Introduction

CDAP Schema is a superset of Avro schema. This is because Avro schema only supports string keys where as CDAP schema can have arbitrary types as keys. From Avro 1.8, Date and Time is supported as Avro logical types. CDAP Schema should also support that.

Goals

Goal for this feature is to support Date/Time as CDAP Schema data types. This should also be surfaced in plugins so that plugin developers can use it.

User Stories

As a pipeline developer, I should be able read date and time as first class types from sources like datasets or database.
As a pipeline developer, I should be able write date and time as first class types to sinks like datasets or database.
As a plugin developer, I should be able to create structured records with date and time field types.
As a CDAP user, I should be able to explore CDAP datasets that have date and time fields.
As a CDAP application developer, I should be able to programmatically create dataset schema with date and time fields.

Design

In Avro 1.8, date/time is represented as LogicalType. A logical type is a primitive type with extra attribute `logicalType`. Below table represent avro types and schema supported in 5.1. Please note that other logical avro types: Duration, Decimal are not in scope of this feature.

Avro Type	Avro Schema	Description
Date	{ "type": "int", "logicalType": "date" }	where the int stores the number of days from the unix epoch
Timestamp in milli secs	{ "type": "long", "logicalType": "timestamp-millis" }	where the long stores the number of milliseconds from the unix epoch
Timestamp in micro secs	{ "type": "long", "logicalType": "timestamp-micros" }	where the long stores the number of microseconds from the unix epoch
Time in milli secs	{ "type": "int", "logicalType": "time-millis" }	where the int stores the number of milliseconds after midnight
Time in micro secs	{ "type": "long", "logicalType": "time-micros" }	where the long stores the number of microseconds after midnight

To support Date and Time in CDAP, we will have to map avro logical types to corresponding jdbc and hive types. Below table represents mapping of logical avro type to corresponding hive and relational database types.

Avro Type	Hive Type	JDBC Type	MySQL	Oracle	MS SQL	BigQuery	Spanner	Redshift
Date	Date	java.sql.Date	DATE	N/A	DATE	DATE	DATE	DATE
Timestamp in milli secs	Timestamp	java.sql.Timestamp	TIMESTAMP	DATE/TIMESTAMP	DATETIME	N/A	N/A	N/A
Timestamp in micro secs	Timestamp	java.sql.Timestamp	TIMESTAMP	DATE/TIMESTAMP	DATETIME2	TIMESTAMP	TIMESTAMP	TIMESTAMP/TIMESTAMPZ
Time in milli secs	-- (not explorable)	java.sql.Time	N/A	N/A	TIME	N/A	N/A	N/A
Time in micro secs	-- (not explorable)	java.sql.Timestamp	N/A	N/A	TIME	TIME	N/A	N/A

Please note that logical type `Time` does not correspond to any of the hive types so datasets with avro type `Time` will not be explorable. For ObjectMappedTable, CDAP supports conversion of Java Pojo to Schema objects. CDAP should support conversion of Date and Timestamp in java class to corresponding CDAP Schema types.

NOTE:

Since streams are deprecated, we will not be supporting Date/Time types for streams

Usage in plugins:

CDAP logical types support two different resolutions, millis and micros, for timestamp and time logical types. This means plugins should be able to support both the time resolutions in input and output schemas. However, while generating the plugin schemas for databases like mysql, bigquery, plugins would consider more granular time unit (micros) wherever possible. This is because many of the datetime/timestamp types in relational and cloud databases are more granular than milliseconds.

New Programmatic APIs

Changes in cdap dataset schema to support logical avro types:

Schema.java

public final class Schema implements Serializable {
private final Type type;
@Nullable
private final LogicalType logicalType;


public enum Type {
  NULL(true),
  BOOLEAN(true),
...
}


public enum LogicalType {
  TIME_MILLIS, //primitive type INT
  TIME_MICROS, // primitive type LONG
  TIMESTAMP_MILLIS, // primitive type LONG
  TIMESTAMP_MICROS, // primitive type LONG
  DATE // primitive type INT
}

/**
 * Creates a {@link Schema} for the given logical type. 
 *
 * @param type LogicalType of the schema to create.
 * @return A {@link Schema} with the given type.
 */
public static Schema of(LogicalType type) {
  if (type.equals(LogicalType.DATE)) {
    return new Schema(Type.INT, type, null, null, null, null, null, null, null);
  } else if (type.equals(LogicalType.TIMESTAMP_MILLIS)) {
    return new Schema(Type.LONG, type, null, null, null, null, null, null, null);
  }....
}

private Schema(Type type,@Nullable LogicalType logicalType,
               @Nullable Set<String> enumValues,                                    // Not null for enum type
               @Nullable Schema componentSchema,                                    // Not null for array type
               @Nullable Schema keySchema, @Nullable Schema valueSchema,            // Not null for map type
               @Nullable String recordName, @Nullable Map<String, Field> fieldMap,  // Not null for record type
               @Nullable List<Schema> unionSchemas) {                               // Not null for union type
  this.type = type;
  this.logicalType = logicalType;
  ...
}


/**
* @return The {@link LogicalType} that this schema represents.
*/
public LogicalType getLogicalType() {
 return logicalType;
}
}

Api changes in StructuredRecord for getting and setting the date/time fields. Please note that setters will accept both Java7 and Java 8 apis where as getters will only return java 8 apis. If user wants to get fields in java 7 apis, he/she can use StructuredRecord#get() which will return underlying avro field long/int and type cast the object as needed.

StructureRecord.java

/**
 * Get the value of date field in the record.
 * @param fieldName field to get.
 * @return Java 8 {@link LocalDate}, to get Java 7 {@link Date}, please use get method
 */
public LocalDate getDate(String fieldName) {

}

/**
 * Get the value of time field in the record.
 * @param fieldName field to get.
 * @return Java 8 {@link LocalTime}, to get Java 7 {@link Date}, please use get method
 */
public LocalTime getTime(String fieldName) {

}

/**
 * Get the value of date field in the record. UTC by default.
 * @param fieldName field to get.
 * @return Java 8 {@link Instant}, to get Java 7 {@link Date}, please use get method
 */
public ZonedDateTime getTimestamp(String fieldName) {
  
}


/**
 * Get the value of date field in the record.
 * @param fieldName field to get.
 * @return Java 8 {@link Instant}, to get Java 7 {@link Date}, please use get method
 */
public ZonedDateTime getTimestamp(String fieldName, ZoneId zoneId) {
  
}
/**
 * Set the date field to the given value
 * @param fieldName Name of the field to set
 * @param value Value for the field
 * @return This builder
 * @throws UnexpectedFormatException if the field is not in the schema, or the field is not nullable but a null
 * value is given
 */
public Builder setDate(String fieldName, @Nullable LocalDate value) {

}

/**
 * Set the time field to the given value
 * @param fieldName Name of the field to set
 * @param value Value for the field
 * @return This builder
 * @throws UnexpectedFormatException if the field is not in the schema, or the field is not nullable but a null
 * value is given
 */
public Builder setTime(String fieldName, @Nullable LocalTime value) {

}

/**
 * Set the timestamp field to the given value
 * @param fieldName Name of the field to set
 * @param value Value for the field
 * @return This builder
 * @throws UnexpectedFormatException if the field is not in the schema, or the field is not nullable but a null
 * value is given
 */
public Builder setTimestamp(String fieldName, @Nullable ZonedDateTime value) {

}

Example of adding an avro logical type field in CDAP Schema:

SchemaExample.java

public class SchemaExample {
  Schema schema = Schema.recordOf(
    "logicalTypeRecord",
    Schema.Field.of("id", Schema.of(Schema.Type.STRING)),
    Schema.Field.of("name", Schema.of(Schema.Type.STRING)),
    Schema.Field.of("date", Schema.of(Schema.LogicalType.DATE)),
    Schema.Field.of("timestamp", Schema.of(Schema.LogicalType.TIMESTAMP_MILLIS)));
}}

Example usage in StructuredRecord:

StructureRecordExample.java

StructuredRecord.Builder builder = StructuredRecord.builder(schema);
builder.setDate(dateField1, LocalDate.now());

Schema for the plugins in pipeline spec will be same as avro logical type schema, below are example of how each avro logical type schema will be represented in pipeline spec:

Date:
{
  "properties": {
    "schema": {
      "type": "record",
      "name": "etlSchemaBody",
      "fields": [
        {
          "name": "offset",
          "type": "long"
        },
        {
          "name": "body",
          "type": "string"
        },
        {
          "name": "Date",
          "type": {
            "type": "int",
            "logicalType": "date"
          }
        },
        {
          "name": "map",
          "type": {
            "type": "map",
            "keys": "long",
            "values": "float"
          }
        }
      ]
    }
  }
..........
}

Timestamp micros:
{
  "properties": {
    "schema": {
      "type": "record",
      "name": "etlSchemaBody",
      "fields": [
        {
          "name": "offset",
          "type": "long"
        },
        {
          "name": "body",
          "type": "string"
        },
        {
          "name": "Timestamp-Micro",
          "type": {
            "type": "long",
            "logicalType": "timestamp-micros"
          }
        },
        {
          "name": "map",
          "type": {
            "type": "map",
            "keys": "long",
            "values": "float"
          }
        }
      ]
    }
  }
.......
}


Timestamp millis:
{
  "properties": {
    "schema": {
      "type": "record",
      "name": "etlSchemaBody",
      "fields": [
        {
          "name": "offset",
          "type": "long"
        },
        {
          "name": "body",
          "type": "string"
        },
        {
          "name": "Timestamp-Millis",
          "type": {
            "type": "long",
            "logicalType": "timestamp-millis"
          }
        },
        {
          "name": "map",
          "type": {
            "type": "map",
            "keys": "long",
            "values": "float"
          }
        }
      ]
    }
  }
.......
}
Time in micros:
{
  "properties": {
    "schema": {
      "type": "record",
      "name": "etlSchemaBody",
      "fields": [
        {
          "name": "offset",
          "type": "long"
        },
        {
          "name": "body",
          "type": "string"
        },
        {
          "name": "Time",
          "type": {
            "type": "long",
            "logicalType": "time-micros"
          }
        },
        {
          "name": "map",
          "type": {
            "type": "map",
            "keys": "long",
            "values": "float"
          }
        }
      ]
    }
  }
.......
}


Time in millis:
{
  "properties": {
    "schema": {
      "type": "record",
      "name": "etlSchemaBody",
      "fields": [
        {
          "name": "offset",
          "type": "long"
        },
        {
          "name": "body",
          "type": "string"
        },
        {
          "name": "Time",
          "type": {
            "type": "int",
            "logicalType": "time-millis"
          }
        },
        {
          "name": "map",
          "type": {
            "type": "map",
            "keys": "long",
            "values": "float"
          }
        }
      ]
    }
  }
.......
}

Limitation

We would not support table creation with date/time types from database plugins. This is because each database has different date and time types and it is hard to know at database plugin level which specific type is needed for the table creation. Also while running the pipeline for data transformation/data migration, it is not a common usecase to create table on each pipeline run.

Date/Time support in Data Prep

Below is the list of existing directives supported for date/time conversion:

Directive	Input	Output	Supported
parse-as-date	String	Timestamp	yes
parse-as-simple-date	String	Timestamp	yes
diff-date	Timestamp	long(millis)	yes
format-date	Timestamp	String	yes

Data prep does not have any directive to convert data with long to Timestamp. This conversion may be needed if data is stored as csv or json. To parse this data, we will add a new directive `parse-timestamp-to-date`

parse-timestamp <column_name> <time-unit>

New Directive	Supported input column type	Supported Output column type	Description
parse-timestamp	long (timestamp in millis)	Timestamp	Convert long representing unix timestamp in millis to Timestamp

List of directives not supported for columns with date/time types:

Find and replace
- find-and-replace
Extract
- extract-regex-groups
- split-to-columns
- cut-character
Explode:
- split-to-rows
- flatten
Change type
- set-type
Filter
Custom Transform

UI Impact or Changes

For new Date and Time types, the pipeline UI will display `Date`, `Time` and `Timestamp`. Whenever a user adds a `Timestamp` field in the UI, it will be internally represented as `timestamp-micros`. Ideally, the UI would display 'Timestamp' whenever it gets a schema with `timestamp-micros` or `timestamp-millis`. This turns out to be a lot of work, so for 5.1.0, the UI will display `timestamp-micros` as `Timestamp`, and will ignore `timestamp-millis`. This means anything with `timestamp-millis` will be treated as a long and the logical type will be lost. Users will only run into this if they write their own plugin that uses `timestamp-millis`, or they import some pre-existing avro schema with `timestamp-millis`. If the user tries to import pipeline/schema with `timestamp-millis`, the UI will ignore it and it will just become a long.

Below is the mapping of backend types to UI types.

Backend schema type	UI type
date	Date
time-micros	Time
timestamp-micros	Timestamp
time-millis	int
timestamp-millis	long

Column types in data prep UI will be same as types in pipeline UI.

Support for Datetime in Dataprep and in Schema

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Test Scenarios

Please note that the tests should be conducted for mysql, ms sql, oracle, bigquery and redshift databases.

Test ID	Test Description	Expected Results
1.	Using cdap pipeline, read records with Date type from a mysql table using DatabaseSource plugin	The correct date should be read from the mysql table
2.	Using cdap pipeline, write records with Date type to a mysql table using DatabaseSink plugin	The correct date should be written to the mysql table
3.	Using cdap pipeline, read records with Timestamp type from a mysql table using DatabaseSource plugin	The correct timestamp should be read from the mysql table
4.	Using cdap pipeline, write records with Timestamp type to a mysql table using DatabaseSink plugin	The correct timestamp should be written to the mysql table
5.	Using cdap pipeline, read records with Timestamp in microseconds from a mysql table using DatabaseSource plugin	The correct timestamp along with micro seconds should be read from the mysql table
6.	Using cdap pipeline, write records with Timestamp in microseconds to a mysql table using DatabaseSink plugin	The correct timestamp along with micro seconds should be written to the mysql table
7.	Using cdap pipeline, read records with Time type from a mysql table using DatabaseSource plugin	The correct time should be read from the mysql table
8.	Using cdap pipeline, write records with Time type to a mysql table using DatabaseSink plugin	The correct time should be written to the mysql table
9.	Using cdap pipeline, write timestamp to cdap Table dataset	The correct timestamp should be written to cdap table dataset
10.	Explore cdap table dataset which has timestamp in its schema	Correct timestamp values should be visible
11.	Using cdap pipeline, write date to cdap Table dataset	The correct date should be written to cdap table dataset
12.	Explore cdap table dataset which has date in its schema	Correct date values should be visible
13.	Using cdap pipeline, write timestamp to cdap avro partitioned fileset	The correct timestamp should be written to cdap fileset
14.	Explore cdap fileset dataset which has timestamp in its schema	Correct timestamp values should be visible
15.	Using cdap pipeline, write date to cdap avro partitioned fileset	The correct date should be written to cdap fileset
16.	Explore cdap fileset dataset which has date in its schema	Correct date values should be visible
17.	Using cdap pipeline, write timestamp to cdap parquet partitioned fileset	The correct timestamp should be written to cdap fileset
18.	Explore cdap parquet fileset dataset which has timestamp in its schema	Correct timestamp values should be visible
19.	Using cdap pipeline, write date to cdap parquet partitioned fileset	The correct timestamp should be written to cdap fileset
20.	Explore cdap parquet fileset dataset which has date in its schema	Correct date values should be visible

Releases

Targeted release CDAP 5.1

Future work

Support other logical Avro types like Decimal and Duration.