Haskell Elasticsearch client and query DSL
* Bloodhound
#+CAPTION: Bloodhound (dog)
[[./bloodhound.jpg]]
#+CAPTION: Build Status
[[https://travis-ci.org/bitemyapp/bloodhound][https://travis-ci.org/bitemyapp/bloodhound.svg]]
* 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
#+BEGIN_SRC haskell
-- 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
#+END_SRC
*** Delete Index
**** Code
#+BEGIN_SRC haskell
-- response :: Reply
response <- deleteIndex testServer testIndex
#+END_SRC
**** Example Response
#+BEGIN_SRC haskell
-- 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}
#+END_SRC
*** Refresh Index
**** Note, you *have* to do this if you expect to read what you just wrote
#+BEGIN_SRC haskell
resp <- refreshIndex testServer testIndex
#+END_SRC
**** Example Response
#+BEGIN_SRC haskell
-- 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}
#+END_SRC
** Mapping Operations
*** Create Mapping
#+BEGIN_SRC haskell
-- 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
#+END_SRC
*** Delete Mapping
#+BEGIN_SRC haskell
resp <- deleteMapping testServer testIndex testMapping
#+END_SRC
** Document Operations
*** Indexing Documents
#+BEGIN_SRC haskell
-- 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")
#+END_SRC
**** Example Response
#+BEGIN_SRC haskell
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}
#+END_SRC
*** Deleting Documents
#+BEGIN_SRC haskell
resp <- deleteDocument testServer testIndex testMapping (DocId "1")
#+END_SRC
*** Getting Documents
#+BEGIN_SRC haskell
-- 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}})
#+END_SRC
** Bulk Operations
*** Bulk create, index
#+BEGIN_SRC haskell
-- 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
:}
#+END_SRC
** Search
*** Querying
**** Term Query
#+BEGIN_SRC haskell
-- 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}}}]
#+END_SRC
**** Match Query
#+BEGIN_SRC haskell
let query = QueryMatchQuery $ mkMatchQuery (FieldName "user") (QueryString "bitemyapp")
let search = mkSearch (Just query) Nothing
#+END_SRC
**** Multi-Match Query
#+BEGIN_SRC haskell
let fields = [FieldName "user", FieldName "message"]
let query = QueryMultiMatchQuery $ mkMultiMatchQuery fields (QueryString "bitemyapp")
let search = mkSearch (Just query) Nothing
#+END_SRC
**** Bool Query
#+BEGIN_SRC haskell
let innerQuery = QueryMatchQuery $
mkMatchQuery (FieldName "user") (QueryString "bitemyapp")
let query = QueryBoolQuery $
mkBoolQuery [innerQuery] [] []
let search = mkSearch (Just query) Nothing
#+END_SRC
**** Boosting Query
#+BEGIN_SRC haskell
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)
#+END_SRC
**** Rest of the query/filter types
Just follow the pattern you've seen here and check the Hackage API documentation.
*** Sorting
#+BEGIN_SRC haskell
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
#+END_SRC
*** Filtering
**** And, Not, and Or filters
Filters form a monoid and seminearring.
#+BEGIN_SRC haskell
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
#+END_SRC
**** Identity Filter
#+BEGIN_SRC haskell
-- And'ing two Identity
let queryFilter = IdentityFilter <&&> IdentityFilter
let search = mkSearch Nothing (Just queryFilter)
reply <- searchByType testServer testIndex testMapping search
#+END_SRC
**** Boolean Filter
Similar to boolean queries.
#+BEGIN_SRC haskell
-- 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)
#+END_SRC
**** Exists Filter
#+BEGIN_SRC haskell
-- Will filter for documents that have the field "user"
let existsFilter = ExistsFilter (FieldName "user")
#+END_SRC
**** Geo BoundingBox Filter
#+BEGIN_SRC haskell
-- 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
#+END_SRC
**** Geo Distance Filter
#+BEGIN_SRC haskell
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
#+END_SRC
**** Geo Distance Range Filter
Think of a donut and you won't be far off.
#+BEGIN_SRC haskell
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
#+END_SRC
**** Geo Polygon Filter
#+BEGIN_SRC haskell
-- 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
#+END_SRC
**** Document IDs filter
#+BEGIN_SRC haskell
-- takes a mapping name and a list of DocIds
IdsFilter (MappingName "tweet") [DocId "1"]
#+END_SRC
**** Range Filter
***** Full Range
#+BEGIN_SRC haskell
-- RangeFilter :: FieldName
-- -> Either HalfRange Range
-- -> RangeExecution
-- -> Cache -> Filter
let filter = RangeFilter (FieldName "age")
(Right (RangeLtGt (LessThan 100000.0) (GreaterThan 1000.0)))
RangeExecutionIndex False
#+END_SRC
***** Half Range
#+BEGIN_SRC haskell
let filter = RangeFilter (FieldName "age")
(Left (HalfRangeLt (LessThan 100000.0)))
RangeExecutionIndex False
#+END_SRC
**** Regexp Filter
#+BEGIN_SRC haskell
-- 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)
#+END_SRC
*** Aggregations
**** Adding aggregations to search
Aggregations can now be added to search queries, or made on their own.
#+BEGIN_SRC haskell
type Aggregations = M.Map Text Aggregation
data Aggregation
= TermsAgg TermsAggregation
| DateHistogramAgg DateHistogramAggregation
#+END_SRC
For convenience, ```mkAggregations``` exists, that will create an
```Aggregations``` with the aggregation provided.
For example:
#+BEGIN_SRC haskell
let a = mkAggregations "users" $ TermsAgg $ mkTermsAggregation "user"
let search = mkAggregateSearch Nothing a
#+END_SRC
Aggregations can be added to an existing search, using the
```aggBody``` field
#+BEGIN_SRC haskell
let search = mkSearch (Just (MatchAllQuery Nothing)) Nothing
let search' = search {aggBody = Just a}
#+END_SRC
Since the ```Aggregations``` structure is just a Map Text
Aggregation, M.insert can be used to add additional aggregations.
#+BEGIN_SRC haskell
let a' = M.insert "age" (TermsAgg $ mkTermsAggregation "age") a
#+END_SRC
**** Extracting aggregations from results
Aggregations are part of the reply structure of every search, in the
form of ~Maybe AggregationResults~
#+BEGIN_SRC haskell
-- 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}]})
#+END_SRC
Note that bucket aggregation results, such as the TermsResult is a
member of the type class ~BucketAggregation~:
#+BEGIN_SRC haskell
class BucketAggregation a where
key :: a -> Text
docCount :: a -> Int
aggs :: a -> Maybe AggregationResults
#+END_SRC haskell
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
#+BEGIN_SRC haskell
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}
#+END_SRC
Term Aggregations have two factory functions,
~mkTermsAggregation~, and ~mkTermsScriptAggregation~, and can
be used as follows:
#+BEGIN_SRC haskell
let ta = TermsAgg $ mkTermsAggregation "user"
#+END_SRC
There are of course other options that can be added to a Terms
Aggregation, such as the collection mode:
#+BEGIN_SRC haskell
let ta = mkTermsAggregation "user"
let ta' = ta { termCollectMode = Just BreadthFirst }
let ta'' = TermsAgg ta'
#+END_SRC
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
#+BEGIN_SRC haskell
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}
#+END_SRC haskell
The Date Histogram Aggregation works much the same as the Terms
Aggregation.
Relevant functions include ~mkDateHistogram~, and ~toDateHistogram~
#+BEGIN_SRC haskell
let dh = DateHistogramAgg (mkDateHistogram (FieldName "postDate") Minute)
#+END_SRC
Date histograms also accept a ~FractionalInterval~:
#+BEGIN_SRC haskell
FractionalInterval :: Float -> TimeInterval -> Interval
-- TimeInterval is the following:
data TimeInterval = Weeks | Days | Hours | Minutes | Seconds
#+END_SRC
It can be used as follows:
#+BEGIN_SRC haskell
let dh = DateHistogramAgg (mkDateHistogram (FieldName "postDate") (FractionalInterval 1.5 Minutes))
#+END_SRC
The ~DateHistogramResult~ is defined as:
#+BEGIN_SRC haskell
data DateHistogramResult
= DateHistogramResult {dateKey :: Int,
dateKeyStr :: Maybe Text,
dateDocCount :: Int,
dateHistogramAggs :: Maybe AggregationResults}
#+END_SRC
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/