Compares features and performance of 11 Json parsers that can be used from Scala.

1. SCALA JSON FEATURES AND
PERFORMANCES
John Nestor- 47 Degrees
nestor@persist.com
Dragos Manolescu
dam@micro-workflow.com
https://github.com/47deg/json-perf
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DISCLAIMER
• Best effort attempt to measure performance and
describe features.
• Corrections always appreciated.
• Also let us know any Json parsers we missed.
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• There are lots of Scala Json parsers
• You can also use Java Json parsers in Scala
• How to Choose:
• Performance
• Features
• API
• Support (will not be abandoned)
• License (most are Apache 2)
SCALA JSON
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SCALA (2.11) JSON PARSERS
Parser URL Version Language
Persist Json https://github.com/nestorpersist/json 1.1.0 Scala
Rojoma https://github.com/rjmac/rojoma-json 3.3.0 Scala
Jackson
http://wiki.fasterxml.com/
JacksonHome
2.5.3 Scala/Java
Spray Json https://github.com/spray/spray-json 1.3.2 Scala
Lift Json
https://github.com/lift/lift/tree/master/
framework/lift-base/lift-json/
2.6.2 Scala
Twitter Json https://github.com/stevej/scala-json NA Scala
Scala Library
https://github.com/scala/scala-parser-
combinators
1.0.4 Scala
Play Json
https://www.playframework.com/
documentation/2.0/ScalaJson
2.4.1 Scala/Java
Json Smart
https://github.com/netplex/json-smart-
v2
2.1.0 Java
Argonaut http://argonaut.io/ 6.0.4 Scala
JAWN https://github.com/non/jawn 0.8.0 Scala
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THE PARSERS (1 OF 4)
• Scala Library. This parser is part of the standard Scala
library in package scala.util.parsing.json. It is
implemented using parsing combinators.
• Twitter Json. A cleaned up version of the JSON parser in
Odersky's Scala book. It is implemented using parsing
combinators. Written by Steve Jenson while at Twitter.
• Persist Json. Developed as part of the OStore, a new
NoSQL database written in Scala. OStore started with the
Twitter parser. This turned out to be much too slow, so it
was rewritten from scratch keeping mostly the same API but
with an emphasis on speed. Developed by John Nestor
(with the codex based mapper by JR Dejardin).
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THE PARSERS (2 OF 4)
• Play Json. A part of the Typesafe Play framework.
Implemented using Jerkson, a Scala wrapper on
Jackson.
• Lift Json. Developed as part of Lift, a framework for
building web apps.
• Spray Json. Developed as part of Spray, a REST/
HTTP network IO toolkit.
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THE PARSERS (3 OF 4)
• Argonaut. Purely functional Json in Scala. Uses
Scalaz.
• Rojama. Another Scala parser that makes extensive
use of Scala’s functional features. Developed by Robert
Macomber of Socrata.
• Jawn. Jawn was designed to parse JSON into an AST
as quickly as possible.
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THE PARSERS (4 OF 4)
• Jackson. Generally regarded as the best and
fastest Java Json parser. Has a very rich set of
features. We test using the DefaultScalaModule
(by Chris Currie) that provides Scala support.
• Json Smart. A newer faster (than Jackson) Json
parser written in Java.
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TEST SETS FOR PERFORMANCE TESTING
• Twitter. Tweets processed by the Yap.tv Guide
(http://j.mp/15WL0p3), a service providing a personalized TV
guide companion experience based on social content from
Twitter and Facebook.This data set contains 100 tweets in Json
(http://j.mp/13lKbU6).
• Google. PlaceSearchResults returned by Google in response
to place queries at 100 locations. The locations correspond to
the top best places to live in 2012, as compiled by CNN Money
(http://j.mp/13NmVid). This data set contains 138
PlaceSearchResults in Json (http://j.mp/13NmCUC) using
keyword “brewery” and a radius of 2 miles.
• Each file has one Json object per line.
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PRETTY SAMPLE TWITTER JSON
{"contributors":null,
"coordinates":null,
"created_at":"Mon Jun 27 21:45:46 +0000 2011",
"entities":
{"hashtags":[],
"urls":
[{"display_url":"mercynotes.com",
"expanded_url":"http://www.mercynotes.com/",
"indices":[61,80],
"url":"http://t.co/lKzLFOd"
}
],
"user_mentions":[]
},
"favorited":false,
"geo":null,
"id":85463859615379456,
"id_str":"85463859615379456",
"in_reply_to_screen_name":null,
"in_reply_to_status_id":null,
"in_reply_to_status_id_str":null,
"in_reply_to_user_id":null,
"in_reply_to_user_id_str":null,
"place":null,
"retweet_count":0,
"retweeted":false,
"source":"web",
"text":
"Been watching Wimbledon? Check out new post Love and Tennis: http://t.co/lKzLFOd",
"truncated":false,
"user":
{"contributors_enabled":false,
"created_at":"Mon May 30 16:35:44 +0000 2011",
"default_profile":true,
"default_profile_image":false,
"description":"",
"favourites_count":0,
"follow_request_sent":null,
"followers_count":6,
"following":null,
"friends_count":12,
"geo_enabled":false,
"id":307978890,
"id_str":"307978890",
"is_translator":false,
"lang":"en",
"listed_count":0,
"location":"NC",
"name":"Julie LaJoe",
"notifications":null,
"profile_background_color":"C0DEED",
"profile_background_image_url":
"http://a0.twimg.com/images/themes/theme1/bg.png",
"profile_background_image_url_https":
"https://si0.twimg.com/images/themes/theme1/bg.png",
"profile_background_tile":false,
"profile_image_url":
"http://a0.twimg.com/profile_images/1375001769/JulieMNnew__2__normal.jpg",
"profile_image_url_https":
"https://si0.twimg.com/profile_images/1375001769/JulieMNnew__2__normal.jpg",
"profile_link_color":"0084B4",
"profile_sidebar_border_color":"C0DEED",
"profile_sidebar_fill_color":"DDEEF6",
"profile_text_color":"333333",
"profile_use_background_image":true,
"protected":false,
"screen_name":"mercynotes",
"show_all_inline_media":false,
"statuses_count":13,
"time_zone":"Quito",
"url":"http://mercynotes.com",
"utc_offset":-18000,
"verified":false
}
}
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PRETTY SAMPLE GOOGLE JSON{"address_components":
[{"long_name":"622",
"short_name":"622",
"types":["street_number"]
},
{"long_name":"South Rangeline Road",
"short_name":"South Rangeline Road",
"types":["route"]
},
{"long_name":"Carmel",
"short_name":"Carmel",
"types":["locality","political"]
},
{"long_name":"Hamilton",
"short_name":"Hamilton",
"types":
["administrative_area_level_2","political"]
},
{"long_name":"Indiana",
"short_name":"Indiana",
"types":
["administrative_area_level_1","political"]
},
{"long_name":"US",
"short_name":"US",
"types":["country","political"]
},
{"long_name":"46032",
"short_name":"46032",
"types":["postal_code"]
}
],
"formatted_address":
"Suite Q, 622 South Rangeline Road, Carmel, Indiana, United States",
"formatted_phone_number":"(317) 429-6345",
"geometry":
{"location":{"lat":39.971703,"lng":-86.129099}},
"icon":
"http://maps.gstatic.com/mapfiles/place_api/icons/generic_business-71.png",
"id":"fcd83d32717980ec1fec2c7ec8719389b201a331",
"international_phone_number":"+1 317-429-6345",
"name":"Union Brewing Company",
"opening_hours":
{"open_now":true,
"periods":
[{"close":{"day":0,"time":"2000"},
"open":{"day":0,"time":"1200"}
},
{"close":{"day":2,"time":"2200"},
"open":{"day":2,"time":"1600"}
},
{"close":{"day":4,"time":"2200"},
"open":{"day":4,"time":"1600"}
},
{"close":{"day":6,"time":"0000"},
"open":{"day":5,"time":"1500"}
},
{"close":{"day":0,"time":"0000"},
"open":{"day":6,"time":"1200"}
}
]
},
"photos":
[{"height":1632,
"html_attributions":
["<a href="https://plus.google.com/117934275405882297051">Greg Magnusson</a>"
],
"photo_reference":
"CnRoAAAAvN9y_gkgZIGa13kUSyyBlqwholvjtH4NKo-BzvlklcX-Tt9Ysc6HRMXPxKl3PumZtiOnomHi-Nk83y-lxf8RX8nsWulwuCBpY2okAqaU9wohOhncStFPZlKr02t3WquA6pt8mfCYYO-
NAdU2HwdM1hIQYJmus4wpQBaRtP7BFdYhzRoU4XvzfAAQQwkdJZluFJ-tDoUulIo",
"width":1224
}
],
"reference":
"CoQBcgAAAF3VKrWBUmLMv5tLs1Ru47j3Tbxa6lPxlIFj5BUvpsTyPt3bpui2vOTCcaHjKYuAjSulIPHpd0YFgm5CKLQH6P_19xU1UPeu6avWeIMWA0u4hxyx4TazCfFF9ESCwHaOEcKZfRyJSD2b5p2IJvT0eVkFFExeWbqAcWrH80jIQ-
VrEhAvUSpbmH3rB4LEKn-cZtsYGhQxFpeco4U1rUtwe-ncAttqLBnSgQ",
"reviews":
[{"aspects":[{"rating":3,"type":"quality"}],
"author_name":"Greg Magnusson",
"author_url":
"https://plus.google.com/117934275405882297051",
"text":
"Truly outstanding local craft brewing company. Indy&#39;s got some great local brewers, but these guys really get it right. Nice little location in Carmel, great beer and local guest taps...
I&#39;m so glad these guys moved into town. Love!",
"time":1361059887
}
],
"types":["food","establishment"],
"url":
"https://plus.google.com/102928473191458623183/about?hl=en-US",
"utc_offset":-300,
"vicinity":
"Suite Q, 622 South Rangeline Road, Carmel",
"website":"http://www.unionbrewingco.com"
}
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• Timing is done with Java System.nanotime().
• For each data set, each line is processed.
• This is repeated 25 time to warm JVM.
• This is repeated 200 times for measurement.
• For example, google has 138 Json lines, so during
warmup a total of 3450 lines are parsed and during
testing 27600 lines are parsed.
• The total summed nanoseconds for all 27600 parse
steps are reported as milliseconds for each parser.
TESTING PROCESS
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TIMING SCALA/JAVA CODE
• Timing is tricky! For example see
• http://www.ibm.com/developerworks/library/j-
benchmark1/
• A few of the many issues:
• Warmup (run several times to warm JVM)
• Repeatability (use average?, but what about P99?)
• Interference from other processes
• Caches
• Garbage collection
• Chosen data set
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TESTING MACHINE
• Times obviously depend on speed of machine used in
testing.
• Numbers here are for a MacBook pro with
• 2 2.9 GHz cores
• 16GB of main memory
• You can run tests on a machine of your choice!
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PARSING TIMES (MS)
Parser Twitter Google Ignore
Persist Json 443 712
Rojoma 540 1251
Jackson 445 842
Spray Json 603 1115
Lift Json 469 1002
Twitter Json 18179 42316 Too Slow
Scala Library 126006 329215 Way Too Slow
Play Json 442 1027
Json Smart 251 424
Argonaut 784 1448
JAWN 603 748
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PARSING TIMES - TWITTER
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PARSING TIMES - GOOGLE
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WHY IS TWITTER SLOW?
• Parsing combinators.
Elegant but slow.
• Interpreted.
Backtracking.
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def value: Parser[Any] = obj | arr | string | number |
"null" ^^ (x => null) | "true" ^^ (x => true) | "false"
^^ (x => false)
def obj: Parser[Map[String, Any]] = "{" ~> repsep(member,
",") <~ "}" ^^ (Map.empty ++ _)
def arr: Parser[List[Any]] = "[" ~> repsep(value, ",") <~
"]"
def member: Parser[(String, Any)] = string ~ ":" ~ value
^^ {
case name ~ ":" ~ value => (name, value)
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WHY IS THE SCALA LIBRARY EVEN
SLOWER?
• Like Twitter uses parsing combinators.
• But why is it so much slower?
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WHY IS PLAY SO SLOW IF IT USES
JACKSON?
• It uses Jerkson (which is abandoned)?
• ???
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JSON LANGUAGE EXTENSIONS
Parser Comments NoQuotes Root Type Other
Persist Json // field any raw strings
Rojoma //,/**/ field any keeps field order
Jackson // field object can use ‘
Spray Json // any
Lift Json object keeps field order
Twitter Json any
Scala Library any
Play Json object
Json Smart # field/value object
Argonaut object keeps field order
JAWN object
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PARSER RESULTS (ASTS)
Parser Object, Array
Wrapped in
Object
Immutable Collections
Persist Json Map, List no yes Scala
Rojoma LinkedHashMap, Vector yes no Scala
Jackson Map, List yes no Java
Spray Json Map, Vector yes yes Scala
Lift Json List[Field], List yes yes Scala
Twitter Json Map, List no yes Scala
Scala Library Map, List yes yes Scala
Play Json Map, List yes yes Scala
Json Smart HashMap, List yes no Java
Argonaut scalaz.InsertionMap, List yes yes Scala
JAWN Map, Array yes no Scala
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UNPARSING
• The inverse of parsing (deserialization) is unparsing
(serialization).
• Unparsing takes the AST from parsing and converts it back to
a string.
• Useful for debugging and logging.
• Many parsers also include a pretty printed unparser.
• Timing here for the “non-pretty” simple form.
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UN-PARSING TIMES (MS)
Parser Twitter Google
Persist Json 622 1172
Rojoma 226 511
Jackson 11 29
Spray Json 232 676
Lift Json 1125 3211
Play Json 322 323
Json Smart 349 934
Argonaut 1005 2468
JAWN 498 1161
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UN-PARSING TIMES - TWITTER
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UN-PARSING TIMES - GOOGLE
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WHY IS JACKSON SO INCREDIBLY FAST?
• Uses SegmentedStringBuilder (rather than
StringBuilder).
• Uses segmented internal buffer.
• Buffers are recycled.
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WHY IS PERSIST SLOW?
• Uses raw Seq and Map rather than being
wrapped in custom classes.
• Must use pattern match rather than virtual
dispatch to a virtual method.
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MAPPERS
• Parsers go from string to AST
• Mappers go to user specified case classes
• Twitter, Scala Library, Json Smart, JAWN: no
mapper
• Jackson, Argonaut, Rojoma:
string => case classes
• Others: string => AST => case classes
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DYNAMIC VERSUS STATIC TYPING
• Dynamic: AST. More flexible and agile. No additional code
needed for parsing. Can be used on any valid json data. But
need extra code if more checking is needed.
• Static: User Specified Case Classes. Must specify case
classes before parsing can proceed. More checking. Can
attach behavior to case classes.
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MAPPING TIMES (MS)
Parser Twitter Google
Persist Json 622 2238
Rojoma 1117 2669
Jackson 326 1150
Spray Json 557 1675
Lift Json 520 2060
Play Json 1123 3768
Argonaut 937 2550
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MAPPING TIMES - TWITTER
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MAPPING TIMES - GOOGLE
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MAPPERS
Parser Extra Code Lines Why
Persist Json 0
Rojoma 135 case classes, Array=>Seq
Jackson 0
Spray Json 16 case classes
Lift Json 7 BigDecimal
Play Json 16 case classes
Argonaut 180
case classes, Array=>List,
Seq=>List, BigDecimal=>Double
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AVOIDING EXTRA CODE
• Find types of case class parameter names. Java
reflection works.
• Find names of case class parameters. Prior to Java 8
not available via Java reflection. Scala reflection
however does work.
• Reflection can be quite slow. Caching can help!
• Persist: Shapeless
• Lift and Jackson: Paranamer. Gets info from reading
Java byte code symbol tables.
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SUMMARY
• Avoid: Scala Library, Twitter
• Fast parse and no other features: Json Smart
• Good overall choices: Jackson, Persist, Spray
• Very fast unparse: Jackson
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QUESTIONS
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QUESTIONS

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THANKS!
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QUESTIONS
To contact me or 47 Degrees:
Email
nestor@persist.com
hello@47deg.com
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