Troubleshoot issues with the multi-stage query engine (v2)

Troubleshoot issues with the multi-stage query engine (v2).

Learn how to troubleshoot errors when using the multi-stage query engine (v2), and see multi-stage query engine limitations.

Find instructions on how to enable the multi-stage query engine, or see a high-level overview of how the multi-stage query engine works.

Limitations of the multi-stage query engine

We are continuously improving the multi-stage query engine. A few limitations to call out:

Support for multi-value columns is limited

Support for multi-value columns is limited to projections, and predicates must use the arrayToMv function. For example, to successfully run the following query:

-- example 1: used in GROUP-BY
SELECT count(*), RandomAirports FROM airlineStats 
GROUP BY RandomAirports

-- example 2: used in PREDICATE
SELECT * FROM airlineStats WHERE RandomAirports IN ('SFO', 'JFK')

-- example 3: used in ORDER-BY
SELECT count(*), RandomAirports FROM airlineStats 
GROUP BY RandomAirports
ORDER BY RandomAirports DESC

You must include arrayToMv in the query as follows:

-- example 1: used in GROUP-BY
SELECT count(*), arrayToMv(RandomAirports) FROM airlineStats 
GROUP BY arrayToMv(RandomAirports)

-- example 2: used in PREDICATE
SELECT * FROM airlineStats WHERE arrayToMv(RandomAirports) IN ('SFO', 'JFK')

-- example 3: used in ORDER-BY
SELECT count(*), arrayToMV(RandomAirports) FROM airlineStats 
GROUP BY arrayToMV(RandomAirports)
ORDER BY arrayToMV(RandomAirports) DESC

Schema and other prefixes are not supported

Schema and other prefixes are not supported in queries. For example, the following queries are not supported:

SELECT* from default.myTable;
SELECT * from schemaName.myTable;

Queries without prefixes are supported:

SELECT * from myTable;

Modifying query behavior based on the cluster config is not supported

Modifying query behavior based on the cluster configuration is not supported. distinctcounthll, distinctcounthllmv, distinctcountrawhll, and distinctcountrawhllmv will always use the default value for log2m in the multi-stage engine unless the value is explicitly defined in the query itself. Therefore, the following query may produce different results in single-stage and multi-stage engine depending on your cluster configuration (default.hyperloglog.log2m):

select distinctcounthll(col) from myTable

To ensure same results across both query engines, specify the log2m param value explicitly in your query:

select distinctcounthll(col, 8) from myTable

Ambiguous reference to a projected column in statement clauses

If a column is repeated more than once in SELECT statement, that column requires disambiguate aliasing. For example, in the following query, the reference to colA is ambiguous whether it's to the first or second projected colA:

SELECT colA, colA, COUNT(*)
FROM myTable GROUP BY colA ORDER BY colA

The solution is to rewrite the query either use aliasing:

SELECT colA AS tmpA, colA as tmpB, COUNT(*) 
FROM myTable GROUP BY tmpA, tmpB ORDER BY tmpA

Or use index-based referencing:

SELECT colA, colA, COUNT(*) 
FROM myTable GROUP BY 1, 2 ORDER BY 1

Tightened restriction on function signature and type matching

Pinot single-stage query engine automatically do implicit type casts in many of the situations, for example when running the following:

timestampCol >= longCol

it will automatically convert both values to long datatypes before comparison. This behavior however could cause issues and thus it is not so widely applied in the multi-stage engine where a stricter datatype conformance is enforced. the example above should be explicitly written as:

CAST(timestampCol AS BIGINT) >= longCol

Default names for projections with function calls

Default names for projections with function calls are different between single and multi-stage.

  • For example, in multi-stage, the following query:

  SELECT count(*) from mytable

Returns the following result:

    "columnNames": [
        "EXPR$0"
      ],

  • In single-stage, the following function:

  SELECT count(*) from mytable

Returns the following result:

      "columnNames": [
        "count(*)"
      ],

Table names and column names are case sensitive

In multi-stage, table and column names and are case sensitive. In single-stage they were not. For example, the following two queries are not equivalent in multi-stage engine:

select * from myTable

select * from mytable

Note: Function names are not case sensitive in neither single nor multi-stage.

Arbitrary number of arguments isn't supported

An arbitrary number of arguments is no longer supported in multi-stage. For example, in single-stage, the following query worked:

select add(1,2,3,4,5) from table

In multi-stage, this query must be rewritten as follows:

select add(1, add(2,add(3, add(4,5)))) from table

Note: Remember that select 1 + 2 + 3 + 4 + 5 from table is still valid in multi-stage

Return type for binary arithmetic operators (+, -, *, /)

In the single-stage engine, these operators would always result in a DOUBLE value being returned, no matter the operand types. In the multi-stage engine, however, the result type depends on the input operand types - for instance, adding two LONG values will result in a LONG and so on.

Return type for aggregations like SUM, MIN, MAX

In the single-stage engine, these aggregations would always result in a DOUBLE value being returned, no matter the operand types. In the multi-stage engine, however, the result type depends on the data type of the column being aggregated.

NULL function support

Null handling is not supported when tables use table based null storing. You have to use column based null storing instead. See null handling support.

Custom transform function support

In multi-stage:

  • The histogram function is not supported.

  • The timeConvert function is not supported, see dateTimeConvert for more details.

  • The dateTimeConvertWindowHop function is not supported.

  • Array & Map-related functions are not supported.

Custom aggregate function support

  • Aggregate functions that requires literal input (such as percentile, firstWithTime) might result in a non-compilable query plan.

Different type names

The multi-stage engine uses different type names than the single-stage engine. Although the classical names must still be used in schemas and some SQL expressions, the new names must be used in CAST expressions.

The following table shows the differences in type names:

Single-stage engine
Multi-stage engine

NULL

NULL

BOOLEAN

BOOLEAN

INT

INT

LONG

BIGINT

BIG_DECIMAL

DECIMAL

FLOAT

FLOAT/REAL

DOUBLE

DOUBLE

INTERVAL

INTERVAL

TIMESTAMP

TIMESTAMP

STRING

VARCHAR

BYTES

VARBINARY

-

ARRAY

JSON

-

Varbinary literals

VARBINARY literals in multi-stage engine must be prefixed with X or x. For example, the following query:

SELECT col1, col2 FROM myTable where bytesCol = X'4a220e6096b25eadb88358cb44068a3248254675'

In single-stage engine the same query would be:

-- not supported in multi-stage
SELECT col1, col2 FROM myTable where bytesCol = '4a220e6096b25eadb88358cb44068a3248254675'

Troubleshoot errors

Troubleshoot semantic/runtime errors and timeout errors.

Semantic/runtime errors

  • Try downloading the latest docker image or building from the latest master commit.

    • We continuously push bug fixes for the multi-stage engine so bugs you encountered might have already been fixed in the latest master build.

  • Try rewriting your query.

    • Some functions previously supported in the single-stage query engine (v1) may have a new way to express in the multi-stage engine (v2). Check and see if you are using any non-standard SQL functions or semantics.

Timeout errors

  • Try reducing the size of the table(s) used.

    • Add higher selectivity filters to the tables.

  • Try executing part of the subquery or a simplified version of the query first.

    • This helps to determine the selectivity and scale of the query being executed.

  • Try adding more servers.

    • The new multi-stage engine runs distributed across the entire cluster, so adding more servers to partitioned queries such as GROUP BY aggregates, and equality JOINs help speed up the query runtime.

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