In Single-stage engine Explain Plan, we do not differentiate any logical/physical plan b/c the structure of the query is fixed. By default it explain the Physical Plan

In multi-stage engine we support EXPLAIN PLAN syntax mostly following Apache Calcite's EXPLAIN PLAN syntax. Here are several examples:

Explain Logical Plan

Using SSB standard query example:

EXPLAIN PLAN FOR 
select 
  P_BRAND1, sum(LO_REVENUE) 
from ssb_lineorder_1, ssb_part_1
where LO_PARTKEY = P_PARTKEY 
  and P_CATEGORY = 'MFGR#12' 
group by P_BRAND1

The result field contains 2 columns and 1 row:

+-----------------------------------|-------------------------------------------------------------|
| SQL#$%0                           |PLAN#$%1                                                     |
+-----------------------------------|-------------------------------------------------------------|
|"EXPLAIN PLAN FOR                  |"Execution Plan                                              | 
|select                             |LogicalAggregate(group=[{0}], agg#0=[$SUM0($1)])             | 
|  P_BRAND1, sum(LO_REVENUE)        |  PinotLogicalExchange(distribution=[hash[0]])               | 
|from ssb_lineorder_1, ssb_part_1   |    LogicalAggregate(group=[{2}], agg#0=[$SUM0($1)])         | 
|where LO_PARTKEY = P_PARTKEY       |      LogicalJoin(condition=[=($0, $3)], joinType=[inner])   | 
|  and P_CATEGORY = 'MFGR#12'       |        PinotLogicalExchange(distribution=[hash[0]])         | 
|group by P_BRAND1                  |          LogicalProject(LO_PARTKEY=[$12], LO_REVENUE=[$14]) | 
|   and P_CATEGORY = 'MFGR#12'      |            LogicalTableScan(table=[[ssb_lineorder_1]])      | 
|"                                  |        PinotLogicalExchange(distribution=[hash[1]])         | 
|                                   |          LogicalProject(P_BRAND1=[$3], P_PARTKEY=[$9])      | 
|                                   |            LogicalFilter(condition=[=($4, 'MFGR#12')])      | 
|                                   |              LogicalTableScan(table=[[ssb_part_1]])         |
|                                   |"                                                            |
+-----------------------------------|-------------------------------------------------------------|

noted that all the normal options for EXPLAIN PLAN in Apache Calcite also works in Pinot with extra information including attributes, type, etc.

Explain Implementation (Pyhsical) Plan

If we want to gather the implementation plan specific to Pinot internal multi-stage engine operator chain. You can use the EXPLAIN IMPLEMENTATION PLAN :

+-----------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------|
| SQL#$%0                           |PLAN#$%1                                                                                                                                                         |  
+-----------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------|
|"EXPLAIN IMPLEMENTATION PLAN FOR   |[0]@local:8843 MAIL_RECEIVE(BROADCAST_DISTRIBUTED)                                                                                                               | 
|select                             |├── [1]@local:8432 MAIL_SEND(BROADCAST_DISTRIBUTED)->{[0]@local@{8843,8843}|[0]} (Subtree Omitted)                                                               | 
|  P_BRAND1, sum(LO_REVENUE)        |├── [1]@local:8432 MAIL_SEND(BROADCAST_DISTRIBUTED)->{[0]@local@{8843,8843}|[0]} (Subtree Omitted)                                                               | 
|from ssb_lineorder_1, ssb_part_1   |└── [1]@local:8432 MAIL_SEND(BROADCAST_DISTRIBUTED)->{[0]@local@{8843,8843}|[0]}                                                                                 | 
|where LO_PARTKEY = P_PARTKEY       |    └── [1]@local:8432 AGGREGATE_FINAL                                                                                                                           | 
|  and P_CATEGORY = 'MFGR#12'       |        └── [1]@local:8432 MAIL_RECEIVE(HASH_DISTRIBUTED)                                                                                                        | 
|group by P_BRAND1                  |            ├── [2]@local:8432 MAIL_SEND(HASH_DISTRIBUTED)->{[1]@local@{8432,8843}|[1],[1]@local@{8432,8843}|[2],[1]@local@{8432,8843}|[0]} (Subtree Omitted)    | 
|   and P_CATEGORY = 'MFGR#12'      |            ├── [2]@local:8432 MAIL_SEND(HASH_DISTRIBUTED)->{[1]@local@{8432,8843}|[1],[1]@local@{8432,8843}|[2],[1]@local@{8432,8843}|[0]} (Subtree Omitted)    | 
|"                                  |            └── [2]@local:8432 MAIL_SEND(HASH_DISTRIBUTED)->{[1]@local@{8432,8843}|[1],[1]@local@{8432,8843}|[2],[1]@local@{8432,8843}|[0]}                      | 
|                                   |                └── [2]@local:8432 AGGREGATE_LEAF                                                                                                                | 
|                                   |                    └── [2]@local:8432 JOIN                                                                                                                      | 
|                                   |                        ├── [2]@local:8432 MAIL_RECEIVE(HASH_DISTRIBUTED)                                                                                        | 
|                                   |                        │   ├── [3]@local:8432 MAIL_SEND(HASH_DISTRIBUTED)->{[2]@local@{8432,8843}|[1],[2]@local@{8432,8843}|[2],[2]@local@{8432,8843}|[0]}      | 
|                                   |                        │   │   └── [3]@local:8432 PROJECT                                                                                                       | 
|                                   |                        │   │       └── [3]@local:8432 TABLE SCAN (ssb_lineorder_1) null                                                                         | 
|                                   |                        │   ├── [3]@local:8432 MAIL_SEND(HASH_DISTRIBUTED)->{[2]@local@{8432,8843}|[1],[2]@local@{8432,8843}|[2],[2]@local@{8432,8843}|[0]}      | 
|                                   |                        │   │   └── [3]@local:8432 PROJECT                                                                                                       | 
|                                   |                        │   │       └── [3]@local:8432 TABLE SCAN (ssb_lineorder_1) null                                                                         | 
|                                   |                        │   └── [3]@local:8432 MAIL_SEND(HASH_DISTRIBUTED)->{[2]@local@{8432,8843}|[1],[2]@local@{8432,8843}|[2],[2]@local@{8432,8843}|[0]}      | 
|                                   |                        │       └── [3]@local:8432 PROJECT                                                                                                       | 
|                                   |                        │           └── [3]@local:8432 TABLE SCAN (ssb_lineorder_1) null                                                                         | 
|                                   |                        └── [2]@local:8432 MAIL_RECEIVE(HASH_DISTRIBUTED)                                                                                        | 
|                                   |                            ├── [4]@local:8432 MAIL_SEND(HASH_DISTRIBUTED)->{[2]@local@{8432,8843}|[1],[2]@local@{8432,8843}|[2],[2]@local@{8432,8843}|[0]}      | 
|                                   |                            │   └── [4]@local:8432 PROJECT                                                                                                       | 
|                                   |                            │       └── [4]@local:8432 FILTER                                                                                                    | 
|                                   |                            │           └── [4]@local:8432 TABLE SCAN (ssb_part_1) null                                                                          | 
|                                   |                            ├── [4]@local:8432 MAIL_SEND(HASH_DISTRIBUTED)->{[2]@local@{8432,8843}|[1],[2]@local@{8432,8843}|[2],[2]@local@{8432,8843}|[0]}      | 
|                                   |                            │   └── [4]@local:8432 PROJECT                                                                                                       | 
|                                   |                            │       └── [4]@local:8432 FILTER                                                                                                    | 
|                                   |                            │           └── [4]@local:8432 TABLE SCAN (ssb_part_1) null                                                                          | 
|                                   |                            └── [4]@local:8432 MAIL_SEND(HASH_DISTRIBUTED)->{[2]@local@{8432,8843}|[1],[2]@local@{8432,8843}|[2],[2]@local@{8432,8843}|[0]}      | 
|                                   |                                └── [4]@local:8432 PROJECT                                                                                                       | 
|                                   |                                    └── [4]@local:8432 FILTER                                                                                                    | 
|                                   |                                        └── [4]@local:8432 TABLE SCAN (ssb_part_1) null                                                                          | 
+-----------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------|

Notes that now there is information regarding how many servers were used, and how are data being shuffled between nodes. etc.

Query execution within Pinot is modeled as a sequence of operators that are executed in a pipelined manner to produce the final result. The output of the EXPLAIN PLAN statement can be used to see how queries are being run or to further optimize queries.

Introduction

EXPLAN PLAN can be run in two modes: verbose and non-verbose (default) via the use of a query option. To enable verbose mode the query option explainPlanVerbose=true must be passed.

EXPLAIN PLAN FOR SELECT playerID, playerName FROM baseballStats

+---------------------------------------------|------------|---------|
| Operator                                    | Operator_Id|Parent_Id|
+---------------------------------------------|------------|---------|
|BROKER_REDUCE(limit:10)                      | 1          | 0       |
|COMBINE_SELECT                               | 2          | 1       |
|PLAN_START(numSegmentsForThisPlan:1)         | -1         | -1      |
|SELECT(selectList:playerID, playerName)      | 3          | 2       |
|TRANSFORM_PASSTHROUGH(playerID, playerName)  | 4          | 3       |
|PROJECT(playerName, playerID)                | 5          | 4       |
|DOC_ID_SET                                   | 6          | 5       |
|FILTER_MATCH_ENTIRE_SEGMENT(docs:97889)      | 7          | 6       |
+---------------------------------------------|------------|---------|

In the non-verbose EXPLAIN PLAN output above, the Operator column describes the operator that Pinot will run where as, the Operator_Id and Parent_Id columns show the parent-child relationship between operators.

This parent-child relationship shows the order in which operators execute. For example, FILTER_MATCH_ENTIRE_SEGMENT will execute before and pass its output to PROJECT. Similarly, PROJECT will execute before and pass its output to TRANSFORM_PASSTHROUGH operator and so on.

Although the EXPLAIN PLAN query produces tabular output, in this document, we show a tree representation of the EXPLAIN PLAN output so that parent-child relationship between operators are easy to see and user can visualize the bottom-up flow of data in the operator tree execution.

BROKER_REDUCE(limit:10)
└── COMBINE_SELECT
    └── PLAN_START(numSegmentsForThisPlan:1)
        └── SELECT(selectList:playerID, playerName)
            └── TRANSFORM_PASSTHROUGH(playerID, playerName)
                └── PROJECT(playerName, playerID)
                    └── DOC_ID_SET
                        └── FILTER_MATCH_ENTIRE_SEGMENT(docs:97889)

Note a special node with the Operator_Id and Parent_Id called PLAN_START(numSegmentsForThisPlan:1). This node indicates the number of segments which match a given plan. The EXPLAIN PLAN query can be run with the verbose mode enabled using the query option explainPlanVerbose=true which will show the varying deduplicated query plans across all segments across all servers.

EXPLAIN PLAN output should only be used for informational purposes because it is likely to change from version to version as Pinot is further developed and enhanced. Pinot uses a "Scatter Gather" approach to query evaluation (see Pinot Architecture for more details). At the Broker, an incoming query is split into several server-level queries for each backend server to evaluate. At each Server, the query is further split into segment-level queries that are evaluated against each segment on the server. The results of segment queries are combined and sent to the Broker. The Broker in turn combines the results from all the Servers and sends the final results back to the user. Note that if the EXPLAIN PLAN query runs without the verbose mode enabled, a single plan will be returned (the heuristic used is to return the deepest plan tree) and this may not be an accurate representation of all plans across all segments. Different segments may execute the plan in a slightly different way.

Reading the EXPLAIN PLAN output from bottom to top will show how data flows from a table to query results. In the example shown above, the FILTER_MATCH_ENTIRE_SEGMENT operator shows that all 977889 records of the segment matched the query. The DOC_ID_SET over the filter operator gets the set of document IDs matching the filter operator. The PROJECT operator over the DOC_ID_SET operator pulls only those columns that were referenced in the query. The TRANSFORM_PASSTHROUGH operator just passes the column data from PROJECT operator to the SELECT operator. At SELECT, the query has been successfully evaluated against one segment. Results from different data segments are then combined (COMBINE_SELECT) and sent to the Broker. The Broker combines and reduces the results from different servers (BROKER_REDUCE) into a final result that is sent to the user. The PLAN_START(numSegmentsForThisPlan:1) indicates that a single segment matched this query plan. If verbose mode is enabled many plans can be returned and each will contain a node indicating the number of matched segments.

The rest of this document illustrates the EXPLAIN PLAN output with examples and describe the operators that show up in the output of the EXPLAIN PLAN.

EXPLAIN PLAN using verbose mode for a query that evaluates filters with and without index

SET explainPlanVerbose=true;
EXPLAIN PLAN FOR
  SELECT playerID, playerName
    FROM baseballStats
   WHERE playerID = 'aardsda01' AND playerName = 'David Allan'

BROKER_REDUCE(limit:10)
└── COMBINE_SELECT
    └── PLAN_START(numSegmentsForThisPlan:1)
        └── SELECT(selectList:playerID, playerName)
            └── TRANSFORM_PASSTHROUGH(playerID, playerName)
                └── PROJECT(playerName, playerID)
                    └── DOC_ID_SET
                        └── FILTER_AND
                            ├── FILTER_INVERTED_INDEX(indexLookUp:inverted_index,operator:EQ,predicate:playerID = 'aardsda01')
                            └── FILTER_FULL_SCAN(operator:EQ,predicate:playerName = 'David Allan')
    └── PLAN_START(numSegmentsForThisPlan:1)
        └── SELECT(selectList:playerID, playerName)
            └── TRANSFORM_PASSTHROUGH(playerID, playerName)
                └── PROJECT(playerName, playerID)
                    └── DOC_ID_SET
                        └── FILTER_EMPTY

Since verbose mode is enabled, the EXPLAIN PLAN output returns two plans matching one segment each (assuming 2 segments for this table). The first EXPLAIN PLAN output above shows that Pinot used an inverted index to evaluate the predicate "playerID = 'aardsda01'" (FILTER_INVERTED_INDEX). The result was then fully scanned (FILTER_FULL_SCAN) to evaluate the second predicate "playerName = 'David Allan'". Note that the two predicates are being combined using AND in the query; hence, only the data that satsified the first predicate needs to be scanned for evaluating the second predicate. However, if the predicates were being combined using OR, the query would run very slowly because the entire "playerName" column would need to be scanned from top to bottom to look for values satisfying the second predicate. To improve query efficiency in such cases, one should consider indexing the "playerName" column as well. The second plan output shows a FILTER_EMPTY indicating that no matching documents were found for one segment.

EXPLAIN PLAN ON GROUP BY QUERY

EXPLAIN PLAN FOR
  SELECT playerID, count(*)
    FROM baseballStats
   WHERE playerID != 'aardsda01'
   GROUP BY playerID

BROKER_REDUCE(limit:10)
└── COMBINE_GROUPBY_ORDERBY
    └── PLAN_START(numSegmentsForThisPlan:1)
        └── AGGREGATE_GROUPBY_ORDERBY(groupKeys:playerID, aggregations:count(*))
            └── TRANORM_PASSTHROUGH(playerID)
                └── PROJECT(playerID)
                    └── DOC_ID_SET
                        └── FILTER_INVERTED_INDEX(indexLookUp:inverted_index,operator:NOT_EQ,predicate:playerID != 'aardsda01')

The EXPLAIN PLAN output above shows how GROUP BY queries are evaluated in Pinot. GROUP BY results are created on the server (AGGREGATE_GROUPBY_ORDERBY) for each segment on the server. The server then combines segment-level GROUP BY results (COMBINE_GROUPBY_ORDERBY) and sends the combined result to the Broker. The Broker combines GROUP BY result from all the servers to produce the final result which is send to the user. Note that the COMBINE_SELECT operator from the previous query was not used here, instead a different COMBINE_GROUPBY_ORDERBY operator was used. Depending upon the type of query different combine operators such as COMBINE_DISTINCT and COMBINE_ORDERBY etc may be seen.

EXPLAIN PLAN OPERATORS

The root operator of the EXPLAIN PLAN output is BROKER_REDUCE. BROKER_REDUCE indicates that Broker is processing and combining server results into final result that is sent back to the user. BROKER_REDUCE has a COMBINE operator as its child. Combine operator combines the results of query evaluation from each segment on the server and sends the combined result to the Broker. There are several combine operators (COMBINE_GROUPBY_ORDERBY, COMBINE_DISTINCT, COMBINE_AGGREGATE, etc.) that run depending upon the operations being performed by the query. Under the Combine operator, either a Select (SELECT, SELECT_ORDERBY, etc.) or an Aggregate (AGGREGATE, AGGREGATE_GROUPBY_ORDERBY, etc.) can appear. Aggreate operator is present when query performs aggregation (count(*), min, max, etc.); otherwise, a Select operator is present. If the query performs scalar transformations (Addition, Multiplication, Concat, etc.), then one would see TRANSFORM operator appear under the SELECT operator. Often a TRANSFORM_PASSTHROUGH operator is present instead of the TRANSFORM operator. TRANSFORM_PASSTHROUGH just passes results from operators that appear lower in the operator execution heirarchy to the SELECT operator. DOC_ID_SET operator usually appear above FILTER operators and indicate that a list of matching document IDs are assessed. FILTER operators usually appear at the bottom of the operator heirarchy and show index use. For example, the presence of FILTER_FULL_SCAN indicates that index was not used (and hence the query is likely to run relatively slow). However, if the query used an index one of the indexed filter operators (FILTER_SORTED_INDEX, FILTER_RANGE_INDEX, FILTER_INVERTED_INDEX, FILTER_JSON_INDEX, etc.) will show up.

A common use case is filtering on an id field with a list of values. This can be done with the IN clause, but using IN doesn't perform well with large lists of IDs. For large lists of IDs, we recommend using an IdSet.

Functions

ID_SET

ID_SET(columnName, 'sizeThresholdInBytes=8388608;expectedInsertions=5000000;fpp=0.03' )

This function returns a base 64 encoded IdSet of the values for a single column. The IdSet implementation used depends on the column data type:

• INT - RoaringBitmap unless sizeThresholdInBytes is exceeded, in which case Bloom Filter.

• LONG - Roaring64NavigableMap unless sizeThresholdInBytes is exceeded, in which case Bloom Filter.

• Other types - Bloom Filter

The following parameters are used to configure the Bloom Filter:

• expectedInsertions - Number of expected insertions for the BloomFilter, must be positive

• fpp - False positive probability to use for the BloomFilter. Must be positive and less than 1.0.

Note that when a Bloom Filter is used, the filter results are approximate - you can get false-positive results (for membership in the set), leading to potentially unexpected results.

IN_ID_SET

IN_ID_SET(columnName, base64EncodedIdSet)

This function returns 1 if a column contains a value specified in the IdSet and 0 if it does not.

IN_SUBQUERY

IN_SUBQUERY(columnName, subQuery)

This function generates an IdSet from a subquery and then filters ids based on that IdSet on a Pinot broker.

IN__PARTITIONED__SUBQUERY

IN_PARTITIONED_SUBQUERY(columnName, subQuery)

This function generates an IdSet from a subquery and then filters ids based on that IdSet on a Pinot server.

This function works best when the data is partitioned by the id column and each server contains all the data for a partition. The generated IdSet for the subquery will be smaller as it will only contain the ids for the partitions served by the server. This will give better performance.

Examples

Create IdSet

You can create an IdSet of the values in the yearID column by running the following:

SELECT ID_SET(yearID)
FROM baseballStats
WHERE teamID = 'WS1'

When creating an IdSet for values in non INT/LONG columns, we can configure the expectedInsertions:

SELECT ID_SET(playerName, 'expectedInsertions=10')
FROM baseballStats
WHERE teamID = 'WS1'

SELECT ID_SET(playerName, 'expectedInsertions=100')
FROM baseballStats
WHERE teamID = 'WS1'

We can also configure the fpp parameter:

SELECT ID_SET(playerName, 'expectedInsertions=100;fpp=0.01')
FROM baseballStats
WHERE teamID = 'WS1'

Filter by values in IdSet

We can use the IN_ID_SET function to filter a query based on an IdSet. To return rows for _yearID_s in the IdSet, run the following:

SELECT yearID, count(*) 
FROM baseballStats 
WHERE IN_ID_SET(
 yearID,   
 'ATowAAABAAAAAAA7ABAAAABtB24HbwdwB3EHcgdzB3QHdQd2B3cHeAd5B3oHewd8B30Hfgd/B4AHgQeCB4MHhAeFB4YHhweIB4kHigeLB4wHjQeOB48HkAeRB5IHkweUB5UHlgeXB5gHmQeaB5sHnAedB54HnwegB6EHogejB6QHpQemB6cHqAc='
  ) = 1 
GROUP BY yearID

Filter by values not in IdSet

To return rows for _yearID_s not in the IdSet, run the following:

SELECT yearID, count(*) 
FROM baseballStats 
WHERE IN_ID_SET(
  yearID,   
  'ATowAAABAAAAAAA7ABAAAABtB24HbwdwB3EHcgdzB3QHdQd2B3cHeAd5B3oHewd8B30Hfgd/B4AHgQeCB4MHhAeFB4YHhweIB4kHigeLB4wHjQeOB48HkAeRB5IHkweUB5UHlgeXB5gHmQeaB5sHnAedB54HnwegB6EHogejB6QHpQemB6cHqAc='
  ) = 0 
GROUP BY yearID

Filter on broker

To filter rows for _yearID_s in the IdSet on a Pinot Broker, run the following query:

SELECT yearID, count(*) 
FROM baseballStats 
WHERE IN_SUBQUERY(
  yearID, 
  'SELECT ID_SET(yearID) FROM baseballStats WHERE teamID = ''WS1'''
  ) = 1
GROUP BY yearID  

To filter rows for _yearID_s not in the IdSet on a Pinot Broker, run the following query:

SELECT yearID, count(*) 
FROM baseballStats 
WHERE IN_SUBQUERY(
  yearID, 
  'SELECT ID_SET(yearID) FROM baseballStats WHERE teamID = ''WS1'''
  ) = 0
GROUP BY yearID  

Filter on server

To filter rows for _yearID_s in the IdSet on a Pinot Server, run the following query:

SELECT yearID, count(*) 
FROM baseballStats 
WHERE IN_PARTITIONED_SUBQUERY(
  yearID, 
  'SELECT ID_SET(yearID) FROM baseballStats WHERE teamID = ''WS1'''
  ) = 1
GROUP BY yearID  

To filter rows for _yearID_s not in the IdSet on a Pinot Server, run the following query:

SELECT yearID, count(*) 
FROM baseballStats 
WHERE IN_PARTITIONED_SUBQUERY(
  yearID, 
  'SELECT ID_SET(yearID) FROM baseballStats WHERE teamID = ''WS1'''
  ) = 0
GROUP BY yearID  

Cardinality estimation is a classic problem. Pinot solves it with multiple ways each of which has a trade-off between accuracy and latency.

Exact Results

Functions:

• DistinctCount(x) -> LONG

Returns accurate count for all unique values in a column.

The underlying implementation is using a IntOpenHashSet in library: it.unimi.dsi:fastutil:8.2.3 to hold all the unique values.

Approximate Results

It usually takes a lot of resources and time to compute exact results for unique counting on large datasets. In some circumstances, we can tolerate a certain error rate, in which case we can use approximation functions to tackle this problem.

HyperLogLog

HyperLogLog is an approximation algorithm for unique counting. It uses fixed number of bits to estimate the cardinality of given data set.

Pinot leverages HyperLogLog Class in library com.clearspring.analytics:stream:2.7.0as the data structure to hold intermediate results.

Functions:

• DistinctCountHLL(x)_ -> LONG_

For column type INT/LONG/FLOAT/DOUBLE/STRING , Pinot treats each value as an individual entry to add into HyperLogLog Object, then compute the approximation by calling method cardinality().

For column type BYTES, Pinot treats each value as a serialized HyperLogLog Object with pre-aggregated values inside. The bytes value is generated by org.apache.pinot.core.common.ObjectSerDeUtils.HYPER_LOG_LOG_SER_DE.serialize(hyperLogLog).

All deserialized HyperLogLog object will be merged into one then calling method **cardinality() **to get the approximated unique count.

Theta Sketches

The Theta Sketch framework enables set operations over a stream of data, and can also be used for cardinality estimation. Pinot leverages the Sketch Class and its extensions from the library org.apache.datasketches:datasketches-java:4.2.0 to perform distinct counting as well as evaluating set operations.

Functions:

• DistinctCountThetaSketch(<thetaSketchColumn>, <thetaSketchParams>, predicate1, predicate2..., postAggregationExpressionToEvaluate**) **-> LONG

  • thetaSketchColumn (required): Name of the column to aggregate on.

  • thetaSketchParams (required): Parameters for constructing the intermediate theta-sketches. Currently, the only supported parameter is nominalEntries.

  • predicates (optional)_: _ These are individual predicates of form lhs <op> rhs which are applied on rows selected by the where clause. During intermediate sketch aggregation, sketches from the thetaSketchColumn that satisfies these predicates are unionized individually. For example, all filtered rows that match country=USA are unionized into a single sketch. Complex predicates that are created by combining (AND/OR) of individual predicates is supported.

  • postAggregationExpressionToEvaluate (required): The set operation to perform on the individual intermediate sketches for each of the predicates. Currently supported operations are SET_DIFF, SET_UNION, SET_INTERSECT , where DIFF requires two arguments and the UNION/INTERSECT allow more than two arguments.

In the example query below, the where clause is responsible for identifying the matching rows. Note, the where clause can be completely independent of the postAggregationExpression. Once matching rows are identified, each server unionizes all the sketches that match the individual predicates, i.e. country='USA' , device='mobile' in this case. Once the broker receives the intermediate sketches for each of these individual predicates from all servers, it performs the final aggregation by evaluating the postAggregationExpression and returns the final cardinality of the resulting sketch.

select distinctCountThetaSketch(
  sketchCol, 
  'nominalEntries=1024', 
  'country'=''USA'' AND 'state'=''CA'', 'device'=''mobile'', 'SET_INTERSECT($1, $2)'
) 
from table 
where country = 'USA' or device = 'mobile...' 

• DistinctCountRawThetaSketch(<thetaSketchColumn>, <thetaSketchParams>, predicate1, predicate2..., postAggregationExpressionToEvaluate**)** -> HexEncoded Serialized Sketch Bytes

This is the same as the previous function, except it returns the byte serialized sketch instead of the cardinality sketch. Since Pinot returns responses as JSON strings, bytes are returned as hex encoded strings. The hex encoded string can be deserialized into sketch by using the library org.apache.commons.codec.binaryas Hex.decodeHex(stringValue.toCharArray()).

Tuple Sketches

The Tuple Sketch is an extension of the Theta Sketch. Tuple sketches store an additional summary value with each retained entry which makes the sketch ideal for summarizing attributes such as impressions or clicks. Tuple sketches are interoperable with the Theta Sketch and enable set operations over a stream of data, and can also be used for cardinality estimation.

Functions:

• avgValueIntegerSumTupleSketch(<tupleSketchColumn>, <tupleSketchLgK>**) -> Long

  • tupleSketchColumn (required): Name of the column to aggregate on.

  • tupleSketchLgK (optional): lgK which is the the log2 of K, which controls both the size and accuracy of the sketch.

This function can be used to combine the summary values from the random sample stored within the Tuple sketch and formulate an estimate for an average that applies to the entire dataset. The average should be interpreted as applying to each key tracked by the sketch and is rounded to the nearest whole number.

• distinctCountTupleSketch(<tupleSketchColumn>, <tupleSketchLgK>**) -> LONG

  • tupleSketchColumn (required): Name of the column to aggregate on.

  • tupleSketchLgK (optional): lgK which is the the log2 of K, which controls both the size and accuracy of the sketch.

This returns the cardinality estimate for a column where the values are already encoded as Tuple sketches, stored as BYTES.

• distinctCountRawIntegerSumTupleSketch(<tupleSketchColumn>, <tupleSketchLgK>**) -> HexEncoded Serialized Sketch Bytes

This is the same as the previous function, except it returns the byte serialized sketch instead of the cardinality sketch. Since Pinot returns responses as JSON strings, bytes are returned as hex encoded strings. The hex encoded string can be deserialized into sketch by using the library org.apache.commons.codec.binaryas Hex.decodeHex(stringValue.toCharArray()).

• sumValueIntegerSumTupleSketch(<tupleSketchColumn>, <tupleSketchLgK>**) -> Long

  • tupleSketchColumn (required): Name of the column to aggregate on.

  • tupleSketchLgK (optional): lgK which is the the log2 of K, which controls both the size and accuracy of the sketch.

This function can be used to combine the summary values (using sum) from the random sample stored within the Tuple sketch and formulate an estimate that applies to the entire dataset. See avgValueIntegerSumTupleSketch for extracting an average for integer summaries. If other merging options are required, it is best to extract the raw sketches directly or to implement a new Pinot aggregation function to support these.

Compressed Probability Counting (CPC) Sketches

The Compressed Probability Counting(CPC) Sketch enables extremely space-efficient cardinality estimation. The stored CPC sketch can consume about 40% less space than an HLL sketch of comparable accuracy. Pinot can aggregate multiple existing CPC sketches together to get a total distinct count or estimated directly from raw values.

Functions:

• distinctCountCpcSketch(<cpcSketchColumn>, <cpcSketchLgK>**) -> Long

  • cpcSketchColumn (required): Name of the column to aggregate on.

  • cpcSketchLgK (optional): lgK which is the the log2 of K, which controls both the size and accuracy of the sketch.

This returns the cardinality estimate for a column.

• distinctCountRawCpcSketch(<cpcSketchColumn>, <cpcSketchLgK>**) -> HexEncoded Serialized Sketch Bytes

  • cpcSketchColumn (required): Name of the column to aggregate on.

  • cpcSketchLgK (optional): lgK which is the the log2 of K, which controls both the size and accuracy of the sketch.

This is the same as the previous function, except it returns the byte serialized sketch instead of the cardinality sketch. Since Pinot returns responses as JSON strings, bytes are returned as hex encoded strings. The hex encoded string can be deserialized into sketch by using the library org.apache.commons.codec.binaryas Hex.decodeHex(stringValue.toCharArray()).

UltraLogLog (ULL) Sketches

The UltraLogLog Sketch from Dynatrace is a variant of HyperLogLog and is used for approximate distinct counts. The UltraLogLog sketch shares many of the same properties of a typical HyperLogLog sketch but requires less space and also provides a simpler and faster estimator.

Pinot uses an production-ready Java implementation available in Hash4j available under the Apache license.

Functions:

• distinctCountULL(<ullSketchColumn>, <ullSketchPrecision>**) -> Long

  • ullSketchColumn (required): Name of the column to aggregate on.

  • ullSketchPrecision (optional): p which is the precision parameter, which controls both the size and accuracy of the sketch.

This returns the cardinality estimate for a column.

• distinctCountRawULL(<cpcSketchColumn>, <ullSketchPrecision>**) -> HexEncoded Serialized Sketch Bytes

  • ullSketchColumn (required): Name of the column to aggregate on.

  • ullSketchPrecision (optional): p which is the precision parameter, which controls both the size and accuracy of the sketch.

This is the same as the previous function, except it returns the byte serialized sketch instead of the cardinality sketch. Since Pinot returns responses as JSON strings, bytes are returned as hex encoded strings. The hex encoded string can be deserialized into sketch by using the library org.apache.commons.codec.binaryas Hex.decodeHex(stringValue.toCharArray()).

Query Pinot using supported syntax.

In this guide we will learn about the heuristics used for trimming results in Pinot's grouping algorithm (used when processing GROUP BY queries) to make sure that the server doesn't run out of memory.

Within segment

When grouping rows within a segment, Pinot keeps a maximum of <numGroupsLimit> groups per segment. This value is set to 100,000 by default and can be configured by the pinot.server.query.executor.num.groups.limit property.

If the number of groups of a segment reaches this value, the extra groups will be ignored and the results returned may not be completely accurate. The numGroupsLimitReached property will be set to true in the query response if the value is reached.

Trimming tail groups

After the inner segment groups have been computed, the Pinot query engine optionally trims tail groups. Tail groups are ones that have a lower rank based on the ORDER BY clause used in the query.

This configuration is disabled by default, but can be enabled by configuring the pinot.server.query.executor.min.segment.group.trim.size property.

When segment group trim is enabled, the query engine will trim the tail groups and keep max(<minSegmentGroupTrimSize>, 5 * LIMIT) groups if it gets more groups. Pinot keeps at least 5 * LIMIT groups when trimming tail groups to ensure the accuracy of results.

This value can be overridden on a query by query basis by passing the following option:

SELECT * 
FROM ...

OPTION(minSegmentGroupTrimSize=<minSegmentGroupTrimSize>)

Cross segments

Once grouping has been done within a segment, Pinot will merge segment results and trim tail groups and keep max(<minServerGroupTrimSize>, 5 * LIMIT) groups if it gets more groups.

<minServerGroupTrimSize> is set to 5,000 by default and can be adjusted by configuring the pinot.server.query.executor.min.server.group.trim.size property. When setting the configuration to -1, the cross segments trim can be disabled.

This value can be overridden on a query by query basis by passing the following option:

SELECT * 
FROM ...

OPTION(minServerGroupTrimSize=<minServerGroupTrimSize>)

When cross segments trim is enabled, the server will trim the tail groups before sending the results back to the broker. It will also trim the tail groups when the number of groups reaches the <trimThreshold>.

This configuration is set to 1,000,000 by default and can be adjusted by configuring the pinot.server.query.executor.groupby.trim.threshold property.

A higher threshold reduces the amount of trimming done, but consumes more heap memory. If the threshold is set to more than 1,000,000,000, the server will only trim the groups once before returning the results to the broker.

At Broker

When broker performs the final merge of the groups returned by various servers, there is another level of trimming that takes place. The tail groups are trimmed and max(<minBrokerGroupTrimSize>, 5 * LIMIT) groups are retained.

Default value of <minBrokerGroupTrimSize> is set to 5000. This can be adjusted by configuring pinot.broker.min.group.trim.size property.

GROUP BY behavior

Pinot sets a default LIMIT of 10 if one isn't defined and this applies to GROUP BY queries as well. Therefore, if no limit is specified, Pinot will return 10 groups.

Pinot will trim tail groups based on the ORDER BY clause to reduce the memory footprint and improve the query performance. It keeps at least 5 * LIMIT groups so that the results give good enough approximation in most cases. The configurable min trim size can be used to increase the groups kept to improve the accuracy but has a larger extra memory footprint.

HAVING behavior

If the query has a HAVING clause, it is applied on the merged GROUP BY results that already have the tail groups trimmed. If the HAVING clause is the opposite of the ORDER BY order, groups matching the condition might already be trimmed and not returned. e.g.

SELECT SUM(colA) 
FROM myTable 
GROUP BY colB 
HAVING SUM(colA) < 100 
ORDER BY SUM(colA) DESC 
LIMIT 10

Increase min trim size to keep more groups in these cases.

Configuration Parameters

JOINs overview

Pinot 1.0 introduces support for all JOIN types. JOINs in Pinot significantly reduce query latency and simplify architecture, achieving the best performance currently available for an OLAP database.

Use JOINs to combine two tables (a left and right table) together, based on a related column between the tables, and other join filters. JOINs let you gain more insights from your data.

Supported JOINs types and examples

Inner join

The inner join selects rows that have matching values in both tables.

Syntax:

SELECT myTable.column1,myTable.column2,myOtherTable.column1,....
FROM mytable INNER JOIN table2
ON table1.matching_column = myOtherTable.matching_column;

Example of inner join

Joins a table containing user transactions with a table containing promotions shown to the users, to show the spending for every userID.

SELECT 
  p.userID, t.spending_val

FROM promotion AS p JOIN transaction AS t 
  ON p.userID = t.userID

WHERE
  p.promotion_val > 10
  AND t.transaction_type IN ('CASH', 'CREDIT')  
  AND t.transaction_epoch >= p.promotion_start_epoch
  AND t.transaction_epoch < p.promotion_end_epoch  

Left join

A left join returns all values from the left relation and the matched values from the right table, or appends NULL if there is no match. Also referred to as a left outer join.

Syntax:

SELECT myTable.column1,table1.column2,myOtherTable.column1,....
FROM myTable LEFT JOIN myOtherTable
ON myTable.matching_column = myOtherTable.matching_column;

Right join

A right join returns all values from the right relation and the matched values from the left relation, or appends NULL if there is no match. It is also referred to as a right outer join.

Syntax:

SELECT table1.column1,table1.column2,table2.column1,....
FROM table1 
RIGHT JOIN table2
ON table1.matching_column = table2.matching_column;

Full join

A full join returns all values from both relations, appending NULL values on the side that does not have a match. It is also referred to as a full outer join.

Syntax:

SELECT table1.column1,table1.column2,table2.column1,....
FROM table1 
FULL JOIN table2
ON table1.matching_column = table2.matching_column;

Cross join

Syntax:

SELECT * 
FROM table1 
CROSS JOIN table2;

Semi/Anti join

Semi/anti-join returns rows from the first table where no matches are found in the second table. Returns one copy of each row in the first table for which no match is found.

Syntax:

SELECT  myTable.column1, myOtherTable.column1
 FROM  myOtherTable
 WHERE  NOT EXISTS [ join_criteria ]

Equi join

An equi join uses an equality operator to match a single or multiple column values of the relative tables.

Syntax:

SELECT *
FROM table1 
JOIN table2
[ON (join_condition)]

OR

SELECT column_list 
FROM table1, table2....
WHERE table1.column_name =
table2.column_name; 

JOINs optimizations

Pinot JOINs include the following optimizations:

• Predicate push-down to individual tables

• Indexing and pruning to reduce scanning and speeds up query processing

• Smart data layout considerations to minimize data shuffling

• Query hints for fine-tuning JOIN operations.

Lookup UDF is used to get dimension data via primary key from a dimension table allowing a decoration join functionality. Lookup UDF can only be used with in Pinot.

Syntax

The UDF function syntax is listed as below:

lookupUDFSpec:
    LOOKUP
    '('
    '''dimTable'''
    '''dimColToLookup'''
    [ '''dimJoinKey''', factJoinKey ]*
    ')'

• dimTable Name of the dim table to perform the lookup on.

• dimColToLookUp The column name of the dim table to be retrieved to decorate our result.

• dimJoinKey The column name on which we want to perform the lookup i.e. the join column name for dim table.

• factJoinKey The column name on which we want to perform the lookup against e.g. the join column name for fact table

Noted that:

1. all the dim-table-related expressions are expressed as literal strings, this is the LOOKUP UDF syntax limitation: we cannot express column identifier which doesn't exist in the query's main table, which is the factTable table.

2. the syntax definition of [ '''dimJoinKey''', factJoinKey ]* indicates that if there are multiple dim partition columns, there should be multiple join key pair expressed.

Examples

Here are some of the examples

Single-partition-key-column Example

Consider the table baseballStats

and dim table dimBaseballTeams

several acceptable queries are:

Dim-Fact LOOKUP example

SELECT 
  playerName, 
  teamID, 
  LOOKUP('dimBaseballTeams', 'teamName', 'teamID', teamID) AS teamName, 
  LOOKUP('dimBaseballTeams', 'teamAddress', 'teamID', teamID) AS teamAddress
FROM baseballStats 

Self LOOKUP example

SELECT 
  teamID, 
  teamName AS nameFromLocal,
  LOOKUP('dimBaseballTeams', 'teamName', 'teamID', teamID) AS nameFromLookup
FROM dimBaseballTeams

Complex-partition-key-columns Example

Consider a single dimension table with schema:

BILLING SCHEMA

Self LOOKUP example

select 
  customerId,
  missedPayment, 
  LOOKUP('billing', 'city', 'customerId', customerId, 'creditHistory', creditHistory) AS lookedupCity 
from billing

Usage FAQ

• The data return type of the UDF will be that of the dimColToLookUp column type.

• when multiple primary key columns are used for the dimension table (e.g. composite primary key), ensure that the order of keys appearing in the lookup() UDF is the same as the order defined in the primaryKeyColumns from the dimension table schema.

Math Functions

String Functions

Multiple string functions are supported out of the box from release-0.5.0 .

DateTime Functions

Date time functions allow you to perform transformations on columns that contain timestamps or dates.

JSON Functions

Transform Functions

These functions can only be used in Pinot SQL queries.

Scalar Functions

These functions can be used for column transformation in table ingestion configs.

Binary Functions

Multi-value Column Functions

All of the functions mentioned till now only support single value columns. You can use the following functions to do operations on multi-value columns.

Advanced Queries

Geospatial Queries

Text Queries

Many of the datasets are time series in nature, tracking state change of an entity over time. The granularity of recorded data points might be sparse or the events could be missing due to network and other device issues in the IOT environment. But analytics applications which are tracking the state change of these entities over time, might be querying for values at lower granularity than the metric interval.

Here is the sample data set tracking the status of parking lots in parking space.

We want to find out the total number of parking lots that are occupied over a period of time which would be a common use case for a company that manages parking spaces.

Let us take 30 minutes' time bucket as an example:

If you look at the above table, you will see a lot of missing data for parking lots inside the time buckets. In order to calculate the number of occupied park lots per time bucket, we need gap fill the missing data.

The Ways of Gap Filling the Data

There are two ways of gap filling the data: FILL_PREVIOUS_VALUE and FILL_DEFAULT_VALUE.

FILL_PREVIOUS_VALUE means the missing data will be filled with the previous value for the specific entity, in this case, park lot, if the previous value exists. Otherwise, it will be filled with the default value.

FILL_DEFAULT_VALUE means that the missing data will be filled with the default value. For numeric column, the defaul value is 0. For Boolean column type, the default value is false. For TimeStamp, it is January 1, 1970, 00:00:00 GMT. For STRING, JSON and BYTES, it is empty String. For Array type of column, it is empty array.

We will leverage the following the query to calculate the total occupied parking lots per time bucket.

Aggregation/Gapfill/Aggregation

Query Syntax

SELECT time_col, SUM(status) AS occupied_slots_count
FROM (
    SELECT GAPFILL(time_col,'1:MILLISECONDS:SIMPLE_DATE_FORMAT:yyyy-MM-dd HH:mm:ss.SSS','2021-10-01 09:00:00.000',
                   '2021-10-01 12:00:00.000','30:MINUTES', FILL(status, 'FILL_PREVIOUS_VALUE'),
                    TIMESERIESON(lotId)), lotId, status
    FROM (
        SELECT DATETIMECONVERT(event_time,'1:MILLISECONDS:EPOCH',
               '1:MILLISECONDS:SIMPLE_DATE_FORMAT:yyyy-MM-dd HH:mm:ss.SSS','30:MINUTES') AS time_col,
               lotId, lastWithTime(is_occupied, event_time, 'INT') AS status
        FROM parking_data
        WHERE event_time >= 1633078800000 AND  event_time <= 1633089600000
        GROUP BY 1, 2
        ORDER BY 1
        LIMIT 100)
    LIMIT 100)
GROUP BY 1
LIMIT 100

Workflow

The most nested sql will convert the raw event table to the following table.

The second most nested sql will gap fill the returned data as following:

The outermost query will aggregate the gapfilled data as follows:

There is one assumption we made here that the raw data is sorted by the timestamp. The Gapfill and Post-Gapfill Aggregation will not sort the data.

The above example just shows the use case where the three steps happen:

1. The raw data will be aggregated;

2. The aggregated data will be gapfilled;

3. The gapfilled data will be aggregated.

There are three more scenarios we can support.

Select/Gapfill

If we want to gapfill the missing data per half an hour time bucket, here is the query:

Query Syntax

SELECT GAPFILL(DATETIMECONVERT(event_time,'1:MILLISECONDS:EPOCH',
               '1:MILLISECONDS:SIMPLE_DATE_FORMAT:yyyy-MM-dd HH:mm:ss.SSS','30:MINUTES'),
               '1:MILLISECONDS:SIMPLE_DATE_FORMAT:yyyy-MM-dd HH:mm:ss.SSS','2021-10-01 09:00:00.000',
               '2021-10-01 12:00:00.000','30:MINUTES', FILL(is_occupied, 'FILL_PREVIOUS_VALUE'),
               TIMESERIESON(lotId)) AS time_col, lotId, is_occupied
FROM parking_data
WHERE event_time >= 1633078800000 AND  event_time <= 1633089600000
ORDER BY 1
LIMIT 100

Workflow

At first the raw data will be transformed as follows:

Then it will be gapfilled as follows:

Aggregate/Gapfill

Query Syntax

SELECT GAPFILL(time_col,'1:MILLISECONDS:SIMPLE_DATE_FORMAT:yyyy-MM-dd HH:mm:ss.SSS','2021-10-01 09:00:00.000',
               '2021-10-01 12:00:00.000','30:MINUTES', FILL(status, 'FILL_PREVIOUS_VALUE'),
               TIMESERIESON(lotId)), lotId, status
FROM (
    SELECT DATETIMECONVERT(event_time,'1:MILLISECONDS:EPOCH',
           '1:MILLISECONDS:SIMPLE_DATE_FORMAT:yyyy-MM-dd HH:mm:ss.SSS','30:MINUTES') AS time_col,
           lotId, lastWithTime(is_occupied, event_time, 'INT') AS status
    FROM parking_data
    WHERE event_time >= 1633078800000 AND  event_time <= 1633089600000
    GROUP BY 1, 2
    ORDER BY 1
    LIMIT 100)
LIMIT 100

Workflow

The nested sql will convert the raw event table to the following table.

The outer sql will gap fill the returned data as following:

Gapfill/Aggregate

Query Syntax

SELECT time_col, SUM(is_occupied) AS occupied_slots_count
FROM (
    SELECT GAPFILL(DATETIMECONVERT(event_time,'1:MILLISECONDS:EPOCH',
           '1:MILLISECONDS:SIMPLE_DATE_FORMAT:yyyy-MM-dd HH:mm:ss.SSS','30:MINUTES'),
           '1:MILLISECONDS:SIMPLE_DATE_FORMAT:yyyy-MM-dd HH:mm:ss.SSS','2021-10-01 09:00:00.000',
           '2021-10-01 12:00:00.000','30:MINUTES', FILL(is_occupied, 'FILL_PREVIOUS_VALUE'),
           TIMESERIESON(lotId)) AS time_col, lotId, is_occupied
    FROM parking_data
    WHERE event_time >= 1633078800000 AND  event_time <= 1633089600000
    ORDER BY 1
    LIMIT 100)
GROUP BY 1
LIMIT 100

Workflow

The raw data will be transformed as following at first:

The transformed data will be gap filled as follows:

The aggregation will generate the following table:

To see how JSON data can be queried, assume that we have the following table:

Table myTable:
  id        INTEGER
  jsoncolumn    JSON 

Table data:
101,{"name":{"first":"daffy"\,"last":"duck"}\,"score":101\,"data":["a"\,"b"\,"c"\,"d"]}
102,{"name":{"first":"donald"\,"last":"duck"}\,"score":102\,"data":["a"\,"b"\,"e"\,"f"]}
103,{"name":{"first":"mickey"\,"last":"mouse"}\,"score":103\,"data":["a"\,"b"\,"g"\,"h"]}
104,{"name":{"first":"minnie"\,"last":"mouse"}\,"score":104\,"data":["a"\,"b"\,"i"\,"j"]}
105,{"name":{"first":"goofy"\,"last":"dwag"}\,"score":104\,"data":["a"\,"b"\,"i"\,"j"]}
106,{"person":{"name":"daffy duck"\,"companies":[{"name":"n1"\,"title":"t1"}\,{"name":"n2"\,"title":"t2"}]}}
107,{"person":{"name":"scrooge mcduck"\,"companies":[{"name":"n1"\,"title":"t1"}\,{"name":"n2"\,"title":"t2"}]}}

We also assume that "jsoncolumn" has a on it. Note that the last two rows in the table have different structure than the rest of the rows. In keeping with JSON specification, a JSON column can contain any valid JSON data and doesn't need to adhere to a predefined schema. To pull out the entire JSON document for each row, we can run the query below:

SELECT id, jsoncolumn 
  FROM myTable

To drill down and pull out specific keys within the JSON column, we simply append the JsonPath expression of those keys to the end of the column name.

SELECT id,
       json_extract_scalar(jsoncolumn, '$.name.last', 'STRING', 'null') last_name,
       json_extract_scalar(jsoncolumn, '$.name.first', 'STRING', 'null') first_name
       json_extract_scalar(jsoncolumn, '$.data[1]', 'STRING', 'null') value
  FROM myTable

Note that the third column (value) is null for rows with id 106 and 107. This is because these rows have JSON documents that don't have a key with JsonPath $.data[1]. We can filter out these rows.

SELECT id,
       json_extract_scalar(jsoncolumn, '$.name.last', 'STRING', 'null') last_name,
       json_extract_scalar(jsoncolumn, '$.name.first', 'STRING', 'null') first_name,
       json_extract_scalar(jsoncolumn, '$.data[1]', 'STRING', 'null') value
  FROM myTable
 WHERE JSON_MATCH(jsoncolumn, '"$.data[1]" IS NOT NULL')

Certain last names (duck and mouse for example) repeat in the data above. We can get a count of each last name by running a GROUP BY query on a JsonPath expression.

  SELECT json_extract_scalar(jsoncolumn, '$.name.last', 'STRING', 'null') last_name,
         count(*)
    FROM myTable
   WHERE JSON_MATCH(jsoncolumn, '"$.data[1]" IS NOT NULL')
GROUP BY json_extract_scalar(jsoncolumn, '$.name.last', 'STRING', 'null')
ORDER BY 2 DESC

Also there is numerical information (jsconcolumn.$.id) embeded within the JSON document. We can extract those numerical values from JSON data into SQL and sum them up using the query below.

  SELECT json_extract_scalar(jsoncolumn, '$.name.last', 'STRING', 'null') last_name,
         sum(json_extract_scalar(jsoncolumn, '$.id', 'INT', 0)) total
    FROM myTable
   WHERE JSON_MATCH(jsoncolumn, '"$.name.last" IS NOT NULL')
GROUP BY json_extract_scalar(jsoncolumn, '$.name.last', 'STRING', 'null')

JSON_MATCH and JSON_EXTRACT_SCALAR

Note that the JSON_MATCH function utilizes JsonIndex and can only be used if a JsonIndex is already present on the JSON column. As shown in the examples above, the second argument of JSON_MATCH operator takes a predicate. This predicate is evaluated against the JsonIndex and supports =, !=, IS NULL, or IS NOT NULL operators. Relational operators, such as >, <, >=, and <= are currently not supported. However, you can combine the use of JSON_MATCH and JSON_EXTRACT_SCALAR function (which supports >, <, >=, and <= operators) to get the necessary functinoality as shown below.

  SELECT json_extract_scalar(jsoncolumn, '$.name.last', 'STRING', 'null') last_name,
         sum(json_extract_scalar(jsoncolumn, '$.id', 'INT', 0)) total
    FROM myTable
   WHERE JSON_MATCH(jsoncolumn, '"$.name.last" IS NOT NULL') AND json_extract_scalar(jsoncolumn, '$.id', 'INT', 0) > 102
GROUP BY json_extract_scalar(jsoncolumn, '$.name.last', 'STRING', 'null')

JSON_MATCH function also provides the ability to use wildcard * JsonPath expressions even though it doesn't support full JsonPath expressions.

  SELECT json_extract_scalar(jsoncolumn, '$.name.last', 'STRING', 'null') last_name,
         json_extract_scalar(jsoncolumn, '$.id', 'INT', 0) total
    FROM myTable
   WHERE JSON_MATCH(jsoncolumn, '"$.data[*]" = ''f''')
GROUP BY json_extract_scalar(jsoncolumn, '$.name.last', 'STRING', 'null')

While, JSON_MATCH supports IS NULL and IS NOT NULL operators, these operators should only be applied to leaf-level path elements, i.e the predicate JSON_MATCH(jsoncolumn, '"$.data[*]" IS NOT NULL') is not valid since "$.data[*]" does not address a "leaf" element of the path; however, "$.data[0]" IS NOT NULL') is valid since "$.data[0]" unambigously identifies a leaf element of the path.

JSON_EXTRACT_SCALAR does not utilize JsonIndex and therefore performs slower than JSON_MATCH which utilizes JsonIndex. However, JSON_EXTRACT_SCALAR supports a wider range for of JsonPath expressions and operators. To make the best use of fast index access (JSON_MATCH) along with JsonPath expressions (JSON_EXTRACT_SCALAR) you can combine the use of these two functions in WHERE clause.

JSON_MATCH syntax

The second argument of the JSON_MATCH function is a boolean expression in string form. This section shows how to correctly write the second argument of JSON_MATCH. Let's assume we want to search a JSON array array data for values k and j. This can be done by the following predicate:

data[0] IN ('k', 'j')

To convert this predicate into string form for use in JSON_MATCH, we first turn the left side of the predicate into an identifier by enclosing it in double quotes:

"data[0]" IN ('k', 'j')

Next, the literals in the predicate also need to be enclosed by '. Any existing ' need to be escaped as well. This gives us:

"data[0]" IN (''k'', ''j'')

Finally, we need to create a string out of the entire expression above by enclosing it in ':

'"data[0]" IN (''k'', ''j'')'

Now we have the string representation of the original predicate and this can be used in JSON_MATCH function:

   WHERE JSON_MATCH(jsoncolumn, '"data[0]" IN (''k'', ''j'')')

Aggregate functions return a single result for a group of rows. The following table shows supported aggregate functions in Pinot.

Deprecated functions:

Multi-value column functions

The following aggregation functions can be used for multi-value columns

FILTER Clause in aggregation

Pinot supports FILTER clause in aggregation queries as follows:

SELECT SUM(COL1) FILTER (WHERE COL2 > 300),
       AVG(COL2) FILTER (WHERE COL2 < 50) 
FROM MyTable WHERE COL3 > 50

In the query above, COL1 is aggregated only for rows where COL2 > 300 and COL3 > 50 . Similarly, COL2 is aggregated where COL2 < 50 and COL3 > 50.

With NULL Value Support enabled, this allows to filter out the null values while performing aggregation as follows:

SELECT SUM(COL1) FILTER (WHERE COL1 IS NOT NULL)
FROM MyTable WHERE COL3 > 50

In the above query, COL1 is aggregated only for the non-null values. Without NULL value support, we would have to filter using the default null value.

Deprecated functions:

Window aggregate overview

This is an overview of the window aggregate feature.

Window aggregate syntax

Pinot's window function (windowedAggCall) includes the following syntax definition:

windowedAggCall:
      windowAggFunction
      OVER 
      window

windowAggFunction:
      agg '(' [ ALL | DISTINCT ] value [, value ]* ')'
   |
      agg '(' '*' ')'

window:
      '('
      [ PARTITION BY expression [, expression ]* ]
      [ ORDER BY orderItem [, orderItem ]* ]
      [
          RANGE numericOrIntervalExpression { PRECEDING | FOLLOWING }
      |   ROWS numericExpression { PRECEDING | FOLLOWING }
      ]
      ')'

• windowAggCall refers to the actual windowed agg operation.

Example window aggregate query layout

The following query shows the complete components of the window function. Note, PARTITION BY and ORDER BY are optional.

SELECT FUNC(column1) OVER (PARTITION BY column2 ORDER BY column3)
    FROM tableName
    WHERE filter_clause  

Window mechanism (OVER clause)

Partition by clause

• If a PARTITION BY clause is specified, the intermediate results will be grouped into different partitions based on the values of the columns appearing in the PARTITION BY clause.

• If the PARTITION BY clause isn’t specified, the whole result will be regarded as one big partition, i.e. there is only one partition in the result set.

Order by clause

• If an ORDER BY clause is specified, all the rows within the same partition will be sorted based on the values of the columns appearing in the window ORDER BY clause. The ORDER BY clause decides the order in which the rows within a partition are to be processed.

• If no ORDER BY clause is specified while a PARTITION BY clause is specified, the order of the rows is undefined. To order the output, use a global ORDER BY clause in the query.

Frame clause

• {RANGE|ROWS} frame_start OR

• {RANGE|ROWS} BETWEEN frame_start AND frame_end; frame_start and frame_end can be any of:

  • UNBOUNDED PRECEDING: expression PRECEDING. May only be allowed in ROWS mode [depends on DB, some support some don’t]

  • CURRENT ROW expression FOLLOWING. May only be allowed in ROWS mode [depends on DB, some support some don’t]

  • UNBOUNDED FOLLOWING:

    • If no FRAME clause is specified, then the default frame behavior depends on whether ORDER BY is present or not.

    • If an ORDER BY clause is specified, the default behavior is to calculate the aggregation from the beginning of the partition to the current row or UNBOUNDED PRECEDING to CURRENT ROW.

    • If only a PARTITION BY clause is present, the default frame behavior is to calculate the aggregation from UNBOUNDED PRECEDING to CURRENT ROW.

If there is no FRAME, no PARTITION BY, and no ORDER BY clause specified in the OVER clause (empty OVER), the whole result set is regarded as one partition, and there's one frame in the window.

Inside the over clause, there are three optional components: PARTITION BY clause, ORDER BY clause, and FRAME clause.

Window aggregate functions

Window aggregate functions are commonly used to do the following:

Supported window aggregate functions are listed in the following table.

Window aggregate query examples

Sum transactions by customer ID

Calculate the rolling sum transaction amount ordered by the payment date for each customer ID (note, the default frame here is UNBOUNDED PRECEDING and CURRENT ROW).

SELECT customer_id, payment_date, amount, SUM(amount) OVER(PARTITION BY customer_id ORDER BY payment_date) from payment;

Find the minimum or maximum transaction by customer ID

Calculate the least (use MIN()) or most expensive (use MAX()) transaction made by each customer comparing all transactions made by the customer (default frame here is UNBOUNDED PRECEDING and UNBOUNDED FOLLOWING). The following query shows how to find the least expensive transaction.

SELECT customer_id, payment_date, amount, MIN(amount) OVER(PARTITION BY customer_id) from payment;

Find the average transaction amount by customer ID

Calculate a customer’s average transaction amount for all transactions they’ve made (default frame here is UNBOUNDED PRECEDING and UNBOUNDED FOLLOWING).

SELECT customer_id, payment_date, amount, AVG(amount) OVER(PARTITION BY customer_id) from payment;

Rank year-to-date sales for a sales team

Use ROW_NUMBER() to rank team members by their year-to-date sales (default frame here is UNBOUNDED PRECEDING and UNBOUNDED FOLLOWING).

SELECT ROW_NUMBER() OVER(ORDER BY SalesYTD DESC) AS Row,   
    FirstName, LastName AS "Total sales YTD"   
FROM Sales.vSalesPerson;  

Count the number of transactions by customer ID

Count the number of transactions made by each customer (default frame here is UNBOUNDED PRECEDING and UNBOUNDED FOLLOWING).

SELECT customer_id, payment_date, amount, count(amount) OVER(PARTITION BY customer_id) from payment;