Convenience function that sets up a manager and BHEnv and runs the given set of bloodhound operations. Connections will be pipelined automatically in accordance with the given manager settings in IO. If you've got your own monad transformer stack, you should use runBH directly.

createIndex will create an index given a Server, IndexSettings, and an IndexName.

>>> response <- runBH' $ createIndex defaultIndexSettings (IndexName "didimakeanindex")
>>> respIsTwoHunna response
True
>>> runBH' $ indexExists (IndexName "didimakeanindex")
True

Create an index, providing it with any number of settings. This is more expressive than createIndex but makes is more verbose for the common case of configuring only the shard count and replica count.

flushIndex will flush an index given a Server and an IndexName.

deleteIndex will delete an index given a Server and an IndexName.

>>> _ <- runBH' $ createIndex defaultIndexSettings (IndexName "didimakeanindex")
>>> response <- runBH' $ deleteIndex (IndexName "didimakeanindex")
>>> respIsTwoHunna response
True
>>> runBH' $ indexExists (IndexName "didimakeanindex")
False

updateIndexSettings will apply a non-empty list of setting updates to an index

>>> _ <- runBH' $ createIndex defaultIndexSettings (IndexName "unconfiguredindex")
>>> response <- runBH' $ updateIndexSettings (BlocksWrite False :| []) (IndexName "unconfiguredindex")
>>> respIsTwoHunna response
True

forceMergeIndex

The force merge API allows to force merging of one or more indices through an API. The merge relates to the number of segments a Lucene index holds within each shard. The force merge operation allows to reduce the number of segments by merging them.

This call will block until the merge is complete. If the http connection is lost, the request will continue in the background, and any new requests will block until the previous force merge is complete.

indexExists enables you to check if an index exists. Returns Bool in IO

>>> exists <- runBH' $ indexExists testIndex

openIndex opens an index given a Server and an IndexName. Explained in further detail at http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/indices-open-close.html

>>> reply <- runBH' $ openIndex testIndex

closeIndex closes an index given a Server and an IndexName. Explained in further detail at http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/indices-open-close.html

>>> reply <- runBH' $ closeIndex testIndex

listIndices returns a list of all index names on a given Server

Block until the index becomes available for indexing documents. This is useful for integration tests in which indices are rapidly created and deleted.

updateIndexAliases updates the server's index alias table. Operations are atomic. Explained in further detail at https://www.elastic.co/guide/en/elasticsearch/reference/current/indices-aliases.html

>>> let src = IndexName "a-real-index"
>>> let aliasName = IndexName "an-alias"
>>> let iAlias = IndexAlias src (IndexAliasName aliasName)
>>> let aliasCreate = IndexAliasCreate Nothing Nothing
>>> _ <- runBH' $ deleteIndex src
>>> respIsTwoHunna <$> runBH' (createIndex defaultIndexSettings src)
True
>>> runBH' $ indexExists src
True
>>> respIsTwoHunna <$> runBH' (updateIndexAliases (AddAlias iAlias aliasCreate :| []))
True
>>> runBH' $ indexExists aliasName
True

Get all aliases configured on the server.

Delete a single alias, removing it from all indices it is currently associated with.

putTemplate creates a template given an IndexTemplate and a TemplateName. Explained in further detail at https://www.elastic.co/guide/en/elasticsearch/reference/1.7/indices-templates.html

>>> let idxTpl = IndexTemplate (TemplatePattern "tweet-*") (Just (IndexSettings (ShardCount 1) (ReplicaCount 1))) [toJSON TweetMapping]
>>> resp <- runBH' $ putTemplate idxTpl (TemplateName "tweet-tpl")

templateExists checks to see if a template exists.

>>> exists <- runBH' $ templateExists (TemplateName "tweet-tpl")

deleteTemplate is an HTTP DELETE and deletes a template.

>>> let idxTpl = IndexTemplate (TemplatePattern "tweet-*") (Just (IndexSettings (ShardCount 1) (ReplicaCount 1))) [toJSON TweetMapping]
>>> _ <- runBH' $ putTemplate idxTpl (TemplateName "tweet-tpl")
>>> resp <- runBH' $ deleteTemplate (TemplateName "tweet-tpl")

putMapping is an HTTP PUT and has upsert semantics. Mappings are schemas for documents in indexes.

>>> _ <- runBH' $ createIndex defaultIndexSettings testIndex
>>> resp <- runBH' $ putMapping testIndex testMapping TweetMapping
>>> print resp
Response {responseStatus = Status {statusCode = 200, statusMessage = "OK"}, responseVersion = HTTP/1.1, responseHeaders = [("content-type","application/json; charset=UTF-8"),("content-encoding","gzip"),("transfer-encoding","chunked")], responseBody = "{\"acknowledged\":true}", responseCookieJar = CJ {expose = []}, responseClose' = ResponseClose}

indexDocument is the primary way to save a single document in Elasticsearch. The document itself is simply something we can convert into a JSON Value. The DocId will function as the primary key for the document. You are encouraged to generate your own id's and not rely on Elasticsearch's automatic id generation. Read more about it here: https://github.com/bitemyapp/bloodhound/issues/107

>>> resp <- runBH' $ indexDocument testIndex testMapping defaultIndexDocumentSettings exampleTweet (DocId "1")
>>> print resp
Response {responseStatus = Status {statusCode = 201, statusMessage = "Created"}, responseVersion = HTTP/1.1, responseHeaders = [("Location","/twitter/tweet/1"),("content-type","application/json; charset=UTF-8"),("content-encoding","gzip"),("transfer-encoding","chunked")], responseBody = "{\"_index\":\"twitter\",\"_type\":\"tweet\",\"_id\":\"1\",\"_version\":1,\"result\":\"created\",\"_shards\":{\"total\":2,\"successful\":1,\"failed\":0},\"created\":true}", responseCookieJar = CJ {expose = []}, responseClose' = ResponseClose}

updateDocument provides a way to perform an partial update of a an already indexed document.

getDocument is a straight-forward way to fetch a single document from Elasticsearch using a Server, IndexName, MappingName, and a DocId. The DocId is the primary key for your Elasticsearch document.

>>> yourDoc <- runBH' $ getDocument testIndex testMapping (DocId "1")

documentExists enables you to check if a document exists. Returns Bool in IO

>>> exists <- runBH' $ documentExists testIndex testMapping Nothing (DocId "1")

deleteDocument is the primary way to delete a single document.

>>> _ <- runBH' $ deleteDocument testIndex testMapping (DocId "1")

searchAll, given a Search, will perform that search against all indexes on an Elasticsearch server. Try to avoid doing this if it can be helped.

>>> let query = TermQuery (Term "user" "bitemyapp") Nothing
>>> let search = mkSearch (Just query) Nothing
>>> reply <- runBH' $ searchAll search

searchByIndex, given a Search and an IndexName, will perform that search against all mappings within an index on an Elasticsearch server.

>>> let query = TermQuery (Term "user" "bitemyapp") Nothing
>>> let search = mkSearch (Just query) Nothing
>>> reply <- runBH' $ searchByIndex testIndex search

searchByIndices is a variant of searchByIndex that executes a Search over many indices. This is much faster than using mapM to searchByIndex over a collection since it only causes a single HTTP request to be emitted.

searchByType, given a Search, IndexName, and MappingName, will perform that search against a specific mapping within an index on an Elasticsearch server.

>>> let query = TermQuery (Term "user" "bitemyapp") Nothing
>>> let search = mkSearch (Just query) Nothing
>>> reply <- runBH' $ searchByType testIndex testMapping search

scanSearch uses the scroll API of elastic, for a given IndexName and MappingName. Note that this will consume the entire search result set and will be doing O(n) list appends so this may not be suitable for large result sets. In that case, getInitialScroll and advanceScroll are good low level tools. You should be able to hook them up trivially to conduit, pipes, or your favorite streaming IO abstraction of choice. Note that ordering on the search would destroy performance and thus is ignored.

For a given search, request a scroll for efficient streaming of search results. Note that the search is put into SearchTypeScan mode and thus results will not be sorted. Combine this with advanceScroll to efficiently stream through the full result set

For a given search, request a scroll for efficient streaming of search results. Combine this with advanceScroll to efficiently stream through the full result set. Note that this search respects sorting and may be less efficient than getInitialScroll.

Use the given scroll to fetch the next page of documents. If there are no further pages, 'SearchResult.searchHits.hits' will be '[]'.

refreshIndex will force a refresh on an index. You must do this if you want to read what you wrote.

>>> _ <- runBH' $ createIndex defaultIndexSettings testIndex
>>> _ <- runBH' $ refreshIndex testIndex

mkSearch is a helper function for defaulting additional fields of a Search to Nothing in case you only care about your Query and Filter. Use record update syntax if you want to add things like aggregations or highlights while still using this helper function.

>>> let query = TermQuery (Term "user" "bitemyapp") Nothing
>>> mkSearch (Just query) Nothing
Search {queryBody = Just (TermQuery (Term {termField = "user", termValue = "bitemyapp"}) Nothing), filterBody = Nothing, sortBody = Nothing, aggBody = Nothing, highlight = Nothing, trackSortScores = False, from = From 0, size = Size 10, searchType = SearchTypeQueryThenFetch, fields = Nothing, source = Nothing}

mkAggregateSearch is a helper function that defaults everything in a Search except for the Query and the Aggregation.

>>> let terms = TermsAgg $ (mkTermsAggregation "user") { termCollectMode = Just BreadthFirst }
>>> terms
TermsAgg (TermsAggregation {term = Left "user", termInclude = Nothing, termExclude = Nothing, termOrder = Nothing, termMinDocCount = Nothing, termSize = Nothing, termShardSize = Nothing, termCollectMode = Just BreadthFirst, termExecutionHint = Nothing, termAggs = Nothing})
>>> let myAggregation = mkAggregateSearch Nothing $ mkAggregations "users" terms

mkHighlightSearch is a helper function that defaults everything in a Search except for the Query and the Aggregation.

>>> let query = QueryMatchQuery $ mkMatchQuery (FieldName "_all") (QueryString "haskell")
>>> let testHighlight = Highlights Nothing [FieldHighlight (FieldName "message") Nothing]
>>> let search = mkHighlightSearch (Just query) testHighlight

bulk uses Elasticsearch's bulk API to perform bulk operations. The BulkOperation data type encodes the index/update/delete/create operations. You pass a Vector of BulkOperations and a Server to bulk in order to send those operations up to your Elasticsearch server to be performed. I changed from [BulkOperation] to a Vector due to memory overhead.

>>> let stream = V.fromList [BulkIndex testIndex testMapping (DocId "2") (toJSON (BulkTest "blah"))]
>>> _ <- runBH' $ bulk stream
>>> _ <- runBH' $ refreshIndex testIndex

pageSearch is a helper function that takes a search and assigns the from and size fields for the search. The from parameter defines the offset from the first result you want to fetch. The size parameter allows you to configure the maximum amount of hits to be returned.

>>> let query = QueryMatchQuery $ mkMatchQuery (FieldName "_all") (QueryString "haskell")
>>> let search = mkSearch (Just query) Nothing
>>> search
Search {queryBody = Just (QueryMatchQuery (MatchQuery {matchQueryField = FieldName "_all", matchQueryQueryString = QueryString "haskell", matchQueryOperator = Or, matchQueryZeroTerms = ZeroTermsNone, matchQueryCutoffFrequency = Nothing, matchQueryMatchType = Nothing, matchQueryAnalyzer = Nothing, matchQueryMaxExpansions = Nothing, matchQueryLenient = Nothing, matchQueryBoost = Nothing})), filterBody = Nothing, sortBody = Nothing, aggBody = Nothing, highlight = Nothing, trackSortScores = False, from = From 0, size = Size 10, searchType = SearchTypeQueryThenFetch, fields = Nothing, source = Nothing}
>>> pageSearch (From 10) (Size 100) search
Search {queryBody = Just (QueryMatchQuery (MatchQuery {matchQueryField = FieldName "_all", matchQueryQueryString = QueryString "haskell", matchQueryOperator = Or, matchQueryZeroTerms = ZeroTermsNone, matchQueryCutoffFrequency = Nothing, matchQueryMatchType = Nothing, matchQueryAnalyzer = Nothing, matchQueryMaxExpansions = Nothing, matchQueryLenient = Nothing, matchQueryBoost = Nothing})), filterBody = Nothing, sortBody = Nothing, aggBody = Nothing, highlight = Nothing, trackSortScores = False, from = From 10, size = Size 100, searchType = SearchTypeQueryThenFetch, fields = Nothing, source = Nothing}

mkShardCount is a straight-forward smart constructor for ShardCount which rejects Int values below 1 and above 1000.

>>> mkShardCount 10
Just (ShardCount 10)

mkReplicaCount is a straight-forward smart constructor for ReplicaCount which rejects Int values below 0 and above 1000.

>>> mkReplicaCount 10
Just (ReplicaCount 10)

getStatus fetches the Status of a Server

>>> serverStatus <- runBH' getStatus
>>> fmap tagline (serverStatus)
Just "You Know, for Search"

getSnapshotRepos gets the definitions of a subset of the defined snapshot repos.

Create or update a snapshot repo

Verify if a snapshot repo is working. NOTE: this API did not make it into Elasticsearch until 1.4. If you use an older version, you will get an error here.

Create and start a snapshot

Get info about known snapshots given a pattern and repo name.

Delete a snapshot. Cancels if it is running.

Restore a snapshot to the cluster See https://www.elastic.co/guide/en/elasticsearch/reference/1.7/modules-snapshots.html#_restore for more details.

encodeBulkOperations is a convenience function for dumping a vector of BulkOperation into an ByteString

>>> let bulkOps = V.fromList [BulkIndex testIndex testMapping (DocId "2") (toJSON (BulkTest "blah"))]
>>> encodeBulkOperations bulkOps
"\n{\"index\":{\"_type\":\"tweet\",\"_id\":\"2\",\"_index\":\"twitter\"}}\n{\"name\":\"blah\"}\n"

encodeBulkOperation is a convenience function for dumping a single BulkOperation into an ByteString

>>> let bulkOp = BulkIndex testIndex testMapping (DocId "2") (toJSON (BulkTest "blah"))
>>> encodeBulkOperation bulkOp
"{\"index\":{\"_type\":\"tweet\",\"_id\":\"2\",\"_index\":\"twitter\"}}\n{\"name\":\"blah\"}"

This is a hook that can be set via the bhRequestHook function that will authenticate all requests using an HTTP Basic Authentication header. Note that it is *strongly* recommended that this option only be used over an SSL connection.

> (mkBHEnv myServer myManager) { bhRequestHook = basicAuthHook (EsUsername "myuser") (EsPassword "mypass") }

Was there an optimistic concurrency control conflict when indexing a document?

Tries to parse a response body as the expected type a and failing that tries to parse it as an EsError. All well-formed, JSON responses from elasticsearch should fall into these two categories. If they don't, a EsProtocolException will be thrown. If you encounter this, please report the full body it reports along with your Elasticsearch verison.