TIMESTAMP data type

Internally, the resolution of the time portion of a TIMESTAMP value is in nanoseconds.

Syntax:

In the column definition of a CREATE TABLE statement:

column_name TIMESTAMP

timestamp [+ | -] INTERVAL interval
DATE_ADD (timestamp, INTERVAL interval time_unit)

Range: 1400-01-01 to 9999-12-31

Out of range TIMESTAMP values are converted to NULL.

The range of Impala TIMESTAMP is different from the Hive TIMESTAMP type.

INTERVAL expressions:

You can perform date arithmetic by adding or subtracting a specified number of time units, using the INTERVAL keyword and the + operator, the - operator, date_add() or date_sub().

You can only specify one time unit in each interval expression, for example INTERVAL 3 DAYS or INTERVAL 25 HOURS, but you can produce any granularity by adding together successive INTERVAL values, such as timestamp_value + INTERVAL 3 WEEKS - INTERVAL 1 DAY + INTERVAL 10 MICROSECONDS.

Internal details: Represented in memory as a 16-byte value.

Time zones:

By default, Impala stores and interprets TIMESTAMP values in UTC time zone when writing to data files, reading from data files, or converting to and from system time values through functions.

The --use_local_tz_for_unix_timestamp_conversions setting can be used to fix discrepancy in INTERVAL operations. For example, a TIMESTAMP + INTERVAL n-hours can be affected by Daylight Saving Time, which Impala does not consider by default as these operations are applied as if the timestamp was in UTC. You can use the --use_local_tz_for_unix_timestamp_conversions setting to fix the issue.

See Customizing time zones for configuring to use custom time zone database and aliases.

See Impala date and time functions for the list of functions affected by the --use_local_tz_for_unix_timestamp_conversions setting.

Time zone handling between Impala and Hive:

Interoperability between Hive and Impala is different depending on the file format.

• Text

  For text tables, TIMESTAMP values can be written and read interchangeably by Impala and Hive as Hive reads and writes TIMESTAMP values without converting with respect to time zones.

• Parquet

  When Hive writes to Parquet data files, the TIMESTAMP values are normalized to UTC from the local time zone of the host where the data was written. On the other hand, Impala does not make any time zone adjustment when it writes or reads INT96 TIMESTAMP values to Parquet files. This difference in time zone handling can cause potentially inconsistent results when Impala processes TIMESTAMP values in the Parquet files written by Hive.

  To avoid incompatibility problems or having to code workarounds, you can specify one or both of these impalad startup flags:

  • --use_local_tz_for_unix_timestamp_conversions=true
  • --convert_legacy_hive_parquet_utc_timestamps=true

  When the ‑‑convert_legacy_hive_parquet_utc_timestamps setting is enabled, Impala recognizes the Parquet data files written by Hive, and applies the same UTC-to-local-timezone conversion logic during the query as Hive does.

  In Impala 3.0 and lower, the ‑‑convert_legacy_hive_parquet_utc_timestamps setting had a severe impact on multi-threaded performance. The new time zone implementation in Impala 3.1 eliminated most of the performance overhead and made Impala scale well to multiple threads. The ‑‑convert_legacy_hive_parquet_utc_timestamps setting is turned off by default for a performance reason. To avoid unexpected incompatibility problems, you should turn on the option when processing TIMESTAMP columns in Parquet files written by Hive.

  Hive currently cannot write INT64 TIMESTAMP values.

  In Impala 3.2 and higher, INT64 TIMESTAMP values annotated with the TIMESTAMP_MILLIS or TIMESTAMP_MICROS OriginalType are assumed to be always UTC normalized, so the UTC to local conversion will be always done. INT64 TIMESTAMP annotated with the TIMESTAMP LogicalType specifies whether UTC to local conversion is necessary depending on the Parquet metadata.

Conversions:

Impala automatically converts STRING literals of the correct format into TIMESTAMP values. Timestamp values are accepted in the format 'yyyy‑MM‑dd HH:mm:ss.SSSSSS', and can consist of just the date, or just the time, with or without the fractional second portion. For example, you can specify TIMESTAMP values such as '1966‑07‑30', '08:30:00', or '1985‑09‑25 17:45:30.005'.

Leading zeroes are not required in the numbers representing the date component, such as month and date, or the time component, such as hour, minute, and second. For example, Impala accepts both '2018‑1‑1 01:02:03' and '2018-01-01 1:2:3' as valid.

In STRING to TIMESTAMP conversions, leading and trailing white spaces, such as a space, a tab, a newline, or a carriage return, are ignored. For example, Impala treats the following as equivalent: '1999‑12‑01 01:02:03 ', ' 1999‑12‑01 01:02:03', '1999‑12‑01 01:02:03\r\n\t'.

Casting an integer or floating-point value N to TIMESTAMP produces a value that is N seconds past the start of the epoch date (January 1, 1970). By default, the result value represents a date and time in the UTC time zone. If the setting ‑‑use_local_tz_for_unix_timestamp_conversions=true is in effect, the resulting TIMESTAMP represents a date and time in the local time zone.

In Impala 1.3 and higher, the FROM_UNIXTIME() and UNIX_TIMESTAMP() functions allow a wider range of format strings, with more flexibility in element order, repetition of letter placeholders, and separator characters. In Impala 2.3 and higher, the UNIX_TIMESTAMP() function also allows a numeric timezone offset to be specified as part of the input string. See Impala date and time functions for details.

In Impala 2.2.0 and higher, built-in functions that accept or return integers representing TIMESTAMP values use the BIGINT type for parameters and return values, rather than INT. This change lets the date and time functions avoid an overflow error that would otherwise occur on January 19th, 2038 (known as the Year 2038 problem or Y2K38 problem). This change affects the FROM_UNIXTIME() and UNIX_TIMESTAMP() functions. You might need to change application code that interacts with these functions, change the types of columns that store the return values, or add CAST() calls to SQL statements that call these functions.

Partitioning:

Although you cannot use a TIMESTAMP column as a partition key, you can extract the individual years, months, days, hours, and so on and partition based on those columns. Because the partition key column values are represented in HDFS directory names, rather than as fields in the data files themselves, you can also keep the original TIMESTAMP values if desired, without duplicating data or wasting storage space. See Partition Key Columns for more details on partitioning with date and time values.

[localhost:21000] > create table timeline (event string) partitioned by (happened timestamp);
ERROR: AnalysisException: Type 'TIMESTAMP' is not supported as partition-column type in column: happened

NULL considerations: Casting any unrecognized STRING value to this type produces a NULL value.

HBase considerations: This data type is fully compatible with HBase tables.

Parquet consideration: INT96 and INT64 encoded Parquet timestamps are supported in Impala.

Parquet considerations: This type is fully compatible with Parquet tables.

Text table considerations: Values of this type are potentially larger in text tables than in tables using Parquet or other binary formats.

Column statistics considerations: Because this type has a fixed size, the maximum and average size fields are always filled in for column statistics, even before you run the COMPUTE STATS statement.

Kudu considerations:

Restrictions:

If you cast a STRING with an unrecognized format to a TIMESTAMP, the result is NULL rather than an error. Make sure to test your data pipeline to be sure any textual date and time values are in a format that Impala TIMESTAMP can recognize.

Currently, Avro tables cannot contain TIMESTAMP columns. If you need to store date and time values in Avro tables, as a workaround you can use a STRING representation of the values, convert the values to BIGINT with the UNIX_TIMESTAMP() function, or create separate numeric columns for individual date and time fields using the EXTRACT() function.

Examples:

The following examples demonstrate using TIMESTAMP values with built-in functions:

select cast('1966-07-30' as timestamp);
select cast('1985-09-25 17:45:30.005' as timestamp);
select cast('08:30:00' as timestamp);
select hour('1970-01-01 15:30:00');         -- Succeeds, returns 15.
select hour('1970-01-01 15:30');            -- Returns NULL because seconds field required.
select hour('1970-01-01 27:30:00');         -- Returns NULL because hour value out of range.
select dayofweek('2004-06-13');             -- Returns 1, representing Sunday.
select dayname('2004-06-13');               -- Returns 'Sunday'.
select date_add('2004-06-13', 365);         -- Returns 2005-06-13 with zeros for hh:mm:ss fields.
select day('2004-06-13');                   -- Returns 13.
select datediff('1989-12-31','1984-09-01'); -- How many days between these 2 dates?
select now();                               -- Returns current date and time in local timezone.

The following examples demonstrate using TIMESTAMP values with HDFS-backed tables:

create table dates_and_times (t timestamp);
insert into dates_and_times values
  ('1966-07-30'), ('1985-09-25 17:45:30.005'), ('08:30:00'), (now());

The following examples demonstrate using TIMESTAMP values with Kudu tables:

create table timestamp_t (x int primary key, s string, t timestamp, b bigint)
  partition by hash (x) partitions 16
  stored as kudu;

-- The default value of now() has microsecond precision, so the final 3 digits
-- representing nanoseconds are all zero.
insert into timestamp_t values (1, cast(now() as string), now(), unix_timestamp(now()));

-- Values with 1-499 nanoseconds are rounded down in the Kudu TIMESTAMP column.
insert into timestamp_t values (2, cast(now() + interval 100 nanoseconds as string), now() + interval 100 nanoseconds, unix_timestamp(now() + interval 100 nanoseconds));
insert into timestamp_t values (3, cast(now() + interval 499 nanoseconds as string), now() + interval 499 nanoseconds, unix_timestamp(now() + interval 499 nanoseconds));

-- Values with 500-999 nanoseconds are rounded up in the Kudu TIMESTAMP column.
insert into timestamp_t values (4, cast(now() + interval 500 nanoseconds as string), now() + interval 500 nanoseconds, unix_timestamp(now() + interval 500 nanoseconds));
insert into timestamp_t values (5, cast(now() + interval 501 nanoseconds as string), now() + interval 501 nanoseconds, unix_timestamp(now() + interval 501 nanoseconds));

-- The string representation shows how underlying Impala TIMESTAMP can have nanosecond precision.
-- The TIMESTAMP column shows how timestamps in a Kudu table are rounded to microsecond precision.
-- The BIGINT column represents seconds past the epoch and so if not affected much by nanoseconds.
select s, t, b from timestamp_t order by t;
+-------------------------------+-------------------------------+------------+
| s                             | t                             | b          |
+-------------------------------+-------------------------------+------------+
| 2017-05-31 15:30:05.107157000 | 2017-05-31 15:30:05.107157000 | 1496244605 |
| 2017-05-31 15:30:28.868151100 | 2017-05-31 15:30:28.868151000 | 1496244628 |
| 2017-05-31 15:34:33.674692499 | 2017-05-31 15:34:33.674692000 | 1496244873 |
| 2017-05-31 15:35:04.769166500 | 2017-05-31 15:35:04.769167000 | 1496244904 |
| 2017-05-31 15:35:33.033082501 | 2017-05-31 15:35:33.033083000 | 1496244933 |
+-------------------------------+-------------------------------+------------+

Added in: Available in all versions of Impala.

Related information:

• To convert to or from different date formats, or perform date arithmetic, use the date and time functions described in Impala date and time functions. In particular, the from_unixtime() function requires a case-sensitive format string such as "yyyy-MM-dd HH:mm:ss.SSSS", matching one of the allowed variations of a TIMESTAMP value (date plus time, only date, only time, optional fractional seconds).
• See SQL differences between Impala and Hive for details about differences in TIMESTAMP handling between Impala and Hive.