EXPLAIN Syntax
Hive provides an EXPLAIN command that shows the execution plan for a query. The syntax for this statement is as follows:
EXPLAIN [EXTENDED|CBO|AST|DEPENDENCY|AUTHORIZATION|LOCKS|VECTORIZATION|ANALYZE] query
AUTHORIZATION is supported from HIVE 0.14.0 via HIVE-5961. VECTORIZATION is supported from Hive 2.3.0 via HIVE-11394. LOCKS is supported from Hive 3.2.0 via HIVE-17683.
AST was removed from EXPLAIN EXTENDED in HIVE-13533 and reinstated as a separate command in HIVE-15932.
The use of EXTENDED in the EXPLAIN statement produces extra information about the operators in the plan. This is typically physical information like file names.
A Hive query gets converted into a sequence (it is more a Directed Acyclic Graph) of stages. These stages may be map/reduce stages or they may even be stages that do metastore or file system operations like move and rename. The explain output has three parts:
- The Abstract Syntax Tree for the query
- The dependencies between the different stages of the plan
- The description of each of the stages
The description of the stages itself shows a sequence of operators with the metadata associated with the operators. The metadata may comprise things like filter expressions for the FilterOperator or the select expressions for the SelectOperator or the output file names for the FileSinkOperator.
Example
As an example, consider the following EXPLAIN query:
EXPLAIN FROM src INSERT OVERWRITE TABLE dest_g1 SELECT src.key, sum(substr(src.value,4)) GROUP BY src.key;
The output of this statement contains the following parts:
The Dependency Graph
STAGE DEPENDENCIES: Stage-1 is a root stage Stage-2 depends on stages: Stage-1 Stage-0 depends on stages: Stage-2
This shows that Stage-1 is the root stage, Stage-2 is executed after Stage-1 is done and Stage-0 is executed after Stage-2 is done.
The plans of each Stage
STAGE PLANS: Stage: Stage-1 Map Reduce Alias -> Map Operator Tree: src Reduce Output Operator key expressions: expr: key type: string sort order: + Map-reduce partition columns: expr: rand() type: double tag: -1 value expressions: expr: substr(value, 4) type: string Reduce Operator Tree: Group By Operator aggregations: expr: sum(UDFToDouble(VALUE.0)) keys: expr: KEY.0 type: string mode: partial1 File Output Operator compressed: false table: input format: org.apache.hadoop.mapred.SequenceFileInputFormat output format: org.apache.hadoop.mapred.SequenceFileOutputFormat name: binary_table Stage: Stage-2 Map Reduce Alias -> Map Operator Tree: /tmp/hive-zshao/67494501/106593589.10001 Reduce Output Operator key expressions: expr: 0 type: string sort order: + Map-reduce partition columns: expr: 0 type: string tag: -1 value expressions: expr: 1 type: double Reduce Operator Tree: Group By Operator aggregations: expr: sum(VALUE.0) keys: expr: KEY.0 type: string mode: final Select Operator expressions: expr: 0 type: string expr: 1 type: double Select Operator expressions: expr: UDFToInteger(0) type: int expr: 1 type: double File Output Operator compressed: false table: input format: org.apache.hadoop.mapred.TextInputFormat output format: org.apache.hadoop.hive.ql.io.IgnoreKeyTextOutputFormat serde: org.apache.hadoop.hive.serde2.dynamic_type.DynamicSerDe name: dest_g1 Stage: Stage-0 Move Operator tables: replace: true table: input format: org.apache.hadoop.mapred.TextInputFormat output format: org.apache.hadoop.hive.ql.io.IgnoreKeyTextOutputFormat serde: org.apache.hadoop.hive.serde2.dynamic_type.DynamicSerDe name: dest_g1In this example there are 2 map/reduce stages (Stage-1 and Stage-2) and 1 File System related stage (Stage-0). Stage-0 basically moves the results from a temporary directory to the directory corresponding to the table dest_g1.
Sort orderindicates the number of columns in key expressions that are used for sorting. Each "+" represents one column sorted in ascending order, and each "-" represents a column sorted in descending order.
A map/reduce stage itself has 2 parts:
- A mapping from table alias to Map Operator Tree – This mapping tells the mappers which operator tree to call in order to process the rows from a particular table or result of a previous map/reduce stage. In Stage-1 in the above example, the rows from src table are processed by the operator tree rooted at a Reduce Output Operator. Similarly, in Stage-2 the rows of the results of Stage-1 are processed by another operator tree rooted at another Reduce Output Operator. Each of these Reduce Output Operators partitions the data to the reducers according to the criteria shown in the metadata.
- A Reduce Operator Tree – This is the operator tree which processes all the rows on the reducer of the map/reduce job. In Stage-1 for example, the Reducer Operator Tree is carrying out a partial aggregation whereas the Reducer Operator Tree in Stage-2 computes the final aggregation from the partial aggregates computed in Stage-1.
The CBO Clause
The CBO clause outputs the plan generated by Calcite optimizer. It can optionally include information about the cost of the plan using Calcite default cost model and cost model used for join reordering. Since Hive release 4.0.0 (HIVE-17503 / HIVE-21184).
Syntax: EXPLAIN [FORMATTED] CBO [COST|JOINCOST]
- COST option prints the plan and cost calculated using Calcite default cost model.
- JOINCOST option prints the plan and cost calculated using the cost model used for join reordering.
For example, we can execute the following statement:
EXPLAIN CBO
WITH customer_total_return AS
(SELECT sr_customer_sk AS ctr_customer_sk,
sr_store_sk AS ctr_store_sk,
SUM(SR_FEE) AS ctr_total_return
FROM store_returns, date_dim
WHERE sr_returned_date_sk = d_date_sk
AND d_year =2000
GROUP BY sr_customer_sk, sr_store_sk)
SELECT c_customer_id
FROM customer_total_return ctr1, store, customer
WHERE ctr1.ctr_total_return > (SELECT AVG(ctr_total_return)*1.2
FROM customer_total_return ctr2
WHERE ctr1.ctr_store_sk = ctr2.ctr_store_sk)
AND s_store_sk = ctr1.ctr_store_sk
AND s_state = 'NM'
AND ctr1.ctr_customer_sk = c_customer_sk
ORDER BY c_customer_id
LIMIT 100
The query will be optimized and Hive produces the following output:
CBO PLAN:
HiveSortLimit(sort0=[$0], dir0=[ASC], fetch=[100])
HiveProject(c_customer_id=[$1])
HiveJoin(condition=[AND(=($3, $7), >($4, $6))], joinType=[inner], algorithm=[none], cost=[not available])
HiveJoin(condition=[=($2, $0)], joinType=[inner], algorithm=[none], cost=[not available])
HiveProject(c_customer_sk=[$0], c_customer_id=[$1])
HiveFilter(condition=[IS NOT NULL($0)])
HiveTableScan(table=[[default, customer]], table:alias=[customer])
HiveJoin(condition=[=($3, $1)], joinType=[inner], algorithm=[none], cost=[not available])
HiveProject(sr_customer_sk=[$0], sr_store_sk=[$1], $f2=[$2])
HiveAggregate(group=[{1, 2}], agg#0=[sum($3)])
HiveJoin(condition=[=($0, $4)], joinType=[inner], algorithm=[none], cost=[not available])
HiveProject(sr_returned_date_sk=[$0], sr_customer_sk=[$3], sr_store_sk=[$7], sr_fee=[$14])
HiveFilter(condition=[AND(IS NOT NULL($0), IS NOT NULL($7), IS NOT NULL($3))])
HiveTableScan(table=[[default, store_returns]], table:alias=[store_returns])
HiveProject(d_date_sk=[$0])
HiveFilter(condition=[AND(=($6, 2000), IS NOT NULL($0))])
HiveTableScan(table=[[default, date_dim]], table:alias=[date_dim])
HiveProject(s_store_sk=[$0])
HiveFilter(condition=[AND(=($24, _UTF-16LE'NM'), IS NOT NULL($0))])
HiveTableScan(table=[[default, store]], table:alias=[store])
HiveProject(_o__c0=[*(/($1, $2), 1.2)], ctr_store_sk=[$0])
HiveAggregate(group=[{1}], agg#0=[sum($2)], agg#1=[count($2)])
HiveProject(sr_customer_sk=[$0], sr_store_sk=[$1], $f2=[$2])
HiveAggregate(group=[{1, 2}], agg#0=[sum($3)])
HiveJoin(condition=[=($0, $4)], joinType=[inner], algorithm=[none], cost=[not available])
HiveProject(sr_returned_date_sk=[$0], sr_customer_sk=[$3], sr_store_sk=[$7], sr_fee=[$14])
HiveFilter(condition=[AND(IS NOT NULL($0), IS NOT NULL($7))])
HiveTableScan(table=[[default, store_returns]], table:alias=[store_returns])
HiveProject(d_date_sk=[$0])
HiveFilter(condition=[AND(=($6, 2000), IS NOT NULL($0))])
HiveTableScan(table=[[default, date_dim]], table:alias=[date_dim])
In turn, we can execute the following command:
EXPLAIN CBO COST
WITH customer_total_return AS
(SELECT sr_customer_sk AS ctr_customer_sk,
sr_store_sk AS ctr_store_sk,
SUM(SR_FEE) AS ctr_total_return
FROM store_returns, date_dim
WHERE sr_returned_date_sk = d_date_sk
AND d_year =2000
GROUP BY sr_customer_sk, sr_store_sk)
SELECT c_customer_id
FROM customer_total_return ctr1, store, customer
WHERE ctr1.ctr_total_return > (SELECT AVG(ctr_total_return)*1.2
FROM customer_total_return ctr2
WHERE ctr1.ctr_store_sk = ctr2.ctr_store_sk)
AND s_store_sk = ctr1.ctr_store_sk
AND s_state = 'NM'
AND ctr1.ctr_customer_sk = c_customer_sk
ORDER BY c_customer_id
LIMIT 100
It will produce a similar plan, but the cost for each operator will be embedded next to the operator descriptors:
CBO PLAN:
HiveSortLimit(sort0=[$0], dir0=[ASC], fetch=[100]): rowcount = 100.0, cumulative cost = {2.395588892021712E26 rows, 1.197794434438787E26 cpu, 0.0 io}, id = 1683
HiveProject(c_customer_id=[$1]): rowcount = 1.1977944344387866E26, cumulative cost = {2.395588892021712E26 rows, 1.197794434438787E26 cpu, 0.0 io}, id = 1681
HiveJoin(condition=[AND(=($3, $7), >($4, $6))], joinType=[inner], algorithm=[none], cost=[not available]): rowcount = 1.1977944344387866E26, cumulative cost = {1.1977944575829254E26 rows, 4.160211553874922E10 cpu, 0.0 io}, id = 1679
HiveJoin(condition=[=($2, $0)], joinType=[inner], algorithm=[none], cost=[not available]): rowcount = 2.3144135067474273E18, cumulative cost = {2.3144137967122499E18 rows, 1.921860676139634E10 cpu, 0.0 io}, id = 1663
HiveProject(c_customer_sk=[$0], c_customer_id=[$1]): rowcount = 7.2E7, cumulative cost = {2.24E8 rows, 3.04000001E8 cpu, 0.0 io}, id = 1640
HiveFilter(condition=[IS NOT NULL($0)]): rowcount = 7.2E7, cumulative cost = {1.52E8 rows, 1.60000001E8 cpu, 0.0 io}, id = 1638
HiveTableScan(table=[[default, customer]], table:alias=[customer]): rowcount = 8.0E7, cumulative cost = {8.0E7 rows, 8.0000001E7 cpu, 0.0 io}, id = 1055
HiveJoin(condition=[=($3, $1)], joinType=[inner], algorithm=[none], cost=[not available]): rowcount = 2.1429754692105807E11, cumulative cost = {2.897408225471977E11 rows, 1.891460676039634E10 cpu, 0.0 io}, id = 1661
HiveProject(sr_customer_sk=[$0], sr_store_sk=[$1], $f2=[$2]): rowcount = 6.210443022113779E9, cumulative cost = {7.544327346205959E10 rows, 1.891460312135634E10 cpu, 0.0 io}, id = 1685
HiveAggregate(group=[{1, 2}], agg#0=[sum($3)]): rowcount = 6.210443022113779E9, cumulative cost = {6.92328304399458E10 rows, 2.8327405501500005E8 cpu, 0.0 io}, id = 1654
HiveJoin(condition=[=($0, $4)], joinType=[inner], algorithm=[none], cost=[not available]): rowcount = 6.2104430221137794E10, cumulative cost = {6.2246082040067795E10 rows, 2.8327405501500005E8 cpu, 0.0 io}, id = 1652
HiveProject(sr_returned_date_sk=[$0], sr_customer_sk=[$3], sr_store_sk=[$7], sr_fee=[$14]): rowcount = 4.198394835000001E7, cumulative cost = {1.4155904670000002E8 rows, 2.8311809440000004E8 cpu, 0.0 io}, id = 1645
HiveFilter(condition=[AND(IS NOT NULL($0), IS NOT NULL($7), IS NOT NULL($3))]): rowcount = 4.198394835000001E7, cumulative cost = {9.957509835000001E7 rows, 1.15182301E8 cpu, 0.0 io}, id = 1643
HiveTableScan(table=[[default, store_returns]], table:alias=[store_returns]): rowcount = 5.759115E7, cumulative cost = {5.759115E7 rows, 5.7591151E7 cpu, 0.0 io}, id = 1040
HiveProject(d_date_sk=[$0]): rowcount = 9861.615, cumulative cost = {92772.23000000001 rows, 155960.615 cpu, 0.0 io}, id = 1650
HiveFilter(condition=[AND(=($6, 2000), IS NOT NULL($0))]): rowcount = 9861.615, cumulative cost = {82910.615 rows, 146099.0 cpu, 0.0 io}, id = 1648
HiveTableScan(table=[[default, date_dim]], table:alias=[date_dim]): rowcount = 73049.0, cumulative cost = {73049.0 rows, 73050.0 cpu, 0.0 io}, id = 1043
HiveProject(s_store_sk=[$0]): rowcount = 230.04000000000002, cumulative cost = {2164.08 rows, 3639.04 cpu, 0.0 io}, id = 1659
HiveFilter(condition=[AND(=($24, _UTF-16LE'NM'), IS NOT NULL($0))]): rowcount = 230.04000000000002, cumulative cost = {1934.04 rows, 3409.0 cpu, 0.0 io}, id = 1657
HiveTableScan(table=[[default, store]], table:alias=[store]): rowcount = 1704.0, cumulative cost = {1704.0 rows, 1705.0 cpu, 0.0 io}, id = 1050
HiveProject(_o__c0=[*(/($1, $2), 1.2)], ctr_store_sk=[$0]): rowcount = 6.900492246793088E8, cumulative cost = {8.537206083312463E10 rows, 2.2383508777352882E10 cpu, 0.0 io}, id = 1677
HiveAggregate(group=[{1}], agg#0=[sum($2)], agg#1=[count($2)]): rowcount = 6.900492246793088E8, cumulative cost = {8.468201160844533E10 rows, 2.1003410327994267E10 cpu, 0.0 io}, id = 1675
HiveProject(sr_customer_sk=[$0], sr_store_sk=[$1], $f2=[$2]): rowcount = 6.900492246793088E9, cumulative cost = {8.381945007759619E10 rows, 2.1003410327994267E10 cpu, 0.0 io}, id = 1686
HiveAggregate(group=[{1, 2}], agg#0=[sum($3)]): rowcount = 6.900492246793088E9, cumulative cost = {7.69189578308031E10 rows, 3.01933587615E8 cpu, 0.0 io}, id = 1673
HiveJoin(condition=[=($0, $4)], joinType=[inner], algorithm=[none], cost=[not available]): rowcount = 6.900492246793088E10, cumulative cost = {6.915590405316087E10 rows, 3.01933587615E8 cpu, 0.0 io}, id = 1671
HiveProject(sr_returned_date_sk=[$0], sr_customer_sk=[$3], sr_store_sk=[$7], sr_fee=[$14]): rowcount = 4.66488315E7, cumulative cost = {1.50888813E8 rows, 3.01777627E8 cpu, 0.0 io}, id = 1667
HiveFilter(condition=[AND(IS NOT NULL($0), IS NOT NULL($7))]): rowcount = 4.66488315E7, cumulative cost = {1.042399815E8 rows, 1.15182301E8 cpu, 0.0 io}, id = 1665
HiveTableScan(table=[[default, store_returns]], table:alias=[store_returns]): rowcount = 5.759115E7, cumulative cost = {5.759115E7 rows, 5.7591151E7 cpu, 0.0 io}, id = 1040
HiveProject(d_date_sk=[$0]): rowcount = 9861.615, cumulative cost = {92772.23000000001 rows, 155960.615 cpu, 0.0 io}, id = 1650
HiveFilter(condition=[AND(=($6, 2000), IS NOT NULL($0))]): rowcount = 9861.615, cumulative cost = {82910.615 rows, 146099.0 cpu, 0.0 io}, id = 1648
HiveTableScan(table=[[default, date_dim]], table:alias=[date_dim]): rowcount = 73049.0, cumulative cost = {73049.0 rows, 73050.0 cpu, 0.0 io}, id = 1043
The AST Clause
Outputs the query's Abstract Syntax Tree.
Example:
EXPLAIN AST FROM src INSERT OVERWRITE TABLE dest_g1 SELECT src.key, sum(substr(src.value,4)) GROUP BY src.key;
Outputs:
ABSTRACT SYNTAX TREE: (TOK_QUERY (TOK_FROM (TOK_TABREF src)) (TOK_INSERT (TOK_DESTINATION (TOK_TAB dest_g1)) (TOK_SELECT (TOK_SELEXPR (TOK_COLREF src key)) (TOK_SELEXPR (TOK_FUNCTION sum (TOK_FUNCTION substr (TOK_COLREF src value) 4)))) (TOK_GROUPBY (TOK_COLREF src key))))
The DEPENDENCY Clause
The use of DEPENDENCY in the EXPLAIN statement produces extra information about the inputs in the plan. It shows various attributes for the inputs. For example, for a query like:
EXPLAIN DEPENDENCY SELECT key, count(1) FROM srcpart WHERE ds IS NOT NULL GROUP BY key
the following output is produced:
{"input_partitions":[{"partitionName":"default<at:var at:name="srcpart" />ds=2008-04-08/hr=11"},{"partitionName":"default<at:var at:name="srcpart" />ds=2008-04-08/hr=12"},{"partitionName":"default<at:var at:name="srcpart" />ds=2008-04-09/hr=11"},{"partitionName":"default<at:var at:name="srcpart" />ds=2008-04-09/hr=12"}],"input_tables":[{"tablename":"default@srcpart","tabletype":"MANAGED_TABLE"}]}
The inputs contain both the tables and the partitions. Note that the table is present even if none of the partitions is accessed in the query.
The dependencies show the parents in case a table is accessed via a view. Consider the following queries:
CREATE VIEW V1 AS SELECT key, value from src; EXPLAIN DEPENDENCY SELECT * FROM V1;
The following output is produced:
{"input_partitions":[],"input_tables":[{"tablename":"default@v1","tabletype":"VIRTUAL_VIEW"},{"tablename":"default@src","tabletype":"MANAGED_TABLE","tableParents":"[default@v1]"}]}
As above, the inputs contain the view V1 and the table 'src' that the view V1 refers to.
All the outputs are shown if a table is being accessed via multiple parents.
CREATE VIEW V2 AS SELECT ds, key, value FROM srcpart WHERE ds IS NOT NULL; CREATE VIEW V4 AS SELECT src1.key, src2.value as value1, src3.value as value2 FROM V1 src1 JOIN V2 src2 on src1.key = src2.key JOIN src src3 ON src2.key = src3.key; EXPLAIN DEPENDENCY SELECT * FROM V4;
The following output is produced.
{"input_partitions":[{"partitionParents":"[default@v2]","partitionName":"default<at:var at:name="srcpart" />ds=2008-04-08/hr=11"},{"partitionParents":"[default@v2]","partitionName":"default<at:var at:name="srcpart" />ds=2008-04-08/hr=12"},{"partitionParents":"[default@v2]","partitionName":"default<at:var at:name="srcpart" />ds=2008-04-09/hr=11"},{"partitionParents":"[default@v2]","partitionName":"default<at:var at:name="srcpart" />ds=2008-04-09/hr=12"}],"input_tables":[{"tablename":"default@v4","tabletype":"VIRTUAL_VIEW"},{"tablename":"default@v2","tabletype":"VIRTUAL_VIEW","tableParents":"[default@v4]"},{"tablename":"default@v1","tabletype":"VIRTUAL_VIEW","tableParents":"[default@v4]"},{"tablename":"default@src","tabletype":"MANAGED_TABLE","tableParents":"[default@v4, default@v1]"},{"tablename":"default@srcpart","tabletype":"MANAGED_TABLE","tableParents":"[default@v2]"}]}
As can be seen, src is being accessed via parents v1 and v4.
The AUTHORIZATION Clause
The use of AUTHORIZATION in the EXPLAIN statement shows all entities needed to be authorized to execute the query and authorization failures if any exist. For example, for a query like:
EXPLAIN AUTHORIZATION SELECT * FROM src JOIN srcpart;
the following output is produced:
INPUTS: default@srcpart default@src default@srcpart@ds=2008-04-08/hr=11 default@srcpart@ds=2008-04-08/hr=12 default@srcpart@ds=2008-04-09/hr=11 default@srcpart@ds=2008-04-09/hr=12 OUTPUTS: hdfs://localhost:9000/tmp/.../-mr-10000 CURRENT_USER: navis OPERATION: QUERY AUTHORIZATION_FAILURES: Permission denied: Principal [name=navis, type=USER] does not have following privileges for operation QUERY [[SELECT] on Object [type=TABLE_OR_VIEW, name=default.src], [SELECT] on Object [type=TABLE_OR_VIEW, name=default.srcpart]]
With the FORMATTED keyword, it will be returned in JSON format.
"OUTPUTS":["hdfs://localhost:9000/tmp/.../-mr-10000"],"INPUTS":["default@srcpart","default@src","default@srcpart@ds=2008-04-08/hr=11","default@srcpart@ds=2008-04-08/hr=12","default@srcpart@ds=2008-04-09/hr=11","default@srcpart@ds=2008-04-09/hr=12"],"OPERATION":"QUERY","CURRENT_USER":"navis","AUTHORIZATION_FAILURES":["Permission denied: Principal [name=navis, type=USER] does not have following privileges for operation QUERY [[SELECT] on Object [type=TABLE_OR_VIEW, name=default.src], [SELECT] on Object [type=TABLE_OR_VIEW, name=default.srcpart]]"]}
The LOCKS Clause
This is useful to understand what locks the system will acquire to run the specified query. Since Hive release 3.2.0 (HIVE-17683).
For example
EXPLAIN LOCKS UPDATE target SET b = 1 WHERE p IN (SELECT t.q1 FROM source t WHERE t.a1=5)
Will produce output like this.
LOCK INFORMATION: default.source -> SHARED_READ default.target.p=1/q=2 -> SHARED_READ default.target.p=1/q=3 -> SHARED_READ default.target.p=2/q=2 -> SHARED_READ default.target.p=2/q=2 -> SHARED_WRITE default.target.p=1/q=3 -> SHARED_WRITE default.target.p=1/q=2 -> SHARED_WRITE
EXPLAIN FORMATTED LOCKS <sql>
is also supported which will produce JSON encoded output.
The VECTORIZATION Clause
Adds detail to the EXPLAIN output showing why Map and Reduce work is not vectorized. Since Hive release 2.3.0 (HIVE-11394).
Syntax: EXPLAIN VECTORIZATION [ONLY] [SUMMARY|OPERATOR|EXPRESSION|DETAIL]
- ONLY option suppresses most non-vectorization elements.
- SUMMARY (default) shows vectorization information for the PLAN (is vectorization enabled) and a summary of Map and Reduce work.
- OPERATOR shows vectorization information for operators. E.g. Filter Vectorization. Includes all information of SUMMARY.
- EXPRESSION shows vectorization information for expressions. E.g. predicateExpression. Includes all information of SUMMARY and OPERATOR.
- DETAIL shows detail-level vectorization information. It includes all information of SUMMARY, OPERATOR, and EXPRESSION.
The optional clause defaults are not ONLY and SUMMARY.
See HIVE-11394 for more details and examples.
The ANALYZE Clause
Annotates the plan with actual row counts. Since in Hive 2.2.0 (HIVE-14362)
Format is: (estimated row count) / (actual row count)
Example:
For the below tablescan; the estimation was 500 rows; but actually the scan only yielded 13 rows.
[...]
TableScan [TS_13] (rows=500/13 width=178)
Output:["key","value"]
[...]
User-level Explain Output
Since HIVE-8600 in Hive 1.1.0, we support a user-level explain extended output for any query at the log4j INFO level after set hive.log.explain.output=true (default is false).
Since HIVE-18469 in Hive 3.1.0, the user-level explain extended output for any query will be shown in the WebUI / Drilldown / Query Plan after set hive.server2.webui.explain.output=true (default is false).
Since HIVE-9780 in Hive 1.2.0, we support a user-level explain for Hive on Tez users. After set hive.explain.user=true (default is false) if the following query is sent, the user can see a much more clearly readable tree of operations.
Since HIVE-11133 in Hive 3.0.0, we support a user-level explain for Hive on Spark users. A separate configuration is used for Hive-on-Spark, hive.spark.explain.user which is set to false by default.
EXPLAIN select sum(hash(key)), sum(hash(value)) from src_orc_merge_test_part where ds='2012-01-03' and ts='2012-01-03+14:46:31'
Plan optimized by CBO.
Vertex dependency in root stage
Reducer 2 <- Map 1 (SIMPLE_EDGE)
Stage-0
Fetch Operator
limit:-1
Stage-1
Reducer 2
File Output Operator [FS_8]
compressed:false
Statistics:Num rows: 1 Data size: 16 Basic stats: COMPLETE Column stats: NONE
table:{"serde:":"org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe","input format:":"org.apache.hadoop.mapred.TextInputFormat","output format:":"org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat"}
Group By Operator [GBY_6]
| aggregations:["sum(VALUE._col0)","sum(VALUE._col1)"]
| outputColumnNames:["_col0","_col1"]
| Statistics:Num rows: 1 Data size: 16 Basic stats: COMPLETE Column stats: NONE
|<-Map 1 [SIMPLE_EDGE]
Reduce Output Operator [RS_5]
sort order:
Statistics:Num rows: 1 Data size: 16 Basic stats: COMPLETE Column stats: NONE
value expressions:_col0 (type: bigint), _col1 (type: bigint)
Group By Operator [GBY_4]
aggregations:["sum(_col0)","sum(_col1)"]
outputColumnNames:["_col0","_col1"]
Statistics:Num rows: 1 Data size: 16 Basic stats: COMPLETE Column stats: NONE
Select Operator [SEL_2]
outputColumnNames:["_col0","_col1"]
Statistics:Num rows: 500 Data size: 47000 Basic stats: COMPLETE Column stats: NONE
TableScan [TS_0]
alias:src_orc_merge_test_part
Statistics:Num rows: 500 Data size: 47000 Basic stats: COMPLETE Column stats: NONE