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JsonSerde - a read/write SerDe for JSON Data

Build Status:

• master :
• develop:

This library enables Apache Hive to read and write in JSON format. It includes support for serialization and deserialization (SerDe) as well as JSON conversion UDF.

Features

• Read data stored in JSON format
• Convert data to JSON format during INSERT INTO <table>
• Support for JSON arrays and maps
• Support for nested data structures
• Support for Cloudera's Distribution Including Apache Hadoop (CDH)
• Support for multiple versions of Hadoop

Installation

Download the latest binaries (json-serde-X.Y.Z-jar-with-dependencies.jar and json-udf-X.Y.Z-jar-with-dependencies.jar) from congiu.net/hive-json-serde. Choose the correct verson for CDH 4, CDH 5 or Hadoop 2.3. Place the JARs into hive/lib or use ADD JAR in Hive.

JSON Data Files

Upload JSON files to HDFS with hadoop fs -put or LOAD DATA LOCAL. JSON records in data files must appear one per line, an empty line would produce a NULL record. This is because Hadoop partitions files as text using CR/LF as a separator to distribute work.

The following example will work.

{ "key" : 10 }
{ "key" : 20 }

The following example will not work.

{
  "key" : 10
}
{
  "key" : 20
}

Loading a JSON File and Querying Data

Uses json-serde/src/test/scripts/test-without-cr-lf.json.

~$ cat test.json

{"text":"foo","number":123}
{"text":"bar","number":345}

$ hadoop fs -put -f test.json /user/data/test.json

$ hive

hive> CREATE DATABASE test;

hive> CREATE EXTERNAL TABLE test ( text string )
      ROW FORMAT SERDE 'org.openx.data.jsonserde.JsonSerDe'
      LOCATION '/user/data';

hive> SELECT * FROM test;
OK

foo 123
bar 345

Querying Complex Fields

Uses json-serde/src/test/scripts/data.txt.

hive> CREATE DATABASE test;

hive> CREATE TABLE test (
        one boolean,
        three array<string>,
        two double,
        four string )
      ROW FORMAT SERDE 'org.openx.data.jsonserde.JsonSerDe'
      STORED AS TEXTFILE;

hive> LOAD DATA LOCAL INPATH 'data.txt' OVERWRITE INTO TABLE test;

hive> select three[1] from test;

gold
yellow

If you have complex json it can be tedious to create tables manually. Try hive-json-schema to build your schema from data.

See json-serde/src/test/scripts for more examples.

Defining Nested Structures

ADD JAR json-serde-1.3.7-SNAPSHOT-jar-with-dependencies.jar;

CREATE TABLE json_nested_test (
	country string,
	languages array<string>,
	religions map<string,array<int>>)
ROW FORMAT SERDE 'org.openx.data.jsonserde.JsonSerDe'
STORED AS TEXTFILE;

-- data : {"country":"Switzerland","languages":["German","French","Italian"],
-- "religions":{"catholic":[10,20],"protestant":[40,50]}}

LOAD DATA LOCAL INPATH 'nesteddata.txt' OVERWRITE INTO TABLE  json_nested_test;

select * from json_nested_test;

-- result: Switzerland	["German","French","Italian"]	{"catholic":[10,20],"protestant":[40,50]}

select languages[0] from json_nested_test;
-- result: German

select religions['catholic'][0] from json_nested_test;
-- result: 10

Using Arrays

Data in JSON arrays should be ordered identically to Hive columns, similarly to text/csv.

For example, array data as follows.

["John", 26 ]
["Mary", 23 ]

Can be imported into the following table.

CREATE TABLE people (name string, age int)

Arrays can also be nested.

CREATE TABLE complex_array (
	name string, address struct<street:string,city:string>
)

-- data:
["John", { street:"10 green street", city:"Paris" } .. ]

Importing Malformed Data

The SerDe will raise exceptions with malformed data. For example, the following malformed JSON will raise org.apache.hadoop.hive.serde2.SerDeException.

{"country":"Italy","languages" "Italian","religions":{"catholic":"90"}}

Failed with exception java.io.IOException:org.apache.hadoop.hive.serde2.SerDeException:
Row is not a valid JSON Object - JSONException: Expected a ':' after a key at 32 [character 33 line 1]

This may not be desirable if you have a few bad lines you wish to ignore. Set ignore.malformed.json in that case.

ALTER TABLE json_table SET SERDEPROPERTIES ( "ignore.malformed.json" = "true");

While this option will not make the query fail, a NULL record will be inserted instead.

NULL	NULL	NULL

Promoting a Scalar to an Array

It is a common issue to have a field that sometimes is a scalar and sometimes an array.

{ "field" : "hello", .. }
{ "field" : [ "hello", "world" ], ...

Declare your table as array<string>, the SerDe will return a one-element array of the right type, promoting the scalar.

Support for UNIONTYPE

A Uniontype is a field that can contain different types. Hive usually stores a 'tag' that is basically the index of the datatype. For example, if you create a uniontype<int,string,float>, a tag would be 0 for int, 1 for string, 2 for float as per the UnionType documentation.

JSON data does not store anything describing the type, so the SerDe will try and infer it. The order matters. For example, if you define a field f as UNIONTYPE<int,string> you will get different results.

The following data will be parsed as int, since it precedes the String type in the defintion and 123 is successfully parsed as a number.

{ "f": "123" }

The following data will parsed as a String.

{ "f": "asv" }

It's worth noting that complex Union types may not be very efficient, since the SerDe may try to parse the same data in multiple ways.

Mapping Hive Keywords

Sometimes JSON data has attributes named like reserved words in hive. For instance, you may have a JSON attribute named 'timestamp', and hive will fail when issuing a CREATE TABLE. This SerDe can map hive columns over attributes with different names using properties.

In the following example mapping.ts translates the ts field into it the JSON attribute called timestamp.

CREATE TABLE mytable (
	myfield string, ts string
) ROW FORMAT SERDE 'org.openx.data.jsonserde.JsonSerDe'
WITH SERDEPROPERTIES ( "mapping.ts" = "timestamp" )
STORED AS TEXTFILE;

Mapping Names with Periods

Hive doesn't support column names containing periods. In theory they should work when quoted in backtics, but doesn't, as noted in SO#35344480. To work around this issue set the property dots.in.keys to true in the SerDe Properties and access these fields by substituting the period with an underscore.

For example, create the following table.

CREATE TABLE mytable (
    my_field string,
    other struct<with_dots:string> )
    ROW FORMAT SERDE 'org.openx.data.jsonserde.JsonSerDe'
WITH SERDEPROPERTIES ("dots.in.keys" = "true" )

Load the following JSON.

{ "my.field" : "value" , "other" : { "with.dots" : "blah" } }

Query data substituting periods with underscores.

SELECT my_field, other.with_dots from mytable

value, blah

Case Sensitivity in mappings

Since hive is case insensitive, all JSON keys are by default lowercased, to accomodate situations where the same JSON key is in a different case. However, this may not be what you want, you may need to treat the same key with different case as two different ones. You'll then have to use mappings, since hive does not support case sensitive columns, and you'll also have to tell the SerDe not to be case insensitive (the default).

CREATE TABLE mytable (  
  time1 string, 
  time2 string)
 ROW FORMAT SERDE 'org.openx.data.jsonserde.JsonSerDe'
WITH SERDEPROPERTIES (
   "case.insensitive" = "false", --tells hive to ignore key case
   "mapping.time1"= "time", -- lowercase 'time' mapped into 'time1'
   "mapping.time2"= "Time") -- uppercase to 'time2'

-- Data: { "time" : "2012-10-22:, "Time": "2012-11-22"} 
SELECT time1,time2 from mytable

Explicit Null Value In Serialized JSON String

In order to be complaint with some object oriented systems an explicit 'null' json value is required in the serialized string. As default, Hive-JSON-Serde will not produce null values in the output serialized JSON string and just drop the key, if you do want to have explicit 'null' values in your output JSON string, use the following:

DROP TABLE tableWithNull;
CREATE EXTERNAL TABLE tableWithNull
(
    `stringCol` STRING,
    'stringNullCol' STRING,
    'stringMissingCol' STRING,
    'structCol' STRUCT<name : STRING>,
    'structNullCol' STRUCT<name : STRING>,
    'structMissingCol' STRUCT<name : STRING>
)
ROW FORMAT SERDE 'org.openx.data.jsonserde.JsonSerDe'
WITH SERDEPROPERTIES ("explicit.null" = "true");

-- JSON string: {\"stringCol\":"blabla",\"stringNullCol\":null,\"structCol\":{\"name\":\"myName\"},\"structNullCol\":{\"name\":null}}
LOAD DATA LOCAL INPATH 'pathToJsonFile.json' OVERWRITE INTO TABLE tableWithNull;

-- The output when ("explicit.null" = "true"):
-- {\"stringCol\":"blabla",\"stringNullCol\":null,\"stringMissingCol\":null,\"structCol\":{\"name\":\"myName\"},\"structNullCol\":{\"name\":null},\"structMissingCol\":null}

-- The default output or when ("explicit.null" = "false"):
-- {\"stringCol\":"blabla",\"structCol\":{\"name\":\"myName\"},\"structNullCol\":{}}

User Defined Functions (UDF)

tjson

The tjson UDF can turn array, structs or strings into JSON.

ADD JAR json-udf-X.Y.Z-jar-with-dependencies.jar;
create temporary function tjson as 'org.openx.data.udf.JsonUDF';

hive> select tjson(named_struct("name",name)) from mytest1;
OK
{"name":"roberto"}

The SerDe must also be in the classpath for the UDF to work. If not installed as a hive extra library, you should also ADD JAR theSerDe Jar

Timestamps

Note that the system default timezone is used to convert timestamps.

Contributing

See CONTRIBUTING for how to build the project.

History

This library is written by Roberto Congiu <[email protected]> during his time at OpenX Technologies, Inc..

See CHANGELOG for details.

Thanks

Thanks to Douglas Crockford for the liberal license for his JSON library, and thanks to my employer OpenX and my boss Michael Lum for letting me open source the code.