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Associated  JIRA ticket : SQOOP-1350 and its sub tickets for more granular discussion points

**The following details are relevant post 1.99.4 release, i,e from 1.99.5 onwards.


Intermediate Data Format (IDF)

  • Connectors have FROM and TO parts. A sqoop job represents data transfer between FROM and TO. IDF API represents how the data is represented as it flows between the FROM and TO via sqoop.  
  • Connectors basically represent different data sources and each data source can have its custom/ native format that it uses. For instance MongoDb might use JSON as its optimal native format, HDFS can use plain CSV text, S3 can use its own custom format. In simple words, every data source has one thing in common, it is collection of rows and each row is a collection of fields / columns. Most if not all data sources have strict schema that tells what each field type is. IDF encapsulated the native format and the schema associated with each field.
  • Before we understand IDF API, it is important to be aware of the two other low level APIs that sqoop defines for data reading and writing between the FROM and TO data sources


DataReader  Expand source



DataWriter  Expand source



  • IDF API is primarily influenced by the above low level apis to read and write data and hence this API dictates that each custom implementations support the following 3 formats
  1. Native format - each row in the data source is a native object, for instance in JSONIDF, an entire row and its fields in sqoop will be represented as a JSON object, in AvroIDF, entire row and its fields will be represented as a Avro record
  2. CSV text format - each row and its fields are represented as CSV text
  3. Object Array format  - each field in the row is an element in the object array. Hence a row in the data source is represented as a object array


IntermediateDataFormat API  Expand source

NOTE: The CSV Text format and the Object Array format are custom to Sqoop and the details of this format for every supported column/field type in the schema are described below.

Schema ( a.k.a Row ) 

Schema represents a row of fields that are transferred between FROM and TO. Hence schema holds the list of column/ field types for that row. 

Schema  Expand source

Column & ColumnType ( a.k.a Row Fields )

Column is an abstraction to represent a field in a row. There are custom classes for sub type such as String, Number, Date, Map, Array. It has attributes that provide metadata about the column data such as is that field nullable?, if that field is a String, what is its maxsize?, if it is DateTime, does it support timezone?, if it is a Map, what is the type of the key and what the is the type of the value?, if it is Array, what is the type of the elements?, if it is Enum, what are the supported options for the enum?

Column  Expand source

ColumnType is an handy enum that represents all the field types Sqoop supports. Note there is a umbrella UNKNOWN type for fields that sqoop does not support.

ColumnType  Expand source

Design goals for IDF

There are a few prior documents that depict the design goals, but it is not crystal clear. Refer to this doc for some context on the research done prior to defining the IDF API. It explains some of the goals of using CSV and Object Array formats. Some of the design influence comes from the its predecessor Sqoop1.

  1. Support data transfer across connectors using an "internal" in memory data representation
  2. CSV is a common format in many databases and hence sqoop's design goals primarily want to optimize for such data sources. But it is unclear how much of performance gains does CSV text provide. 
    The following is a java doc comment I pulled from the code that explains the choice of CSV.

    Why a "native" internal format and then return CSV text too?
    Imagine a connector that moves data from a system that stores data as a
    serialization format called FooFormat. If I also need the data to be
    written into HDFS as FooFormat, the additional cycles burnt in converting
    the FooFormat to text and back is useless - so using the sqoop specified
    CSV text format saves those extra cycles
    Most fast access mechanisms, like mysqldump or pgsqldump write the data
    out as CSV, and most often the source data is also represented as CSV
    - so having a minimal CSV support is mandated for all IDF, so we can easily read the
    data out as text and write as text.

  3. has discussions that detail the current IDF API design goals 


The following is the spec as per 1.99.5, Please do not edit this directly in future. If there is format spec change in future releases add a new section to highlight what changed.

1.99.5 SQOOP CSV and Object Format in IDF

Column TypeCSV FormatObject Format
NULL value in the field

  public static final String NULL_FIELD = "NULL";

java null
  • Will be encoded as String (and hence enclosed with '\, inside there will be JSON encoding of the top level array elements (hence the entire value will be enclosed in [] pair), Nested values are not JSON encoded..
  • Few examples:
    • Array of FixedPoint '[1,2,3]'
    • Array of Text '["A","B","C"]'
    • Array of Objects of type FixedPoint '[[11, 12],[14, 15]]'
    • Array of Objects of type Text '[[A, B],[X, Y]]' 

Refer for more details

java Object[]
byte array enclosed in quotes and encoded with ISO-8859-1 charsetjava byte[]

true, TRUE, 1

false, FALSE, 0

( not encoded in quotes )

Unsupported values should throw an exception

java boolean

YYYY-MM-DD ( no time)org.joda.time.LocalDate

YYYY-MM-DD HH:MM:DD[.ZZZ][+/-XX] ( fraction and timezone are optional)

Refer for more details

org.joda.time. DateTime


org.joda.time. LocalDateTime

(depends on timezone attribute )


BigDecimal (not encoded in quotes ),


java BigDecimal

scale and precision fields are handled via :

Same as TEXTjava String

Integer or Long, ( not encoded in quotes )

java Integer


java Long

( depends on

byteSize attribute

and signed attribute)

Float or Double ( not encoded in quotes )

java Double


java Float

( depends on

byteSize attribute)

  • Will be encoded as String (and hence enclosed with '\, inside there will be JSON encoding of the map (hence the entire value will be enclosed in  pair { }, nested values are also encoded as JSON
  • Map<Number, Number> '{1:20}'
  • Map<String, String> - '{"testKey":"testValue"}'

    Refer for more details
java.util.Map<Object, Object>
same as ARRAY

java Object[]


Entire string will be enclosed in single quotes and all bytes will be printed as they are will exception of following bytes


Encoded as


\ \ (no space) 













java String

HH:MM:DD[.ZZZ] ( fraction is optional )

3 digit milli second support only for time

org.joda.time.LocalTime ( No Timezone)
same as BINARYsame as java byte[]

Custom Implementations of IDF


Relevant JIRA : SQOOP-555 and SQOOP-1350

It is one of the sample implementation of the IDF API.

CSV IDF piggy backs on the the Sqoop CSV Format and its native format is the CSV Format represented as String. 

See the implementation class in the connector-sdk package for more details

NOTE: It may not be obvious but the current IDF design expect every new implementation of it to expose the CSV an ObjectArray formats in addition to its native format.


Relevant JIRA: SQOOP-1901

Avro Intermediate Data Format


It is a utility class in sqoop to aid connectors in encoding data into expected CSV format and object format and also parsing the CSV string back to the prescribed object format.


Food for Thought.?

(Some of the below are some serious shortcomings of the current design as it exists)

  • The choice of using CSVText  as mandated formats for Sqoop IDF are influenced from the Sqoop 1 design, It favors some traditional fast dump databases but there is no real benchmark to prove how optimal it is vs using Avro or other formats for representing the data
  • Using  intermediate format might lead to discrepancies in some specific column types, for instance using JODA for representing the date time objects only gives 3 digit precision, where as the sql timestamp from JDBC sources supports 6 digit precision
  • More importantly SqoopConnector API has a getIDF..() method, that ties a connector to a  specific intermediate format for all the supported directions ( i.e both FROM and TO at this point) . This means the connector in both FROM and TO side has to provide this format and expect this format respectively. 
  • There are 3 different formats as described above in each IDF implementation, so each connector can potentially support one of these formats and that is not obvious at all when a connector proclaims to use a particular implementation of IDF such as CSVIntermediateDataFormat. For instance the GenericJDBCConnector says it uses CSVIntermediateDataFormat but chooses to write objectArray in extractor and readObjectArray in Loader. Hence it is not obvious what is the format underneath that it will read and write to. On the other hand, HDFSConnector also says it uses CSVIntermediateDataFormat but, uses only the CSV text format in the extractor and loader at this point. May change in future. 
  • A connector possibly should be able to handle multiple IDFs, and expose the supported IDFs per direction. It is not possible today, For instance a sqoop job should be able to dynamically choose the IDF for HDFSConnector when used in the TO direction. The job might be able to say, use AVRO IDF for the TO side and hence load all my data into HDFS in avro format. This means when doing the load, the HDFS will use the readContent API of the  SqoopOutputFormatDataReader. But today HDFS can only say it uses CSVIntermediateDataFormat and the data loaded into HDFS will need conversion from CSV to Avro as a separate step.

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