _source = Tweet {user = "bitemyapp"
               , postDate = 2009-06-18 00:00:10 UTC
               , message = "Use haskell!"
               , age = 10000
               , location = Location {lat = 40.12, lon = -71.34}}})

-- _source in EsResult is parametric, we dispatch the type by passing in what we expect (Tweet) as a parameter to EsResult.

-- use the _source record accessor to get at your document
λ> fmap _source result
Right (Tweet {user = "bitemyapp"
            , postDate = 2009-06-18 00:00:10 UTC
            , message = "Use haskell!"
            , age = 10000
            , location = Location {lat = 40.12, lon = -71.34}})

Bulk Operations

Bulk create, index

-- don't forget the imports and derive generic setting for ghci
-- at the beginning of the examples.

:{
-- Using the earlier Tweet datatype and exampleTweet data

-- just changing up the data a bit.
let bulkTest = exampleTweet { user = "blah" }
let bulkTestTwo = exampleTweet { message = "woohoo!" }

-- create only bulk operation
-- BulkCreate :: IndexName -> MappingName -> DocId -> Value -> BulkOperation
let firstOp = BulkCreate testIndex
              testMapping (DocId "3") (toJSON bulkTest)

-- index operation "create or update"
let sndOp   = BulkIndex testIndex
              testMapping (DocId "4") (toJSON bulkTestTwo)

-- Some explanation, the final "Value" type that BulkIndex,
-- BulkCreate, and BulkUpdate accept is the actual document
-- data that your operation applies to. BulkDelete doesn't
-- take a value because it's just deleting whatever DocId 
-- you pass.

-- list of bulk operations
let stream = [firstDoc, secondDoc]

-- Fire off the actual bulk request
-- bulk :: Server -> [BulkOperation] -> IO Reply
resp <- bulk testServer stream
:}

Search

Querying

Term Query

-- exported by the Client module, just defaults some stuff.
-- mkSearch :: Maybe Query -> Maybe Filter -> Search
-- mkSearch query filter = Search query filter Nothing False 0 10

let query = TermQuery (Term "user" "bitemyapp") Nothing

-- AND'ing identity filter with itself and then tacking it onto a query
-- search should be a null-operation. I include it for the sake of example.
-- <||> (or/plus) should make it into a search that returns everything.

let filter = IdentityFilter <&&> IdentityFilter

-- constructing the search object the searchByIndex function dispatches on.
let search = mkSearch (Just query) (Just filter)

-- you can also searchByType and specify the mapping name.
reply <- searchByIndex testServer testIndex search

let result = eitherDecode (responseBody reply) :: Either String (SearchResult Tweet)

λ> fmap (hits . searchHits) result
Right [Hit {hitIndex = IndexName "twitter"
          , hitType = MappingName "tweet"
          , hitDocId = DocId "1"
          , hitScore = 0.30685282
          , hitSource = Tweet {user = "bitemyapp"
                             , postDate = 2009-06-18 00:00:10 UTC
                             , message = "Use haskell!"
                             , age = 10000
                             , location = Location {lat = 40.12, lon = -71.34}}}]

Match Query

let query = QueryMatchQuery $ mkMatchQuery (FieldName "user") (QueryString "bitemyapp")
let search = mkSearch (Just query) Nothing

Multi-Match Query

let fields = [FieldName "user", FieldName "message"]
let query = QueryMultiMatchQuery $ mkMultiMatchQuery fields (QueryString "bitemyapp")
let search = mkSearch (Just query) Nothing

Bool Query

let innerQuery = QueryMatchQuery $
                 mkMatchQuery (FieldName "user") (QueryString "bitemyapp")
let query = QueryBoolQuery $
            mkBoolQuery [innerQuery] [] []
let search = mkSearch (Just query) Nothing

Boosting Query

let posQuery = QueryMatchQuery $
               mkMatchQuery (FieldName "user") (QueryString "bitemyapp")
let negQuery = QueryMatchQuery $
               mkMatchQuery (FieldName "user") (QueryString "notmyapp")
let query = QueryBoostingQuery $
            BoostingQuery posQuery negQuery (Boost 0.2)

Rest of the query/filter types

Just follow the pattern you've seen here and check the Hackage API documentation.

Sorting

let sortSpec = DefaultSortSpec $ mkSort (FieldName "age") Ascending

-- mkSort is a shortcut function that takes a FieldName and a SortOrder
-- to generate a vanilla DefaultSort.
-- checkt the DefaultSort type for the full list of customizable options.

-- From and size are integers for pagination.

-- When sorting on a field, scores are not computed. By setting TrackSortScores to true, scores will still be computed and tracked.

-- type Sort = [SortSpec]
-- type TrackSortScores = Bool
-- type From = Int
-- type Size = Int

-- Search takes Maybe Query
--              -> Maybe Filter
--              -> Maybe Sort
--              -> TrackSortScores
--              -> From -> Size

-- just add more sortspecs to the list if you want tie-breakers.
let search = Search Nothing (Just IdentityFilter) (Just [sortSpec]) False 0 10

Filtering

And, Not, and Or filters

Filters form a monoid and seminearring.

instance Monoid Filter where
  mempty = IdentityFilter
  mappend a b = AndFilter [a, b] defaultCache

instance Seminearring Filter where
  a <||> b = OrFilter [a, b] defaultCache

-- AndFilter and OrFilter take [Filter] as an argument.

-- This will return anything, because IdentityFilter returns everything
OrFilter [IdentityFilter, someOtherFilter] False

-- This will return exactly what someOtherFilter returns
AndFilter [IdentityFilter, someOtherFilter] False

-- Thanks to the seminearring and monoid, the above can be expressed as:

-- "and"
IdentityFilter <&&> someOtherFilter

-- "or"
IdentityFilter <||> someOtherFilter

-- Also there is a NotFilter, it only accepts a single filter, not a list.

NotFilter someOtherFilter False

Identity Filter

-- And'ing two Identity
let queryFilter = IdentityFilter <&&> IdentityFilter

let search = mkSearch Nothing (Just queryFilter)

reply <- searchByType testServer testIndex testMapping search

Boolean Filter

Similar to boolean queries.

-- Will return only items whose "user" field contains the term "bitemyapp"
let queryFilter = BoolFilter (MustMatch (Term "user" "bitemyapp") False)

-- Will return only items whose "user" field does not contain the term "bitemyapp"
let queryFilter = BoolFilter (MustNotMatch (Term "user" "bitemyapp") False)

-- The clause (query) should appear in the matching document.
-- In a boolean query with no must clauses, one or more should
-- clauses must match a document. The minimum number of should
-- clauses to match can be set using the minimum_should_match parameter.
let queryFilter = BoolFilter (ShouldMatch [(Term "user" "bitemyapp")] False)

Exists Filter

-- Will filter for documents that have the field "user"
let existsFilter = ExistsFilter (FieldName "user")

Geo BoundingBox Filter

-- topLeft and bottomRight
let box = GeoBoundingBox (LatLon 40.73 (-74.1)) (LatLon 40.10 (-71.12))

let constraint = GeoBoundingBoxConstraint (FieldName "tweet.location") box False GeoFilterMemory

Geo Distance Filter

let geoPoint = GeoPoint (FieldName "tweet.location") (LatLon 40.12 (-71.34))

-- coefficient and units
let distance = Distance 10.0 Miles

-- GeoFilterType or NoOptimizeBbox
let optimizeBbox = OptimizeGeoFilterType GeoFilterMemory

-- SloppyArc is the usual/default optimization in Elasticsearch today
-- but pre-1.0 versions will need to pick Arc or Plane.

let geoFilter = GeoDistanceFilter geoPoint distance SloppyArc optimizeBbox False

Geo Distance Range Filter

Think of a donut and you won't be far off.

let geoPoint = GeoPoint (FieldName "tweet.location") (LatLon 40.12 (-71.34))

let distanceRange = DistanceRange (Distance 0.0 Miles) (Distance 10.0 Miles)

let geoFilter = GeoDistanceRangeFilter geoPoint distanceRange

Geo Polygon Filter

-- I think I drew a square here.
let points = [LatLon 40.0 (-70.00),
              LatLon 40.0 (-72.00),
              LatLon 41.0 (-70.00),
              LatLon 41.0 (-72.00)]

let geoFilter = GeoPolygonFilter (FieldName "tweet.location") points

Document IDs filter

-- takes a mapping name and a list of DocIds
IdsFilter (MappingName "tweet") [DocId "1"]

Range Filter

Full Range

-- RangeFilter :: FieldName
--                -> Either HalfRange Range
--                -> RangeExecution
--                -> Cache -> Filter

let filter = RangeFilter (FieldName "age")
             (Right (RangeLtGt (LessThan 100000.0) (GreaterThan 1000.0)))
             RangeExecutionIndex False

Half Range

let filter = RangeFilter (FieldName "age")
             (Left (HalfRangeLt (LessThan 100000.0)))
             RangeExecutionIndex False

Regexp Filter

-- RegexpFilter
--   :: FieldName
--      -> Regexp
--      -> RegexpFlags
--      -> CacheName
--      -> Cache
--      -> CacheKey
--      -> Filter
let filter = RegexpFilter (FieldName "user") (Regexp "bite.*app")
             AllRegexpFlags (CacheName "test") False (CacheKey "key")

-- n.b.
-- data RegexpFlags = AllRegexpFlags
--                 | NoRegexpFlags
--                 | SomeRegexpFlags (NonEmpty RegexpFlag) deriving (Eq, Show)

-- data RegexpFlag = AnyString
--                | Automaton
--                | Complement
--                | Empty
--                | Intersection
--                | Interval deriving (Eq, Show)

Aggregations

Adding aggregations to search

Aggregations can now be added to search queries, or made on their own.

type Aggregations = M.Map Text Aggregation
data Aggregation
  = TermsAgg TermsAggregation
  | DateHistogramAgg DateHistogramAggregation

For convenience, ```mkAggregations``` exists, that will create an ```Aggregations``` with the aggregation provided.

For example:

 let a = mkAggregations "users" $ TermsAgg $ mkTermsAggregation "user"
 let search = mkAggregateSearch Nothing a

Aggregations can be added to an existing search, using the ```aggBody``` field

 let search  = mkSearch (Just (MatchAllQuery Nothing)) Nothing
 let search' = search {aggBody = Just a}

Since the ```Aggregations``` structure is just a Map Text Aggregation, M.insert can be used to add additional aggregations.

 let a' = M.insert "age" (TermsAgg $ mkTermsAggregation "age") a

Extracting aggregations from results

Aggregations are part of the reply structure of every search, in the form of Maybe AggregationResults

-- Lift decode and response body to be in the IO monad.
let decode' = liftM decode
let responseBody' = liftM responseBody
let reply = searchByIndex testServer testIndex search
let response = decode' $ responseBody' reply :: IO (Maybe (SearchResult Tweet))

-- Now that we have our response, we can extract our terms aggregation result -- which is a list of buckets.

let terms = do { response' <- response; return $ response' >>= aggregations >>= toTerms "users" }
terms
Just (Bucket {buckets = [TermsResult {termKey = "bitemyapp", termsDocCount = 1, termsAggs = Nothing}]})

Note that bucket aggregation results, such as the TermsResult is a member of the type class BucketAggregation:

class BucketAggregation a where
  key :: a -> Text
  docCount :: a -> Int
  aggs :: a -> Maybe AggregationResults

You can use the aggs function to get any nested results, if there were any. For example, if there were a nested terms aggregation keyed to "age" in a TermsResult named termresult, you would call aggs termresult >>= toTerms "age"

Terms Aggregation

data TermsAggregation
  = TermsAggregation {term :: Either Text Text,
                      termInclude :: Maybe TermInclusion,
                      termExclude :: Maybe TermInclusion,
                      termOrder :: Maybe TermOrder,
                      termMinDocCount :: Maybe Int,
                      termSize :: Maybe Int,
                      termShardSize :: Maybe Int,
                      termCollectMode :: Maybe CollectionMode,
                      termExecutionHint :: Maybe ExecutionHint,
                      termAggs :: Maybe Aggregations}

Term Aggregations have two factory functions, mkTermsAggregation, and mkTermsScriptAggregation, and can be used as follows:

let ta = TermsAgg $ mkTermsAggregation "user"

There are of course other options that can be added to a Terms Aggregation, such as the collection mode:

let ta   = mkTermsAggregation "user"
let ta'  = ta { termCollectMode = Just BreadthFirst }
let ta'' = TermsAgg ta'

For more documentation on how the Terms Aggregation works, see http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/search-aggregations-bucket-terms-aggregation.html

Date Histogram Aggregation

data DateHistogramAggregation
  = DateHistogramAggregation {dateField :: FieldName,
                              dateInterval :: Interval,
                              dateFormat :: Maybe Text,
                              datePreZone :: Maybe Text,
                              datePostZone :: Maybe Text,
                              datePreOffset :: Maybe Text,
                              datePostOffset :: Maybe Text,
                              dateAggs :: Maybe Aggregations}

The Date Histogram Aggregation works much the same as the Terms Aggregation.

Relevant functions include mkDateHistogram, and toDateHistogram

let dh = DateHistogramAgg (mkDateHistogram (FieldName "postDate") Minute)

Date histograms also accept a FractionalInterval:

FractionalInterval :: Float -> TimeInterval -> Interval
-- TimeInterval is the following:
data TimeInterval = Weeks | Days | Hours | Minutes | Seconds

It can be used as follows:

let dh = DateHistogramAgg (mkDateHistogram (FieldName "postDate") (FractionalInterval 1.5 Minutes))

The DateHistogramResult is defined as:

data DateHistogramResult
  = DateHistogramResult {dateKey :: Int,
                         dateKeyStr :: Maybe Text,
                         dateDocCount :: Int,
                         dateHistogramAggs :: Maybe AggregationResults}

It is an instance of BucketAggregation, and can have nested aggregations in each bucket.

Buckets can be extracted from a AggregationResult using toDateHistogram name

For more information on the Date Histogram Aggregation, see: http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/search-aggregations-bucket-datehistogram-aggregation.html

Possible future functionality

Span Queries

Beginning here: http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/query-dsl-span-first-query.html

Function Score Query

http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/query-dsl-function-score-query.html

Node discovery and failover

Might require TCP support.

Support for TCP access to Elasticsearch

Pretend to be a transport client?

Bulk cluster-join merge

Might require making a lucene index on disk with the appropriate format.

GeoShapeQuery

http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/query-dsl-geo-shape-query.html

GeoShapeFilter

http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/query-dsl-geo-shape-filter.html

Geohash cell filter

http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/query-dsl-geohash-cell-filter.html

HasChild Filter

http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/query-dsl-has-child-filter.html

HasParent Filter

http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/query-dsl-has-parent-filter.html

Indices Filter

http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/query-dsl-indices-filter.html

Query Filter

http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/query-dsl-query-filter.html

Script based sorting

http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/search-request-sort.html#_script_based_sorting

Collapsing redundantly nested and/or structures

The Seminearring instance, if deeply nested can possibly produce nested structure that is redundant. Depending on how this affects ES perforamnce, reducing this structure might be valuable.

Runtime checking for cycles in data structures

check for n > 1 occurrences in DFS:

http://hackage.haskell.org/package/stable-maps-0.0.5/docs/System-Mem-StableName-Dynamic.html

http://hackage.haskell.org/package/stable-maps-0.0.5/docs/System-Mem-StableName-Dynamic-Map.html

Photo Origin

Photo from HA! Designs: https://www.flickr.com/photos/hadesigns/