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 v2 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:
You must include arrayToMv
in the query as follows:
Schema and other prefixes are not supported
Schema and other prefixes are not supported in queries. For example, the following queries are not supported:
Queries without prefixes are supported:
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` use
a different default value of log2mParam
in the multi-stage v2 engine. In v2, this value can no longer be configured. Therefore, the following query may produce different results in v1 and v2 engine:
To ensure v2 returns the same result, specify the log2mParam
value in your query:
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
:
The solution is to rewrite the query either use aliasing:
Or use index-based referencing:
Tightened restriction on function naming
Pinot single-stage query engine automatically removes the underscore _ character from function names. So co_u_n_t()
is equivalent to count().
In v2, function naming restrictions were tightened, so the underscore(_)
character is only allowed to separate word boundaries in a function name. Also camel case is supported in function names. For example, the following function names are allowed:
Default names for projections with function calls
Default names for projections with function calls are different between v1 and v2.
For example, in v1, the following query:
Returns the following result:
In v2, the following function:
Returns the following result:
Table names and column names are case sensitive
In v2, table and column names and are case sensitive. In v1 they were not. For example, the following two queries are not equivalent in v2:
select * from myTable
select * from mytable
Note: Function names are not case sensitive in v2 or v1.
Arbitrary number of arguments isn't supported
An arbitrary number of arguments is no longer supported in v2. For example, in v1, the following query worked:
In v2, this query must be rewritten as follows:
NULL function support
IS NULL
andIS NOT NULL
functions do not work correctly in v2Using the
COUNT
function on aNULL
column does not work correctly in v2
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.