The multi-stage query engine uses a set of operators to process the query. These operators are based on relational algebra, with some modifications to better fit the distributed nature of the engine.

These operators are the execution units that Pinot uses to execute a query. The operators are executed in a pipeline with tree structure, where each operator consumes the output of the previous operators (also known as upstreams).

Users do not directly specify these operators. Instead they write SQL queries that are translated into a logical plan, which is then transformed into different operators. The logical plan can be obtained using explaining a query, while there is no way to get the operators directly. The closest thing to the operators that users can get is the multi-stage stats output.

Operators vs SQL clauses

These operators are generated from the SQL query that you write, but even they are similar, there is not a one-to-one mapping between the SQL clauses and the operators. Some SQL clauses generate multiple operators, while some operators are generated by multiple SQL clauses.

Operators vs explain plan nodes

Operators and explain plan nodes are closer than SQL clauses and operators. Although most explain plan nodes can be directly mapped to an operator, there are some exceptions:

• Each PinotLogicalExchange and each PinotLogicalSortExchange explain node is materialized into a pair of and operators.

• All plan nodes that belong to the same leaf stage are executed in the operator.

In general terms, the operators are the execution units that Pinot uses to execute a query and are also known as the multi-stage physical plan, while the explain plan nodes are logical plans. The difference between the two is that the operators can be actually executed, while the explain plan nodes are the logical representation of the query plan.

List of operators

The following is a list of operators that are used by the multi-stage query engine:

The filter operator is used to filter rows based on a condition.

This page describes the filter operator defined in the relational algebra used by multi-stage queries. This operator is generated by the multi-stage query engine when you use the where, having or sometimes on clauses.

Implementation details

Filter operations apply a predicate to each row and only keep the rows that satisfy the predicate.

It is important to notice that filter operators can only be optimized using indexes when they are executed in the leaf stage. The reason for that is that the intermediate stages don't have access to the actual segments. This is why the engine will try to push down the filter operation to the leaf stage whenever possible.

As explained in , the explain plan in the multi-stage query engine does not indicate whether indexes are used or not.

Blocking nature

The filter operator is a streaming operator. It emits the blocks of rows as soon as they are received from the upstream operator.

Hints

None

Stats

executionTimeMs

Type: Long

The summation of time spent by all threads executing the operator. This means that the wall time spent in the operation may be smaller that this value if the parallelism is larger than 1. This number is affected by the number of received rows and the complexity of the predicate.

emittedRows

Type: Long

The number of groups emitted by the operator. A large number of emitted rows may not be problematic, but indicates that the predicate is not very selective.

Explain attributes

The filter operator is represented in the explain plan as a LogicalFilter explain node.

condition

Type: Expression

The condition that is being applied to the rows. The expression may use indexed columns ($0, $1, etc), functions and literals. The indexed columns are always 0-based.

For example, the following explain plan:

Is saying that the filter is applying the condition $5 > 2 which means that only the rows where the 6th column is greater than 2 will be emitted. In order to know which column is the 6th, you need to look at the schema of the table scanned.

Tips and tricks

How to know if indexes are used

As explained in , the explain plan in the multi-stage query engine does not directly indicate whether indexes are used or not.

Apache Pinot contributors are working on improving this, but it is not yet available. Meanwhile, we need an indirect approach to get that information.

First, we need to know on which stage the filter is being used. If the filter is being used in an intermediate stage, then the filter is not using indexes. In order to know the stage, you can extract stages as explained in .

But what about the leaf filters executed in the stage? Not all filters in the leaf stage can use indexes. The only way to know if the filter is using indexes is to use single-stage explain plan. In order to do so you need to transform the leaf stage into a single-stage query. This is a manual process that can be tedious but ends up not being so difficult once you get used to it.

See for more information.

The aggregate operator is used to perform calculations on a set of rows and return a single row of results.

This page describes the aggregate operator defined in the relational algebra used by multi-stage queries. This operator is generated by the multi-stage query engine when you use aggregate functions in a query either with or without a group by clause.

Implementation details

Aggregate operations may be expensive in terms of memory, CPU and network usage. As explained in , the multi-stage query engine breaks down the query into multiple stages and each stage is then executed in parallel on different workers. Each worker processes a subset of the data and sends the results to the coordinator which then aggregates the results. When possible, the multi-stage query engine will try to apply a divide-and-conquer strategy to reduce the amount of data that needs to be processed in the coordinator stage.

For example if the aggregation function is a sum, the engine will try to sum the results of each worker before sending the partial result to the coordinator, which would then sum the partial results in order to get the final result. But some aggregation functions, like count(distinct), cannot be computed in this way and require all the data to be processed in the coordinator stage.

In Apache Pinot 1.1.0, the multi-stage query engine always keeps the data in memory. This means that the amount of memory used by the engine is proportional to the number of groups generated by the group by clause and the amount of data that needs to be kept for each group (which depends on the aggregation function).

Even when the aggregation function is a simple count, which only requires to keep a long for each group in memory, the amount of memory used can be high if the number of groups is high. This is why the engine limits the number of groups. By default, this limit is 100.000, but this can be changed by providing hints.

Blocking nature

The aggregate operator is a blocking operator. It needs to consume all the input data before emitting the result.

Hints

num_groups_limit

Type: Integer

Default: 100.000

Defines the max number of groups that can be created by the group by clause. If the number of groups exceeds this limit, the query will not fail but will stop the execution.

Example:

is_partitioned_by_group_by_keys

Type: Boolean

Default: false

If set to true, the engine will consider that the data is already partitioned by the group by keys. This means that the engine will not need to shuffle the data to group them by the group by keys and the coordinator stage will be able to compute the final result without needing to merge the partial results.

Example:

is_skip_leaf_stage_group_by

Type: Boolean

Default: false

If set to true, the engine will not push down the aggregate into the leaf stage. In some situations, it could be wasted effort to do group-by on leaf, eg: when cardinality of group by column is very high.

Example:

max_initial_result_holder_capacity

Type: Integer

Default: 10.000

Defines the initial capacity of the result holder that stores the intermediate results of the aggregation. This hint can be used to reduce the memory usage of the engine by setting a value close to the expected number of groups. It is usually recommended to not change this hint unless you know that the expected number of groups is much lower than the default value.

Example:

Stats

executionTimeMs

Type: Long

The summation of time spent by all threads executing the operator. This means that the wall time spent in the operation may be smaller that this value if the parallelism is larger than 1. Remember that this value is affected by the number of received rows and the complexity of the aggregation function.

emittedRows

Type: Long

The number of groups emitted by the operator. A large number of emitted rows can indicate that the query is not well optimized.

Remember that the number of groups is limited by the num_groups_limit hint and a large number of groups can lead to high memory usage and slow queries.

numGroupsLimitReached

Type: Boolean

This stat is set to true when the number of groups exceeds the limit defined by the num_groups_limit hint. In that case, the query will not fail but will return partial results, which will be indicated by the global partialResponse stat.

Explain attributes

The aggregate operator is represented in the explain plan as a LogicalAggregate explain node.

Remember that these nodes appear in pairs: First in one stage where the aggregation is done in parallel and then in the upstream stage where the partial results are merged.

group

Type: List of Integer

The list of columns used in the group by clause. These numbers are 0-based column indexes on the virtual row projected by the upstream.

For example the explain plan:

Is saying that the group by clause is using the column with index 6 in the virtual row projected by the upstream. Given in this case that row is generated by a table scan, the column with index 6 is the 7th column in the table as defined in its schema.

agg#N

Type: Expression

The aggregation functions applied to the columns. There may be multiple agg#N attributes, each one representing a different aggregation function.

For example the explain plan:

Has two aggregation functions: COUNT() and MAX(). The second is applied to the column with index 5 in the virtual row projected by the upstream. Given in this case that row is generated by a table scan, the column with index 5 is the 6th column in the table as defined in its schema.

Tips and tricks

Try to not use aggregation functions that cannot be parallelized

For example, it is recommended to use one of the different hyperloglog flavor instead of count(distinct) when the cardinality of the data or their size.

For example, it is cheaper to execute count(distinct) on an int column with 1000 distinct values than on a column that stores very long strings, even if the number of distinct values is the same.

The window operator is used to define a window over which to perform calculations.

This page describes the window operator defined in the relational algebra used by multi-stage queries. This operator is generated by the multi-stage query engine when you window functions in a query. You can read more about window functions in the reference documentation.

Unlike the , which will output one row per group, the window operator will output as many rows as input rows.

Implementation details

The transform operator is used to apply a transformation to the input data. They may filter out columns or add new ones by applying functions to the existing columns. This operator is generated by the multi-stage query engine when you use a SELECT clause in a query, but can also be used to implement other transformations.

Implementation details

Transform operators apply some transformation functions to the input data received from upstream. The cost of the transformation usually depends on the complexity of the functions applied, but comparing to other operators, it is usually not very high.

The literal operator is used to define a constant value in the query. This operator may be generated by the multi-stage query engine when you use a constant value in a query.

Blocking nature

The literal operator is a blocking operator, but given its trivial nature it should not matter.

The mailbox receive operator is the operator that receives the data from the mailbox send operator. This is not an actual relational operator but a Pinot extension used to receive data from other stages.

Implementation details

Stages in the multi-stage query engine are executed in parallel by different workers. Workers send data to each other using mailboxes. The number of mailboxes depends on the send operator parallelism, the receive operator parallelism and the distribution being used. At worse, there is one mailbox per worker pair, so if the upstream send operator has a parallelism of S and the receive operator has a parallelism of R, there will be S * R mailboxes.

By default, these mailboxes are GRPC channels, but when both workers are in the same server, they can use shared memory and therefore a more efficient on heap mailbox is used.

The mailbox receive operator pulls data from these mailboxes and sends it to the downstream operator.

Blocking nature

The mailbox receive operator is a streaming operator. It emits the blocks of rows as soon as they are received from the upstream operator.

It is important to notice that the mailbox receive operator tries to be fair when reading from multiple workers.

Hints

None

Stats

executionTimeMs

Type: Long

The summation of time spent by all threads executing the operator. This means that the wall time spent in the operation may be smaller that this value if the parallelism is larger than 1.

emittedRows

Type: Long

The number of groups emitted by the operator. This operator should always emit as many rows as its upstream operator.

fanIn

Type: Long

How many workers are sending data to this operator.

inMemoryMessages

Type: Long

How many messages have been received in heap format by this mailbox. Receiving in heap messages is more efficient than receiving them in raw format, as the messages do not need to be serialized and deserialized and no network transfer is needed.

rawMessages

Type: Long

How many messages have been received in raw format and therefore serialized by this mailbox. Receiving in heap messages is more efficient than receiving them in raw format, as the messages do not need to be serialized and deserialized and no network transfer is needed.

deserializedBytes

Type: Long

How many bytes have been deserialized by this mailbox. A high number here indicates that the mailbox is receiving a lot of data from other servers, which is expensive in terms of CPU, memory and network.

deserializeTimeMs

Type: Long

How long it took to deserialize the raw messages sent to this mailbox. This time is not wall time, but the sum of the time spent by all threads deserializing messages.

downstreamWaitMs

Type: Long

How much time this operator has been blocked waiting while offering data to be consumed by the downstream operator. A high number here indicates that the downstream operator is slow and may be a bottleneck. For example, usually the receive operator that is the left input of a join operator has a high value here, as the join needs to consume all the messages from the right input before it can start consuming the left input.

upstreamWaitMs

Type: Long

How much time this operator has been blocked waiting for more data to be sent by the upstream (send) operator. A high number here indicates that the upstream operator is slow and may be a bottleneck. For example, blocking operators like aggregations, sorts, joins or window functions require all the data to be received before they can start emitting a result, so having them as upstream operators of a mailbox receive operator can lead to high values here.

Explain attributes

Given that the mailbox receive operator is a meta-operator, it is not actually shown in the explain plan. Instead, a single PinotLogicalExchange or PinotLogicalSortExchange is shown in the explain plan. This exchange explain node is the logical representation of a pair of send and receive operators.

See the to understand the attributes of the exchange explain node.

Tips and tricks

None

The minus operator is used to subtract the result of one query from another query. This operator is used to find the difference between two sets of rows, usually by using the SQL EXCEPT operator.

Implementation details

The minus operator is a semi-blocking operator that first consumes the right input relation in a blocking fashion and then consumes the left input relation in a streaming fashion.

The current implementation consumes the whole right input relation first and stores the rows in a set. Then it consumes the left input relation one block at a time. Each time a block of rows is read from the left input relation, the operator checks if the rows are in the set of rows from the right input relation. All unique rows that are not in the set are added to a new partial result block. Once the whole left input block is analyzed, the operator emits the partial result block.

This process is repeated until all rows from the left input relation are processed.

Blocking nature

The minus operator is a semi-blocking operator that first consumes the right input relation in a blocking fashion and then consumes the left input relation in a streaming fashion.

In pseudo-code, the algorithm looks like this:

Hints

None

Stats

executionTimeMs

Type: Long

The summation of time spent by all threads executing the operator. This means that the wall time spent in the operation may be smaller that this value if the parallelism is larger than 1.

emittedRows

Type: Long

The number of groups emitted by the operator.

Explain attributes

The minus operator is represented in the explain plan as a LogicalMinus explain node.

all

Type: Boolean

This attribute is used to indicate if the operator should return all the rows or only the distinct rows.

Tips and tricks

Memory pressure

The minus operator ends up having to store all unique rows from both input relations in memory. This can lead to memory pressure if the input relations are large and have a high number of unique rows.

The order of input relations matter

Although the minus operator ends up adding all unique rows from both input relations to a set, the order of input relations matters. While the right input relation is consumed in a blocking fashion, the left input relation is consumed in a streaming fashion. Therefore the latency of the whole query could be improved if the left input relation is producing values in streaming fashion.

The union operator combines the results of two or more queries into a single result set. The result set contains all the rows from the queries. Contrary to other set operations ( and ), the union operator does not remove duplicates from the result set. Therefore its semantic is similar to the SQL UNION or UNION ALL operator.

There is no guarantee on the order of the rows in the result set.

The mailbox send operator is the operator that sends data to the mailbox receive operator. This is not an actual relational operator but a Pinot extension used to send data to other stages.

These operators are always the root of the intermediate and leaf stages.

Implementation details

Stages in the multi-stage query engine are executed in parallel by different workers. Workers send data to each other using mailboxes. The number of mailboxes depends on the send operator parallelism, the receive operator parallelism and the distribution being used. At worse, there is one mailbox per worker pair, so if the upstream send operator has a parallelism of S and the receive operator has a parallelism of R, there will be S * R mailboxes.

By default, these mailboxes are GRPC channels, but when both workers are in the same server, they can use shared memory and therefore a more efficient on heap mailbox is used.

The mailbox send operator wraps these mailboxes, offering single logical mailbox to the stage. How to distribute data to different workers of the downstream stage is determined by the distribution of the operator. The supported distributions are hash, random and broadcast.

• hash means there are multiple instances of the stream, and each instance contains records whose keys hash to a particular hash value. Instances are disjoint; a given record appears on exactly one stream. The list of numbers in the bracket indicates the columns used to calculate the hash. These numbers are 0-based column indexes on the virtual row projected by the upstream.

• random means there are multiple instances of the stream, and each instance contains randomly chosen records. Instances are disjoint; a given record appears on exactly one stream.

Blocking nature

The mailbox send operator is a streaming operator. It emits the blocks of rows as soon as they are received from the upstream operator.

Hints

None

Stats

executionTimeMs

Type: Long

The summation of time spent by all threads executing the operator. This means that the wall time spent in the operation may be smaller that this value if the parallelism is larger than 1.

emittedRows

Type: Long

The number of groups emitted by the operator. This operator should always emit as many rows as its upstream operator.

stage

Type: number

The stage id of the operator. The root stage has id 0 and this number is incremented by 1 for each stage. Current implementation iterates over the stages in pre-order traversal, although this is not guaranteed.

This stat is useful to understand extract stages from queries, as explained in .

parallelism

Type: Int

Number of threads executing the stage. Although this stat is only reported in the send mailbox operator, it is the same for all operators in the stage.

fanOut

Type: Int

The number of workers this operation is sending data to. A large fan out may indicate that the operation is sending data to many workers, which may be a bottleneck that may be improved using partitioning.

inMemoryMessages

Type: Int

How many messages have been sent in heap format by this mailbox. Sending in heap messages is more efficient than sending in raw format, as the messages do not need to be serialized and deserialized and no network transfer is needed.

rawMessages

Type: Int

How many messages have been sent in raw format and therefore serialized by this mailbox. Sending in heap messages is more efficient than sending in raw format, as the messages do not need to be serialized and deserialized and no network transfer is needed.

serializedBytes

Type: Long

How many bytes have been serialized by this mailbox. A high number here indicates that the mailbox is sending a lot of data to other servers, which is expensive in terms of CPU, memory and network.

serializationTimeMs

Type: Long

How long it took to serialize the raw messages sent by this mailbox. This time is not wall time, but the sum of the time spent by all threads serializing messages.

Explain attributes

Given that the mailbox send operator is a meta-operator, it is not actually shown in the explain plan. Instead, a single PinotLogicalExchange or PinotLogicalSortExchange is shown in the explain plan. This exchange explain node is the logical representation of a pair of send and receive operators.

distribution

Type: Expression

Example: distribution=[hash[0]], distribution=[random] or distribution=[broadcast]

The distribution used by the mailbox receive operator. Values supported by Pinot are hash, random and broadcast, as explained in the .

While broadcast and random distributions don't have any parameters, the hash distribution includes a list of numbers in brackets. That list represents the columns used to calculate the hash and are the 0-based column indexes on the virtual row projected by the upstream operator.

For example, in following explain plan:

Indicates that the data is distributed by the first and second columns of the projected row, which are groupUUID and userUUID respectively.

Tips and tricks

None

The hash join operator is used to join two relations using a hash join algorithm. It is a binary operator that takes two inputs, the left and right relations, and produces a single output relation.

This is the only join operator in the multi-stage query engine and it is always created as a result of a query that contains a join clause, but can be created by other SQL queries like ones using semi-join.

There are different types of joins that can be performed using the hash join operator. Apache Pinot supports:

• Inner join, where only the rows that have a match in both relations are returned.

The leaf operator is the operator that actually reads the data from the segments. Instead of being just a simple table scan, the leaf operator is a meta-operator that wraps the single-stage query engine and executes all the operators in the leaf stage of the query plan.

Implementation details

The leaf operator is not a relational operator itself but a meta-operator that is able to execute single-stage queries. When servers execute a leaf stage, they compile all operations in the stage but the send operator into the equivalent single-stage query and execute that using a slightly modified version of the single-stage engine.

As a result, leaf stage operators can use all the optimizations and indices that the single-stage engine can use but it also means that there may be slight differences when an operator is executed in a leaf stage compared to when it is executed in an intermediate stage. For example, operations pushed down to the leaf stage may use indexes (see ) or the semantics can be slightly different.

You can read for more information on the differences between the leaf and intermediate stages, but the main ones are:

• Null handling is different.

• Some functions are only supported in multi-stage and some others only in single-stage.

• Type coercion is different. While the single-stage engine always operates with generic types (ie uses doubles when mathematical operations are used), the multi-stage engine tries to keep the types (ie adding two integers will result in an integer).

Blocking nature

One of the slight differences between the leaf and the normal single-stage engine is that the leaf engine tries to be not blocking.

Hints

None

Stats

executionTimeMs

Type: Long

The summation of time spent by all threads executing the operator. This means that the wall time spent in the operation may be smaller that this value if the parallelism is larger than 1.

emittedRows

Type: Long

The number of groups emitted by the operator.

table

Type: String

The name of the table that is scanned. This is the name without the type suffix (so without _REALTIME or _OFFLINE). This is very useful to understand which table is being scanned by this leaf stage in case of complex queries.

numDocsScanned

Type: Long

Similar to the same stat in single-stage queries, this stat indicates the number of rows selected after the filter phase.

If it is very high, that means the selectivity for the query is low and lots of rows need to be processed after the filtering. Consider refining the filter to increase the selectivity of the query.

numEntriesScannedInFilter

Type: Long

Similar to the same stat in single-stage queries, this stat indicates the number of entries (aka scalar values) scanned in the filtering phase of query execution.

Can be larger than the total scanned doc count because of multiple filtering predicates or multi-value entries. Can also be smaller than the total scanned doc count if indexing is used for filtering.

This along with numEntriesScannedPostFilter indicates where most of the time is spent during table scan processing. If this value is high, enabling indexing for affected columns is a way to bring it down. Another option is to partition the data based on the dimension most heavily used in your filter queries.

numEntriesScannedPostFilter

Type: Long

Similar to the same stat in single-stage queries, this stat indicates the number of entries (aka scalar values) scanned after the filtering phase of query execution, ie. aggregation and/or group-by phases. This is equivalent to numDocScanned * number of projected columns.

This along with numEntriesScannedInFilter indicates where most of the time is spent during table scan processing. A high number for this means the selectivity is low (that is, Pinot needs to scan a lot of records to answer the query). If this is high, consider using star-tree index, given a regular index won't improve performance.

numSegmentsQueried

Type: Integer

Similar to the same stat in single-stage queries, this stat indicates the total number of segment queried for a query. May be less than the total number of segments if the broker applies optimizations.

The broker decides how many segments to query on each server, based on broker pruning logic. The server decides how many of these segments to actually look at, based on server pruning logic. After processing segments for a query, fewer may have the matching records.

In general, numSegmentsQueried >= numSegmentsProcessed >= numSegmentsMatched.

numSegmentsProcessed

Type: Integer

Similar to the same stat in single-stage queries, this stat indicates the number of segments processed with at least one document matched in the query response.

The more segments are processed, the more IO has to be done. This is why selective queries where numSegmentsProcessed is close to numSegmentsQueried can be optimized by changing the data distribution.

numSegmentsMatched

Type: Integer

Similar to the same stat in single-stage queries, this stat indicates the number of segment operators used to process segments. Indicates the effectiveness of the pruning logic.

totalDocs

Type: Long

Similar to the same stat in single-stage queries, this stat indicates the number of rows in the table.

numGroupsLimitReached

Type: Boolean

Similar to the same stat in single-stage queries and the same in operators, this stat indicates if the max group limit has been reached in a group by aggregation operator executed in the leaf stage.

If this boolean is set to true, the query result may not be accurate. The default value for numGroupsLimit is 100k, and should be sufficient for most use cases.

numResizes

Type: Integer

Number of result resizes for queries

resizeTimeMs

Type: Long

Time spent in resizing results for the output. Either because of LIMIT or maximum allowed group by keys or any other criteria.

threadCpuTimeNs

Type: Long

Aggregated thread cpu time in nanoseconds for query processing from servers. This metric is only available if Pinot is configured with pinot.server.instance.enableThreadCpuTimeMeasurement.

systemActivitiesCpuTimeNs

Type: Long

Aggregated system activities cpu time in nanoseconds for query processing (e.g. GC, OS paging etc.) This metric is only available if Pinot is configured with pinot.server.instance.enableThreadCpuTimeMeasurement.

numSegmentsPrunedByServer

Type: Integer

The number of segments pruned by the server, for any reason.

numSegmentsPrunedInvalid

Type: Integer

The number of segments pruned because they are invalid. Segments are invalid when the schema has changed and the segment has not been refreshed.

For example, if a column is added to the schema, the segment will be invalid for queries that use that column until it is refreshed.

numSegmentsPrunedByLimit

Type: Integer

The number of segments pruned because they are not needed for the query due to the limit clause.

Pinot keeps a count of the number of rows returned by each segment. Once it's guaranteed that no more segments need to be read to satisfy the limit clause without breaking semantics, the remaining segments are pruned.

For example, a query like SELECT col1 FROM table2 LIMIT 10 can be pruned for this reason while a query like SELECT col1 FROM table2 ORDER BY col1 DESC LIMIT 10 cannot because Pinot needs to read all segments to guarantee the larger values of col1 are returned.

numSegmentsPrunedByValue

Type: Integer

The number of segments pruned because they are not needed for the query due to a value clause, usually a where.

Pinot keeps the maximum and minimum values of each segment for each column. If the value clause is such that the segment cannot contain any rows that satisfy the clause, the segment is pruned.

numConsumingSegmentsProcessed

Type: Integer

Like numSegmentsProcessed but only for consuming segments.

numConsumingSegmentsMatched

Type: Integer

Like numSegmentsMatched but only for consuming segments.

operatorExecutionTimeMs

Type: Long

The time spent by the operator executing.

operatorExecStartTimeMs

Type: Long

The instant in time when the operator started executing.

Explain attributes

Given that the leaf operator is a meta-operator, it is not actually shown in the explain plan. But the leaf stage is the only operator that can execute table scans, so here we list the attributes that can be found in the explain plan for a table scan

table

Type: String array

Example: table=[[default, userGroups]]

The qualified name of the table that is scanned, which means it also contains the name of the database being used.

Tips and tricks

Try to push as much as possible to the leaf stage

Leaf stage operators can use all the optimizations and indices that the single-stage engine can use. This means that it is usually better to push down as much as possible to the leaf stage.

The engine is smart enough to push down filters and aggregations without breaking semantics, but sometimes there are subtle SQL semantics and what the domain expert writing the query wants to do.

Sometimes things the engine is too paranoid about null handling or the query includes an unnecessary limit clause that prevents the engine from pushing down the filter.

It is recommended to analyze your explain plan to be sure that the engine is able to push down as much logic as you expect.

The intersect operator is a relational operator that combines two relations and returns the common rows between them. The operator is used to find the intersection of two or more relations, usually by using the SQL INTERSECT operator.

The sort or limit operator is used to sort the input data, limit the number of rows emitted by the operator or both. This operator is generated by the multi-stage query engine when you use an order by, limit or offset operation in a query.

Implementation details

Blocking nature

Hints

None

Stats

executionTimeMs

Type: Long

The summation of time spent by all threads executing the operator. This means that the wall time spent in the operation may be smaller that this value if the parallelism is larger than 1.

emittedRows

Type: Long

The number of groups emitted by the operator.

Explain attributes

The sort or limit operator is represented in the explain plan as a LogicalSort explain node.

sort#

Type: Expression

The sort expressions used by the operator. There is one of these attributes per sort expression. The first one is sort0, the second one is sort1, and so on.

The value of this attribute is the expression used to sort the data and may contain indexed columns ($0, $1, etc) that represent the columns of the virtual row generated by the upstream.

For example, the following plan:

Is saying that the rows are sorted first by the column with index 0 and then by the column with index 2 in the virtual row generated by the upstream. That column is generated by a projection whose first column (index 0) is userUUID, the second (index 1) is deviceOS and third (index 2) is the result of the SUBSTRING($4, 0, 2) expression. As we know $4 in this project is deviceOS, we can infer that the third column is the first two characters of the deviceOS column.

dir#

Type: ASC or DESC

The direction of the sort. There is one of these attributes per sort expression.

fetch

Type: Long

The number of rows to emit. This is the equivalent to LIMIT in SQL. Remember that the limit can be applied without sorting, in which case the order on which the rows are emitted is undefined.

offset

Type: Long

The number of rows to skip before emitting the rows. This is the equivalent to OFFSET in SQL.

Tips and tricks

Limit and offset can prevent filter pushdown

In SQL, usually limit and offset are used in the last stage of the query. But when being used in the middle of the query (like in a subquery or a CTE), it can prevent filter pushdown optimization.

For example, imagine the following query:

This query may generate the plan:

We can see that the filter deviceOS = 'windows' is pushed down to the leaf stage. This reduce the amount of data that needs to be scanned and can improve the query performance, specially if there is an inverted index in the deviceOS column.

But if we modify the query to add a limit to the userAttributes table scan:

The generated plan will be:

Here we can see that the filter deviceOS = 'windows' is not pushed down leaf stage, which means that the engine will need to scan all the data in the userAttributes table and then apply the filter.

The reason why the filter is not pushed down is that the limit operation must be applied before the filter in order to not break the semantics, which in this case are saying that we want 10 rows of the userAttributes table without considering their deviceOS value.

In cases where you actually want to apply the filter before the limit, you can specify the where clause in the subquery. For example:

Which will produce the following plan:

As you can see, the filter is pushed down to leaf stage, which will reduce the amount of data

Do not abuse offset pagination

Although OFFSET and LIMIT are a very simple way to paginate results, they can be very inefficient. It is almost always better to paginate using a WHERE clause that uses a range of values instead of using OFFSET.

The reason is that in order to apply an OFFSET the engine must generate these rows and then discard them. Instead, if you use a WHERE clause with a range of values, the engine can apply different techniques like indexes or pruning to avoid reading the rows that are not needed.

This is not a Pinot specific issue, but a general one. See for example or .