Migrating from Hive

Trino uses ANSI SQL syntax and semantics, whereas Hive uses a language similar to SQL called HiveQL which is loosely modeled after MySQL (which itself has many differences from ANSI SQL).

Use subscript for accessing a dynamic index of an array instead of an udf

The subscript operator in SQL supports full expressions, unlike Hive (which only supports constants). Therefore you can write queries like:

SELECT my_array[CARDINALITY(my_array)] as last_element
FROM ...

Avoid out of bounds access of arrays

Accessing out of bounds elements of an array will result in an exception. You can avoid this with an if as follows:

SELECT IF(CARDINALITY(my_array) >= 3, my_array[3], NULL)
FROM ...

Use ANSI SQL syntax for arrays

Arrays are indexed starting from 1, not from 0:

SELECT my_array[1] AS first_element
FROM ...

Construct arrays with ANSI syntax:

SELECT ARRAY[1, 2, 3] AS my_array

Use ANSI SQL syntax for identifiers and strings

Strings are delimited with single quotes and identifiers are quoted with double quotes, not backquotes:

SELECT name AS "User Name"
FROM "7day_active"
WHERE name = 'foo'

Quote identifiers that start with numbers

Identifiers that start with numbers are not legal in ANSI SQL and must be quoted using double quotes:

SELECT *
FROM "7day_active"

Use the standard string concatenation operator

Use the ANSI SQL string concatenation operator:

SELECT a || b || c
FROM ...

Use standard types for CAST targets

The following standard types are supported for CAST targets:

SELECT
  CAST(x AS varchar)
, CAST(x AS bigint)
, CAST(x AS double)
, CAST(x AS boolean)
FROM ...

In particular, use VARCHAR instead of STRING.

Use CAST when dividing integers

Trino follows the standard behavior of performing integer division when dividing two integers. For example, dividing 7 by 2 will result in 3, not 3.5. To perform floating point division on two integers, cast one of them to a double:

SELECT CAST(5 AS DOUBLE) / 2

Use WITH for complex expressions or queries

When you want to re-use a complex output expression as a filter, use either an inline subquery or factor it out using the WITH clause:

WITH a AS (
  SELECT substr(name, 1, 3) x
  FROM ...
)
SELECT *
FROM a
WHERE x = 'foo'

Use UNNEST to expand arrays and maps

Trino supports {ref}unnest for expanding arrays and maps. Use UNNEST instead of LATERAL VIEW explode().

Hive query:

SELECT student, score
FROM tests
LATERAL VIEW explode(scores) t AS score;

Trino query:

SELECT student, score
FROM tests
CROSS JOIN UNNEST(scores) AS t (score);

Use ANSI SQL syntax for date and time INTERVAL expressions

Trino supports the ANSI SQL style INTERVAL expressions that differs from the implementation used in Hive.

• The INTERVAL keyword is required and is not optional.
• Date and time units must be singular. For example day and not days.
• Values must be quoted.

Hive query:

SELECT cast('2000-08-19' as date) + 14 days;

Equivalent Trino query:

SELECT cast('2000-08-19' as date) + INTERVAL '14' day;

Caution with datediff

The Hive datediff function returns the difference between the two dates in days and is declared as:

datediff(string enddate, string startdate)  -> integer

The equivalent Trino function {ref}date_diff<datetime-interval-functions> uses a reverse order for the two date parameters and requires a unit. This has to be taken into account when migrating:

Hive query:

datediff(enddate, startdate)

Trino query:

date_diff('day', startdate, enddate)

Overwriting data on insert

By default, INSERT queries are not allowed to overwrite existing data. You can use the catalog session property insert_existing_partitions_behavior to allow overwrites. Prepend the name of the catalog using the Hive connector, for example hdfs, and set the property in the session before you run the insert query:

SET SESSION hdfs.insert_existing_partitions_behavior = 'OVERWRITE';
INSERT INTO hdfs.schema.table ...

The resulting behavior is equivalent to using INSERT OVERWRITE in Hive.

Insert overwrite operation is not supported by Trino when the table is stored on encrypted HDFS, when the table is unpartitioned or table is transactional.