- StarRocks
- Introduction to StarRocks
- Quick Start
- Deployment
- Deployment overview
- Prepare
- Deploy
- Deploy shared-nothing StarRocks
- Deploy and use shared-data StarRocks
- Manage
- Table Design
- Understand StarRocks table design
- Table types
- Data distribution
- Data compression
- Sort keys and prefix indexes
- Data Loading
- Concepts
- Overview of data loading
- Load data from a local file system or a streaming data source using HTTP PUT
- Load data from HDFS
- Load data from cloud storage
- Load data from Apache Kafka®
- Continuously load data from Apache Kafka®
- Load data from Apache Sparkâ„¢
- Load data using INSERT
- Load data using Stream Load transaction interface
- Realtime synchronization from MySQL
- Continuously load data from Apache Flink®
- Change data through loading
- Transform data at loading
- Data Unloading
- Query Data Lakes
- Query Acceleration
- Gather CBO statistics
- Synchronous materialized views
- Asynchronous materialized views
- Colocate Join
- Lateral Join
- Query Cache
- Index
- Computing the Number of Distinct Values
- Sorted streaming aggregate
- Integrations
- Administration
- Management
- Data recovery
- User Privilege and Authentication
- Performance Tuning
- Reference
- SQL Reference
- User Account Management
- Cluster Management
- ADD SQLBLACKLIST
- ADMIN CANCEL REPAIR TABLE
- ADMIN CHECK TABLET
- ADMIN REPAIR TABLE
- ADMIN SET CONFIG
- ADMIN SET REPLICA STATUS
- ADMIN SHOW CONFIG
- ADMIN SHOW REPLICA DISTRIBUTION
- ADMIN SHOW REPLICA STATUS
- ALTER RESOURCE GROUP
- ALTER STORAGE VOLUME
- ALTER SYSTEM
- CANCEL DECOMMISSION
- CREATE FILE
- CREATE RESOURCE GROUP
- CREATE STORAGE VOLUME
- DELETE SQLBLACKLIST
- DESC STORAGE VOLUME
- DROP FILE
- DROP RESOURCE GROUP
- DROP STORAGE VOLUME
- EXPLAIN
- INSTALL PLUGIN
- KILL
- SET
- SET DEFAULT STORAGE VOLUME
- SHOW BACKENDS
- SHOW BROKER
- SHOW COMPUTE NODES
- SHOW FILE
- SHOW FRONTENDS
- SHOW FULL COLUMNS
- SHOW INDEX
- SHOW PLUGINS
- SHOW PROC
- SHOW PROCESSLIST
- SHOW RESOURCE GROUP
- SHOW SQLBLACKLIST
- SHOW STORAGE VOLUMES
- SHOW TABLE STATUS
- SHOW VARIABLES
- UNINSTALL PLUGIN
- DDL
- ALTER DATABASE
- ALTER MATERIALIZED VIEW
- ALTER TABLE
- ALTER VIEW
- ALTER RESOURCE
- ANALYZE TABLE
- BACKUP
- CANCEL ALTER TABLE
- CANCEL BACKUP
- CANCEL RESTORE
- CREATE ANALYZE
- CREATE DATABASE
- CREATE EXTERNAL CATALOG
- CREATE FUNCTION
- CREATE INDEX
- CREATE MATERIALIZED VIEW
- CREATE REPOSITORY
- CREATE RESOURCE
- CREATE TABLE
- CREATE TABLE AS SELECT
- CREATE TABLE LIKE
- CREATE VIEW
- DROP ANALYZE
- DROP CATALOG
- DROP DATABASE
- DROP FUNCTION
- DROP INDEX
- DROP MATERIALIZED VIEW
- DROP REPOSITORY
- DROP RESOURCE
- DROP STATS
- DROP TABLE
- DROP VIEW
- HLL
- KILL ANALYZE
- RECOVER
- REFRESH EXTERNAL TABLE
- RESTORE
- SET CATALOG
- SHOW ANALYZE JOB
- SHOW ANALYZE STATUS
- SHOW FUNCTION
- SHOW META
- SHOW RESOURCES
- TRUNCATE TABLE
- USE
- DML
- ALTER LOAD
- ALTER ROUTINE LOAD
- BROKER LOAD
- CANCEL LOAD
- CANCEL EXPORT
- CANCEL REFRESH MATERIALIZED VIEW
- CREATE ROUTINE LOAD
- DELETE
- DROP TASK
- EXPORT
- GROUP BY
- INSERT
- PAUSE ROUTINE LOAD
- REFRESH MATERIALIZED VIEW
- RESUME ROUTINE LOAD
- SELECT
- SHOW ALTER TABLE
- SHOW ALTER MATERIALIZED VIEW
- SHOW BACKUP
- SHOW CATALOGS
- SHOW CREATE CATALOG
- SHOW CREATE DATABASE
- SHOW CREATE MATERIALIZED VIEW
- SHOW CREATE TABLE
- SHOW CREATE VIEW
- SHOW DATA
- SHOW DATABASES
- SHOW DELETE
- SHOW DYNAMIC PARTITION TABLES
- SHOW EXPORT
- SHOW LOAD
- SHOW MATERIALIZED VIEWS
- SHOW PARTITIONS
- SHOW PROPERTY
- SHOW REPOSITORIES
- SHOW RESTORE
- SHOW ROUTINE LOAD
- SHOW ROUTINE LOAD TASK
- SHOW SNAPSHOT
- SHOW TABLES
- SHOW TABLET
- SHOW TRANSACTION
- SPARK LOAD
- STOP ROUTINE LOAD
- STREAM LOAD
- SUBMIT TASK
- UPDATE
- Auxiliary Commands
- Data Types
- Keywords
- Function Reference
- Function list
- Java UDFs
- Window functions
- Lambda expression
- Aggregate Functions
- any_value
- approx_count_distinct
- array_agg
- avg
- bitmap
- bitmap_agg
- count
- corr
- covar_pop
- covar_samp
- group_concat
- grouping
- grouping_id
- hll_empty
- hll_hash
- hll_raw_agg
- hll_union
- hll_union_agg
- max
- max_by
- min
- min_by
- multi_distinct_sum
- multi_distinct_count
- percentile_approx
- percentile_cont
- percentile_disc
- retention
- stddev
- stddev_samp
- sum
- variance, variance_pop, var_pop
- var_samp
- window_funnel
- Array Functions
- all_match
- any_match
- array_agg
- array_append
- array_avg
- array_concat
- array_contains
- array_contains_all
- array_cum_sum
- array_difference
- array_distinct
- array_filter
- array_generate
- array_intersect
- array_join
- array_length
- array_map
- array_max
- array_min
- array_position
- array_remove
- array_slice
- array_sort
- array_sortby
- array_sum
- arrays_overlap
- array_to_bitmap
- cardinality
- element_at
- reverse
- unnest
- Bit Functions
- Bitmap Functions
- base64_to_bitmap
- bitmap_agg
- bitmap_and
- bitmap_andnot
- bitmap_contains
- bitmap_count
- bitmap_from_string
- bitmap_empty
- bitmap_has_any
- bitmap_hash
- bitmap_intersect
- bitmap_max
- bitmap_min
- bitmap_or
- bitmap_remove
- bitmap_subset_in_range
- bitmap_subset_limit
- bitmap_to_array
- bitmap_to_base64
- bitmap_to_string
- bitmap_union
- bitmap_union_count
- bitmap_union_int
- bitmap_xor
- intersect_count
- sub_bitmap
- to_bitmap
- JSON Functions
- Overview of JSON functions and operators
- JSON operators
- JSON constructor functions
- JSON query and processing functions
- Map Functions
- Binary Functions
- Conditional Functions
- Cryptographic Functions
- Date Functions
- add_months
- adddate
- convert_tz
- current_date
- current_time
- current_timestamp
- date
- date_add
- date_diff
- date_format
- date_slice
- date_sub, subdate
- date_trunc
- datediff
- day
- dayname
- dayofmonth
- dayofweek
- dayofyear
- days_add
- days_diff
- days_sub
- from_days
- from_unixtime
- hour
- hours_add
- hours_diff
- hours_sub
- last_day
- makedate
- microseconds_add
- microseconds_sub
- minute
- minutes_add
- minutes_diff
- minutes_sub
- month
- monthname
- months_add
- months_diff
- months_sub
- next_day
- now
- previous_day
- quarter
- second
- seconds_add
- seconds_diff
- seconds_sub
- str_to_date
- str2date
- time_slice
- time_to_sec
- timediff
- timestamp
- timestampadd
- timestampdiff
- to_date
- to_days
- unix_timestamp
- utc_timestamp
- week
- week_iso
- weekofyear
- weeks_add
- day_of_week_iso
- weeks_diff
- weeks_sub
- year
- years_add
- years_diff
- years_sub
- Geographic Functions
- Math Functions
- String Functions
- append_trailing_char_if_absent
- ascii
- char
- char_length
- character_length
- concat
- concat_ws
- ends_with
- find_in_set
- group_concat
- hex
- hex_decode_binary
- hex_decode_string
- instr
- lcase
- left
- length
- locate
- lower
- lpad
- ltrim
- money_format
- null_or_empty
- parse_url
- repeat
- replace
- reverse
- right
- rpad
- rtrim
- space
- split
- split_part
- starts_with
- strleft
- strright
- str_to_map
- substring
- trim
- ucase
- unhex
- upper
- url_decode
- url_encode
- Pattern Matching Functions
- Percentile Functions
- Scalar Functions
- Struct Functions
- Table Functions
- Utility Functions
- cast function
- hash function
- AUTO_INCREMENT
- Generated columns
- System variables
- User-defined variables
- Error code
- System limits
- AWS IAM policies
- SQL Reference
- FAQ
- Benchmark
- Ecosystem Release Notes
- Developers
- Contribute to StarRocks
- Code Style Guides
- Use the debuginfo file for debugging
- Development Environment
- Trace Tools
query_dump interface
This topic describes how to use the query_dump interface to obtain the details of an SQL query and its related information.
If you encounter any of the following issues when executing SQL queries with StarRocks, you can use query_dump to obtain the SQL details and send the information to StarRocks technical support for troubleshooting:
Unknown Error
is returned when you execute an SQL query or EXPLAIN.- An error message or exception is returned when you execute an SQL query.
- Executing an SQL query is not as efficient as expected, or the execution plan can be optimized (for example, partitions can be pruned or Join order can be adjusted).
Function overview
The query_dump interface returns the information that FE relies on when executing the SQL, including:
- Query statement
- Table creation statement
- Session variables
- Number of BEs
- Statistics information (Min, Max values in a column)
- Exception
- Explain costs info
To ensure data privacy, we desensitize the meta information such as database names, table names, column names, etc. We also utilize the desensitized metadata to rewrite the query statements. Meta information desensitization is enabled by default. If an exception occurs during the desensitization process, use the original info directly. If desensitization needs to be bypassed, you can add the "mock=false" parameter in the HTTP URI.
Syntax
HTTP Post
fe_host:fe_http_port/api/query_dump?db=${database}&mock=${value} post_data=${Query}
wget --user=${username} --password=${password} --post-file ${query_file} "http://${fe_host}:${fe_http_port}/api/query_dump?db=${database}&mock={value}" -O ${dump_file}
Parameter description:
- query_file: the file containing the query
- dump_file: the output file
- db: the database where the SQL query is executed. The
db
parameter is optional if the query includesuse db
. Otherwise, it must be specified. - mock: turn on/off the desensitization process.
Example
command:
wget --user=root --password=123 --post-file query_file "http://127.0.0.1:8030/api/query_dump?db=tpch&mock=false" -O dump_file
Return data:
Data is returned in JSON format.
{
"statement": "select\n l_returnflag,\n l_linestatus,\n sum(l_quantity) as sum_qty,\n sum(l_extendedprice) as sum_base_price,\n sum(l_extendedprice * (1 - l_discount)) as sum_disc_price,\n sum(l_extendedprice * (1 - l_discount) * (1 + l_tax)) as sum_charge,\n avg(l_quantity) as avg_qty,\n avg(l_extendedprice) as avg_price,\n avg(l_discount) as avg_disc,\n count(*) as count_order\nfrom\n lineitem\nwhere\n l_shipdate <= date '1998-12-01'\ngroup by\n l_returnflag,\n l_linestatus\norder by\n l_returnflag,\n l_linestatus ;\n\n",
"table_meta": {
"tpch.lineitem": "CREATE TABLE `lineitem` (\n `L_ORDERKEY` int(11) NOT NULL COMMENT \"\",\n `L_PARTKEY` int(11) NOT NULL COMMENT \"\",\n `L_SUPPKEY` int(11) NOT NULL COMMENT \"\",\n `L_LINENUMBER` int(11) NOT NULL COMMENT \"\",\n `L_QUANTITY` double NOT NULL COMMENT \"\",\n `L_EXTENDEDPRICE` double NOT NULL COMMENT \"\",\n `L_DISCOUNT` double NOT NULL COMMENT \"\",\n `L_TAX` double NOT NULL COMMENT \"\",\n `L_RETURNFLAG` char(1) NOT NULL COMMENT \"\",\n `L_LINESTATUS` char(1) NOT NULL COMMENT \"\",\n `L_SHIPDATE` date NOT NULL COMMENT \"\",\n `L_COMMITDATE` date NOT NULL COMMENT \"\",\n `L_RECEIPTDATE` date NOT NULL COMMENT \"\",\n `L_SHIPINSTRUCT` char(25) NOT NULL COMMENT \"\",\n `L_SHIPMODE` char(10) NOT NULL COMMENT \"\",\n `L_COMMENT` varchar(44) NOT NULL COMMENT \"\",\n `PAD` char(1) NOT NULL COMMENT \"\"\n) ENGINE=OLAP \nDUPLICATE KEY(`L_ORDERKEY`)\nCOMMENT \"OLAP\"\nDISTRIBUTED BY HASH(`L_ORDERKEY`) BUCKETS 20 \nPROPERTIES (\n\"replication_num\" = \"1\",\n\"in_memory\" = \"false\",\n\"enable_persistent_index\" = \"false\",\n\"replicated_storage\" = \"true\",\n\"compression\" = \"LZ4\"\n);"
},
"table_row_count": {
"tpch.lineitem": {
"lineitem": 3
}
},
"column_statistics": {
"tpch.lineitem": {
"L_TAX": "[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE",
"L_SHIPDATE": "[1.6094304E9, 1.6094304E9, 0.0, 4.0, 1.0] ESTIMATE",
"L_EXTENDEDPRICE": "[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE",
"L_DISCOUNT": "[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE",
"L_RETURNFLAG": "[-Infinity, Infinity, 0.0, 1.0, 1.0] ESTIMATE",
"L_LINESTATUS": "[-Infinity, Infinity, 0.0, 1.0, 1.0] ESTIMATE",
"L_QUANTITY": "[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE"
}
},
"explain_info": "PLAN FRAGMENT 0(F02)\n Output Exprs:9: L_RETURNFLAG | 10: L_LINESTATUS | 20: sum | 21: sum | 22: sum | 23: sum | 24: avg | 25: avg | 26: avg | 27: count\n Input Partition: UNPARTITIONED\n RESULT SINK\n\n 6:MERGING-EXCHANGE\n distribution type: GATHER\n cardinality: 1\n column statistics: \n * L_RETURNFLAG-->[-Infinity, Infinity, 0.0, 1.0, 1.0] ESTIMATE\n * L_LINESTATUS-->[-Infinity, Infinity, 0.0, 1.0, 1.0] ESTIMATE\n * sum-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n * sum-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n * sum-->[0.0, 0.0, 0.0, 8.0, 1.0] ESTIMATE\n * sum-->[0.0, 0.0, 0.0, 8.0, 1.0] ESTIMATE\n * avg-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n * avg-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n * avg-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n * count-->[0.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n\nPLAN FRAGMENT 1(F01)\n\n Input Partition: HASH_PARTITIONED: 9: L_RETURNFLAG, 10: L_LINESTATUS\n OutPut Partition: UNPARTITIONED\n OutPut Exchange Id: 06\n\n 5:SORT\n | order by: [9, VARCHAR, false] ASC, [10, VARCHAR, false] ASC\n | offset: 0\n | cardinality: 1\n | column statistics: \n | * L_RETURNFLAG-->[-Infinity, Infinity, 0.0, 1.0, 1.0] ESTIMATE\n | * L_LINESTATUS-->[-Infinity, Infinity, 0.0, 1.0, 1.0] ESTIMATE\n | * sum-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | * sum-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | * sum-->[0.0, 0.0, 0.0, 8.0, 1.0] ESTIMATE\n | * sum-->[0.0, 0.0, 0.0, 8.0, 1.0] ESTIMATE\n | * avg-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | * avg-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | * avg-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | * count-->[0.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | \n 4:AGGREGATE (merge finalize)\n | aggregate: sum[([20: sum, DOUBLE, true]); args: DOUBLE; result: DOUBLE; args nullable: true; result nullable: true], sum[([21: sum, DOUBLE, true]); args: DOUBLE; result: DOUBLE; args nullable: true; result nullable: true], sum[([22: sum, DOUBLE, true]); args: DOUBLE; result: DOUBLE; args nullable: true; result nullable: true], sum[([23: sum, DOUBLE, true]); args: DOUBLE; result: DOUBLE; args nullable: true; result nullable: true], avg[([24: avg, VARBINARY, true]); args: DOUBLE; result: DOUBLE; args nullable: true; result nullable: true], avg[([25: avg, VARBINARY, true]); args: DOUBLE; result: DOUBLE; args nullable: true; result nullable: true], avg[([26: avg, VARBINARY, true]); args: DOUBLE; result: DOUBLE; args nullable: true; result nullable: true], count[([27: count, BIGINT, false]); args: ; result: BIGINT; args nullable: true; result nullable: false]\n | group by: [9: L_RETURNFLAG, VARCHAR, false], [10: L_LINESTATUS, VARCHAR, false]\n | cardinality: 1\n | column statistics: \n | * L_RETURNFLAG-->[-Infinity, Infinity, 0.0, 1.0, 1.0] ESTIMATE\n | * L_LINESTATUS-->[-Infinity, Infinity, 0.0, 1.0, 1.0] ESTIMATE\n | * sum-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | * sum-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | * sum-->[0.0, 0.0, 0.0, 8.0, 1.0] ESTIMATE\n | * sum-->[0.0, 0.0, 0.0, 8.0, 1.0] ESTIMATE\n | * avg-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | * avg-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | * avg-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | * count-->[0.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | \n 3:EXCHANGE\n distribution type: SHUFFLE\n partition exprs: [9: L_RETURNFLAG, VARCHAR, false], [10: L_LINESTATUS, VARCHAR, false]\n cardinality: 1\n\nPLAN FRAGMENT 2(F00)\n\n Input Partition: RANDOM\n OutPut Partition: HASH_PARTITIONED: 9: L_RETURNFLAG, 10: L_LINESTATUS\n OutPut Exchange Id: 03\n\n 2:AGGREGATE (update serialize)\n | STREAMING\n | aggregate: sum[([5: L_QUANTITY, DOUBLE, false]); args: DOUBLE; result: DOUBLE; args nullable: false; result nullable: true], sum[([6: L_EXTENDEDPRICE, DOUBLE, false]); args: DOUBLE; result: DOUBLE; args nullable: false; result nullable: true], sum[([18: expr, DOUBLE, false]); args: DOUBLE; result: DOUBLE; args nullable: false; result nullable: true], sum[([19: expr, DOUBLE, false]); args: DOUBLE; result: DOUBLE; args nullable: false; result nullable: true], avg[([5: L_QUANTITY, DOUBLE, false]); args: DOUBLE; result: VARBINARY; args nullable: false; result nullable: true], avg[([6: L_EXTENDEDPRICE, DOUBLE, false]); args: DOUBLE; result: VARBINARY; args nullable: false; result nullable: true], avg[([7: L_DISCOUNT, DOUBLE, false]); args: DOUBLE; result: VARBINARY; args nullable: false; result nullable: true], count[(*); args: ; result: BIGINT; args nullable: false; result nullable: false]\n | group by: [9: L_RETURNFLAG, VARCHAR, false], [10: L_LINESTATUS, VARCHAR, false]\n | cardinality: 1\n | column statistics: \n | * L_RETURNFLAG-->[-Infinity, Infinity, 0.0, 1.0, 1.0] ESTIMATE\n | * L_LINESTATUS-->[-Infinity, Infinity, 0.0, 1.0, 1.0] ESTIMATE\n | * sum-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | * sum-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | * sum-->[0.0, 0.0, 0.0, 8.0, 1.0] ESTIMATE\n | * sum-->[0.0, 0.0, 0.0, 8.0, 1.0] ESTIMATE\n | * avg-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | * avg-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | * avg-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | * count-->[0.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | \n 1:Project\n | output columns:\n | 5 <-> [5: L_QUANTITY, DOUBLE, false]\n | 6 <-> [6: L_EXTENDEDPRICE, DOUBLE, false]\n | 7 <-> [7: L_DISCOUNT, DOUBLE, false]\n | 9 <-> [9: L_RETURNFLAG, CHAR, false]\n | 10 <-> [10: L_LINESTATUS, CHAR, false]\n | 18 <-> [29: multiply, DOUBLE, false]\n | 19 <-> [29: multiply, DOUBLE, false] * 1.0 + [8: L_TAX, DOUBLE, false]\n | common expressions:\n | 28 <-> 1.0 - [7: L_DISCOUNT, DOUBLE, false]\n | 29 <-> [6: L_EXTENDEDPRICE, DOUBLE, false] * [28: subtract, DOUBLE, false]\n | cardinality: 1\n | column statistics: \n | * L_QUANTITY-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | * L_EXTENDEDPRICE-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | * L_DISCOUNT-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | * L_RETURNFLAG-->[-Infinity, Infinity, 0.0, 1.0, 1.0] ESTIMATE\n | * L_LINESTATUS-->[-Infinity, Infinity, 0.0, 1.0, 1.0] ESTIMATE\n | * expr-->[0.0, 0.0, 0.0, 8.0, 1.0] ESTIMATE\n | * expr-->[0.0, 0.0, 0.0, 8.0, 1.0] ESTIMATE\n | \n 0:OlapScanNode\n table: lineitem, rollup: lineitem\n preAggregation: on\n Predicates: [11: L_SHIPDATE, DATE, false] <= '1998-12-01'\n partitionsRatio=1/1, tabletsRatio=20/20\n tabletList=45030,45032,45034,45036,45038,45040,45042,45044,45046,45048 ...\n actualRows=3, avgRowSize=54.0\n cardinality: 1\n column statistics: \n * L_QUANTITY-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n * L_EXTENDEDPRICE-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n * L_DISCOUNT-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n * L_TAX-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n * L_RETURNFLAG-->[-Infinity, Infinity, 0.0, 1.0, 1.0] ESTIMATE\n * L_LINESTATUS-->[-Infinity, Infinity, 0.0, 1.0, 1.0] ESTIMATE\n * L_SHIPDATE-->[NaN, NaN, 0.0, 4.0, 1.0] ESTIMATE\n * expr-->[0.0, 0.0, 0.0, 8.0, 1.0] ESTIMATE\n * expr-->[0.0, 0.0, 0.0, 8.0, 1.0] ESTIMATE\n",
"session_variables": "{\"partial_update_mode\":\"auto\",\"cbo_cte_reuse\":true,\"character_set_connection\":\"utf8\",\"cbo_use_correlated_join_estimate\":true,\"enable_insert_strict\":true,\"enable_connector_adaptive_io_tasks\":true,\"tx_isolation\":\"REPEATABLE-READ\",\"enable_hive_metadata_cache_with_insert\":false,\"cbo_cte_reuse_rate_v2\":1.15,\"character_set_results\":\"utf8\",\"enable_count_star_optimization\":true,\"query_excluding_mv_names\":\"\",\"enable_rewrite_simple_agg_to_meta_scan\":false,\"enable_adaptive_sink_dop\":true,\"consistent_hash_virtual_number\":32,\"enable_profile\":false,\"load_mem_limit\":0,\"sql_safe_updates\":0,\"runtime_filter_early_return_selectivity\":0.05,\"enable_local_shuffle_agg\":true,\"disable_function_fold_constants\":false,\"select_ratio_threshold\":0.15,\"query_delivery_timeout\":300,\"collation_database\":\"utf8_general_ci\",\"spill_mem_table_size\":104857600,\"cbo_use_lock_db\":false,\"new_planner_agg_stage\":0,\"use_compute_nodes\":-1,\"collation_connection\":\"utf8_general_ci\",\"resource_group\":\"\",\"profile_limit_fold\":true,\"spill_operator_max_bytes\":1048576000,\"cbo_max_reorder_node_use_dp\":10,\"enable_hive_column_stats\":true,\"enable_groupby_use_output_alias\":false,\"forward_to_leader\":false,\"count_distinct_column_buckets\":1024,\"query_cache_agg_cardinality_limit\":5000000,\"cboPushDownAggregateMode_v1\":-1,\"window_partition_mode\":1,\"enable_tablet_internal_parallel_v2\":true,\"interpolate_passthrough\":true,\"enable_incremental_mv\":false,\"SQL_AUTO_IS_NULL\":false,\"event_scheduler\":\"OFF\",\"max_pipeline_dop\":64,\"broadcast_right_table_scale_factor\":10,\"materialized_view_rewrite_mode\":\"DEFAULT\",\"enable_simplify_case_when\":true,\"runtime_join_filter_push_down_limit\":1024000,\"big_query_log_cpu_second_threshold\":480,\"div_precision_increment\":4,\"runtime_adaptive_dop_max_block_rows_per_driver_seq\":16384,\"log_rejected_record_num\":0,\"cbo_push_down_distinct_below_window\":true,\"sql_mode_v2\":32,\"prefer_cte_rewrite\":false,\"hdfs_backend_selector_scan_range_shuffle\":false,\"pipeline_profile_level\":1,\"parallel_fragment_exec_instance_num\":1,\"max_scan_key_num\":-1,\"net_read_timeout\":60,\"streaming_preaggregation_mode\":\"auto\",\"hive_partition_stats_sample_size\":3000,\"enable_mv_planner\":false,\"enable_collect_table_level_scan_stats\":true,\"profile_timeout\":2,\"cbo_push_down_aggregate\":\"global\",\"spill_encode_level\":7,\"enable_query_dump\":false,\"global_runtime_filter_build_max_size\":67108864,\"enable_rewrite_sum_by_associative_rule\":true,\"query_cache_hot_partition_num\":3,\"enable_prune_complex_types\":true,\"query_cache_type\":0,\"max_parallel_scan_instance_num\":-1,\"query_cache_entry_max_rows\":409600,\"enable_mv_optimizer_trace_log\":false,\"connector_io_tasks_per_scan_operator\":16,\"enable_materialized_view_union_rewrite\":true,\"sql_quote_show_create\":true,\"scan_or_to_union_threshold\":50000000,\"enable_exchange_pass_through\":true,\"runtime_profile_report_interval\":10,\"query_cache_entry_max_bytes\":4194304,\"enable_exchange_perf\":false,\"workgroup_id\":0,\"enable_rewrite_groupingsets_to_union_all\":false,\"transmission_compression_type\":\"NO_COMPRESSION\",\"interactive_timeout\":3600,\"use_page_cache\":true,\"big_query_log_scan_bytes_threshold\":10737418240,\"collation_server\":\"utf8_general_ci\",\"tablet_internal_parallel_mode\":\"auto\",\"enable_pipeline\":true,\"spill_mode\":\"auto\",\"enable_query_debug_trace\":false,\"enable_show_all_variables\":false,\"full_sort_max_buffered_bytes\":16777216,\"wait_timeout\":28800,\"transmission_encode_level\":7,\"query_including_mv_names\":\"\",\"transaction_isolation\":\"REPEATABLE-READ\",\"enable_global_runtime_filter\":true,\"enable_load_profile\":false,\"enable_plan_validation\":true,\"load_transmission_compression_type\":\"NO_COMPRESSION\",\"cbo_enable_low_cardinality_optimize\":true,\"scan_use_query_mem_ratio\":0.3,\"new_planner_optimize_timeout\":3000,\"enable_outer_join_reorder\":true,\"force_schedule_local\":false,\"hudi_mor_force_jni_reader\":false,\"cbo_enable_greedy_join_reorder\":true,\"range_pruner_max_predicate\":100,\"enable_rbo_table_prune\":false,\"spillable_operator_mask\":-1,\"rpc_http_min_size\":2147482624,\"cbo_debug_alive_backend_number\":0,\"global_runtime_filter_probe_min_size\":102400,\"scan_or_to_union_limit\":4,\"enable_cbo_table_prune\":false,\"enable_parallel_merge\":true,\"nested_mv_rewrite_max_level\":3,\"net_write_timeout\":60,\"cbo_prune_shuffle_column_rate\":0.1,\"spill_revocable_max_bytes\":0,\"hash_join_push_down_right_table\":true,\"pipeline_sink_dop\":0,\"broadcast_row_limit\":15000000,\"enable_populate_block_cache\":true,\"exec_mem_limit\":2147483648,\"enable_sort_aggregate\":false,\"query_cache_force_populate\":false,\"runtime_filter_on_exchange_node\":false,\"disable_join_reorder\":false,\"enable_rule_based_materialized_view_rewrite\":true,\"connector_scan_use_query_mem_ratio\":0.3,\"net_buffer_length\":16384,\"cbo_prune_subfield\":true,\"full_sort_max_buffered_rows\":1024000,\"query_timeout\":300,\"connector_io_tasks_slow_io_latency_ms\":50,\"cbo_max_reorder_node\":50,\"enable_distinct_column_bucketization\":false,\"enable_big_query_log\":true,\"analyze_mv\":\"sample\",\"runtime_filter_scan_wait_time\":20,\"enable_sync_materialized_view_rewrite\":true,\"prefer_compute_node\":false,\"enable_strict_type\":false,\"group_concat_max_len\":65535,\"parse_tokens_limit\":3500000,\"chunk_size\":4096,\"global_runtime_filter_probe_min_selectivity\":0.5,\"query_mem_limit\":0,\"enable_filter_unused_columns_in_scan_stage\":true,\"enable_scan_block_cache\":false,\"enable_materialized_view_single_table_view_delta_rewrite\":false,\"auto_increment_increment\":1,\"sql_dialect\":\"StarRocks\",\"big_query_log_scan_rows_threshold\":1000000000,\"character_set_client\":\"utf8\",\"autocommit\":true,\"enable_column_expr_predicate\":true,\"enable_runtime_adaptive_dop\":false,\"cbo_cte_max_limit\":10,\"storage_engine\":\"olap\",\"enable_optimizer_trace_log\":false,\"spill_operator_min_bytes\":52428800,\"cbo_enable_dp_join_reorder\":true,\"tx_visible_wait_timeout\":10,\"enable_materialized_view_view_delta_rewrite\":true,\"cbo_max_reorder_node_use_exhaustive\":4,\"enable_sql_digest\":false,\"spill_mem_table_num\":2,\"enable_spill\":false,\"pipeline_dop\":0,\"single_node_exec_plan\":false,\"full_sort_late_materialization_v2\":true,\"join_implementation_mode_v2\":\"auto\",\"sql_select_limit\":9223372036854775807,\"enable_materialized_view_rewrite\":true,\"statistic_collect_parallel\":1,\"hdfs_backend_selector_hash_algorithm\":\"consistent\",\"disable_colocate_join\":false,\"max_pushdown_conditions_per_column\":-1,\"default_table_compression\":\"lz4_frame\",\"runtime_adaptive_dop_max_output_amplification_factor\":0,\"innodb_read_only\":true,\"spill_mem_limit_threshold\":0.8,\"cbo_reorder_threshold_use_exhaustive\":6,\"enable_predicate_reorder\":false,\"enable_query_cache\":false,\"max_allowed_packet\":33554432,\"time_zone\":\"Asia/Shanghai\",\"enable_multicolumn_global_runtime_filter\":false,\"character_set_server\":\"utf8\",\"cbo_use_nth_exec_plan\":0,\"io_tasks_per_scan_operator\":4,\"parallel_exchange_instance_num\":-1,\"enable_shared_scan\":false,\"allow_default_partition\":false}",
"be_number": 1,
"be_core_stat": {
"numOfHardwareCoresPerBe": "{\"10004\":104}",
"cachedAvgNumOfHardwareCores": 104
},
"exception": [],
"version": "main_querydump",
"commit_version": "0c4d8c8d3e"
}
command:
wget --user=root --password=123 --post-file query_file "http://127.0.0.1:8030/api/query_dump?db=tpch -O dump_file
Return data:
The desensitized data is returned in JSON format.
{
"statement": "SELECT tbl_mock_001.mock_012, tbl_mock_001.mock_007, sum(tbl_mock_001.mock_010) AS mock_019, sum(tbl_mock_001.mock_005) AS mock_020, sum(tbl_mock_001.mock_005 * (1 - tbl_mock_001.mock_004)) AS mock_021, sum((tbl_mock_001.mock_005 * (1 - tbl_mock_001.mock_004)) * (1 + tbl_mock_001.mock_017)) AS mock_022, avg(tbl_mock_001.mock_010) AS mock_023, avg(tbl_mock_001.mock_005) AS mock_024, avg(tbl_mock_001.mock_004) AS mock_025, count(*) AS mock_026\nFROM db_mock_000.tbl_mock_001\nWHERE tbl_mock_001.mock_013 <= '1998-12-01'\nGROUP BY tbl_mock_001.mock_012, tbl_mock_001.mock_007 ORDER BY tbl_mock_001.mock_012 ASC, tbl_mock_001.mock_007 ASC ",
"table_meta": {
"db_mock_000.tbl_mock_001": "CREATE TABLE db_mock_000.tbl_mock_001 (\nmock_008 int(11) NOT NULL ,\nmock_009 int(11) NOT NULL ,\nmock_016 int(11) NOT NULL ,\nmock_006 int(11) NOT NULL ,\nmock_010 double NOT NULL ,\nmock_005 double NOT NULL ,\nmock_004 double NOT NULL ,\nmock_017 double NOT NULL ,\nmock_012 char(1) NOT NULL ,\nmock_007 char(1) NOT NULL ,\nmock_013 date NOT NULL ,\nmock_003 date NOT NULL ,\nmock_011 date NOT NULL ,\nmock_014 char(25) NOT NULL ,\nmock_015 char(10) NOT NULL ,\nmock_002 varchar(44) NOT NULL ,\nmock_018 char(1) NOT NULL \n) ENGINE= OLAP \nDUPLICATE KEY(mock_008)\nDISTRIBUTED BY HASH(mock_008) BUCKETS 20 \nPROPERTIES (\n\"replication_num\" = \"1\"\n);"
},
"table_row_count": {
"db_mock_000.tbl_mock_001": {
"tbl_mock_001": 3
}
},
"column_statistics": {
"db_mock_000.tbl_mock_001": {
"mock_017": "[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE",
"mock_013": "[1.6094304E9, 1.6094304E9, 0.0, 4.0, 1.0] ESTIMATE",
"mock_005": "[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE",
"mock_004": "[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE",
"mock_012": "[-Infinity, Infinity, 0.0, 1.0, 1.0] ESTIMATE",
"mock_007": "[-Infinity, Infinity, 0.0, 1.0, 1.0] ESTIMATE",
"mock_010": "[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE"
}
},
"explain_info": "PLAN FRAGMENT 0(F02)\n Output Exprs:9: mock_012 | 10: mock_007 | 20: sum | 21: sum | 22: sum | 23: sum | 24: avg | 25: avg | 26: avg | 27: count\n Input Partition: UNPARTITIONED\n RESULT SINK\n\n 6:MERGING-EXCHANGE\n distribution type: GATHER\n cardinality: 1\n column statistics: \n * mock_012-->[-Infinity, Infinity, 0.0, 1.0, 1.0] ESTIMATE\n * mock_007-->[-Infinity, Infinity, 0.0, 1.0, 1.0] ESTIMATE\n * sum-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n * sum-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n * sum-->[0.0, 0.0, 0.0, 8.0, 1.0] ESTIMATE\n * sum-->[0.0, 0.0, 0.0, 8.0, 1.0] ESTIMATE\n * avg-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n * avg-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n * avg-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n * count-->[0.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n\nPLAN FRAGMENT 1(F01)\n\n Input Partition: HASH_PARTITIONED: 9: mock_012, 10: mock_007\n OutPut Partition: UNPARTITIONED\n OutPut Exchange id: 06\n\n 5:SORT\n | order by: [9, VARCHAR, false] ASC, [10, VARCHAR, false] ASC\n | offset: 0\n | cardinality: 1\n | column statistics: \n | * mock_012-->[-Infinity, Infinity, 0.0, 1.0, 1.0] ESTIMATE\n | * mock_007-->[-Infinity, Infinity, 0.0, 1.0, 1.0] ESTIMATE\n | * sum-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | * sum-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | * sum-->[0.0, 0.0, 0.0, 8.0, 1.0] ESTIMATE\n | * sum-->[0.0, 0.0, 0.0, 8.0, 1.0] ESTIMATE\n | * avg-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | * avg-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | * avg-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | * count-->[0.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | \n 4:AGGREGATE (merge finalize)\n | aggregate: sum[([20: sum, DOUBLE, true]); args: DOUBLE; result: DOUBLE; args nullable: true; result nullable: true], sum[([21: sum, DOUBLE, true]); args: DOUBLE; result: DOUBLE; args nullable: true; result nullable: true], sum[([22: sum, DOUBLE, true]); args: DOUBLE; result: DOUBLE; args nullable: true; result nullable: true], sum[([23: sum, DOUBLE, true]); args: DOUBLE; result: DOUBLE; args nullable: true; result nullable: true], avg[([24: avg, VARBINARY, true]); args: DOUBLE; result: DOUBLE; args nullable: true; result nullable: true], avg[([25: avg, VARBINARY, true]); args: DOUBLE; result: DOUBLE; args nullable: true; result nullable: true], avg[([26: avg, VARBINARY, true]); args: DOUBLE; result: DOUBLE; args nullable: true; result nullable: true], count[([27: count, BIGINT, false]); args: ; result: BIGINT; args nullable: true; result nullable: false]\n | group by: [9: mock_012, VARCHAR, false], [10: mock_007, VARCHAR, false]\n | cardinality: 1\n | column statistics: \n | * mock_012-->[-Infinity, Infinity, 0.0, 1.0, 1.0] ESTIMATE\n | * mock_007-->[-Infinity, Infinity, 0.0, 1.0, 1.0] ESTIMATE\n | * sum-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | * sum-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | * sum-->[0.0, 0.0, 0.0, 8.0, 1.0] ESTIMATE\n | * sum-->[0.0, 0.0, 0.0, 8.0, 1.0] ESTIMATE\n | * avg-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | * avg-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | * avg-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | * count-->[0.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | \n 3:EXCHANGE\n distribution type: SHUFFLE\n partition exprs: [9: mock_012, VARCHAR, false], [10: mock_007, VARCHAR, false]\n cardinality: 1\n\nPLAN FRAGMENT 2(F00)\n\n Input Partition: RANDOM\n OutPut Partition: HASH_PARTITIONED: 9: mock_012, 10: mock_007\n OutPut Exchange id: 03\n\n 2:AGGREGATE (update serialize)\n | STREAMING\n | aggregate: sum[([5: mock_010, DOUBLE, false]); args: DOUBLE; result: DOUBLE; args nullable: false; result nullable: true], sum[([6: mock_005, DOUBLE, false]); args: DOUBLE; result: DOUBLE; args nullable: false; result nullable: true], sum[([18: expr, DOUBLE, false]); args: DOUBLE; result: DOUBLE; args nullable: false; result nullable: true], sum[([19: expr, DOUBLE, false]); args: DOUBLE; result: DOUBLE; args nullable: false; result nullable: true], avg[([5: mock_010, DOUBLE, false]); args: DOUBLE; result: VARBINARY; args nullable: false; result nullable: true], avg[([6: mock_005, DOUBLE, false]); args: DOUBLE; result: VARBINARY; args nullable: false; result nullable: true], avg[([7: mock_004, DOUBLE, false]); args: DOUBLE; result: VARBINARY; args nullable: false; result nullable: true], count[(*); args: ; result: BIGINT; args nullable: false; result nullable: false]\n | group by: [9: mock_012, VARCHAR, false], [10: mock_007, VARCHAR, false]\n | cardinality: 1\n | column statistics: \n | * mock_012-->[-Infinity, Infinity, 0.0, 1.0, 1.0] ESTIMATE\n | * mock_007-->[-Infinity, Infinity, 0.0, 1.0, 1.0] ESTIMATE\n | * sum-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | * sum-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | * sum-->[0.0, 0.0, 0.0, 8.0, 1.0] ESTIMATE\n | * sum-->[0.0, 0.0, 0.0, 8.0, 1.0] ESTIMATE\n | * avg-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | * avg-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | * avg-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | * count-->[0.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | \n 1:Project\n | output columns:\n | 5 <-> [5: mock_010, DOUBLE, false]\n | 6 <-> [6: mock_005, DOUBLE, false]\n | 7 <-> [7: mock_004, DOUBLE, false]\n | 9 <-> [9: mock_012, CHAR, false]\n | 10 <-> [10: mock_007, CHAR, false]\n | 18 <-> [29: multiply, DOUBLE, false]\n | 19 <-> [29: multiply, DOUBLE, false] * 1.0 + [8: mock_017, DOUBLE, false]\n | common expressions:\n | 28 <-> 1.0 - [7: mock_004, DOUBLE, false]\n | 29 <-> [6: mock_005, DOUBLE, false] * [28: subtract, DOUBLE, false]\n | cardinality: 1\n | column statistics: \n | * mock_010-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | * mock_005-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | * mock_004-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n | * mock_012-->[-Infinity, Infinity, 0.0, 1.0, 1.0] ESTIMATE\n | * mock_007-->[-Infinity, Infinity, 0.0, 1.0, 1.0] ESTIMATE\n | * expr-->[0.0, 0.0, 0.0, 8.0, 1.0] ESTIMATE\n | * expr-->[0.0, 0.0, 0.0, 8.0, 1.0] ESTIMATE\n | \n 0:OlapScanNode\n table: mock_001, rollup: mock_001\n preAggregation: on\n Predicates: [11: mock_013, DATE, false] <= '1998-12-01'\n partitionsRatio=1/1, tabletsRatio=20/20\n tabletList=45030,45032,45034,45036,45038,45040,45042,45044,45046,45048 ...\n actualRows=3, avgRowSize=54.0\n cardinality: 1\n column statistics: \n * mock_010-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n * mock_005-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n * mock_004-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n * mock_017-->[1.0, 1.0, 0.0, 8.0, 1.0] ESTIMATE\n * mock_012-->[-Infinity, Infinity, 0.0, 1.0, 1.0] ESTIMATE\n * mock_007-->[-Infinity, Infinity, 0.0, 1.0, 1.0] ESTIMATE\n * mock_013-->[NaN, NaN, 0.0, 4.0, 1.0] ESTIMATE\n * expr-->[0.0, 0.0, 0.0, 8.0, 1.0] ESTIMATE\n * expr-->[0.0, 0.0, 0.0, 8.0, 1.0] ESTIMATE\n",
"session_variables": "{\"partial_update_mode\":\"auto\",\"cbo_cte_reuse\":true,\"character_set_connection\":\"utf8\",\"cbo_use_correlated_join_estimate\":true,\"enable_insert_strict\":true,\"enable_connector_adaptive_io_tasks\":true,\"tx_isolation\":\"REPEATABLE-READ\",\"enable_hive_metadata_cache_with_insert\":false,\"cbo_cte_reuse_rate_v2\":1.15,\"character_set_results\":\"utf8\",\"enable_count_star_optimization\":true,\"query_excluding_mv_names\":\"\",\"enable_rewrite_simple_agg_to_meta_scan\":false,\"enable_adaptive_sink_dop\":true,\"consistent_hash_virtual_number\":32,\"enable_profile\":false,\"load_mem_limit\":0,\"sql_safe_updates\":0,\"runtime_filter_early_return_selectivity\":0.05,\"enable_local_shuffle_agg\":true,\"disable_function_fold_constants\":false,\"select_ratio_threshold\":0.15,\"query_delivery_timeout\":300,\"collation_database\":\"utf8_general_ci\",\"spill_mem_table_size\":104857600,\"cbo_use_lock_db\":false,\"new_planner_agg_stage\":0,\"use_compute_nodes\":-1,\"collation_connection\":\"utf8_general_ci\",\"resource_group\":\"\",\"profile_limit_fold\":true,\"spill_operator_max_bytes\":1048576000,\"cbo_max_reorder_node_use_dp\":10,\"enable_hive_column_stats\":true,\"enable_groupby_use_output_alias\":false,\"forward_to_leader\":false,\"count_distinct_column_buckets\":1024,\"query_cache_agg_cardinality_limit\":5000000,\"cboPushDownAggregateMode_v1\":-1,\"window_partition_mode\":1,\"enable_tablet_internal_parallel_v2\":true,\"interpolate_passthrough\":true,\"enable_incremental_mv\":false,\"SQL_AUTO_IS_NULL\":false,\"event_scheduler\":\"OFF\",\"max_pipeline_dop\":64,\"broadcast_right_table_scale_factor\":10,\"materialized_view_rewrite_mode\":\"DEFAULT\",\"enable_simplify_case_when\":true,\"runtime_join_filter_push_down_limit\":1024000,\"big_query_log_cpu_second_threshold\":480,\"div_precision_increment\":4,\"runtime_adaptive_dop_max_block_rows_per_driver_seq\":16384,\"log_rejected_record_num\":0,\"cbo_push_down_distinct_below_window\":true,\"sql_mode_v2\":32,\"prefer_cte_rewrite\":false,\"hdfs_backend_selector_scan_range_shuffle\":false,\"pipeline_profile_level\":1,\"parallel_fragment_exec_instance_num\":1,\"max_scan_key_num\":-1,\"net_read_timeout\":60,\"streaming_preaggregation_mode\":\"auto\",\"hive_partition_stats_sample_size\":3000,\"enable_mv_planner\":false,\"enable_collect_table_level_scan_stats\":true,\"profile_timeout\":2,\"cbo_push_down_aggregate\":\"global\",\"spill_encode_level\":7,\"enable_query_dump\":false,\"global_runtime_filter_build_max_size\":67108864,\"enable_rewrite_sum_by_associative_rule\":true,\"query_cache_hot_partition_num\":3,\"enable_prune_complex_types\":true,\"query_cache_type\":0,\"max_parallel_scan_instance_num\":-1,\"query_cache_entry_max_rows\":409600,\"enable_mv_optimizer_trace_log\":false,\"connector_io_tasks_per_scan_operator\":16,\"enable_materialized_view_union_rewrite\":true,\"sql_quote_show_create\":true,\"scan_or_to_union_threshold\":50000000,\"enable_exchange_pass_through\":true,\"runtime_profile_report_interval\":10,\"query_cache_entry_max_bytes\":4194304,\"enable_exchange_perf\":false,\"workgroup_id\":0,\"enable_rewrite_groupingsets_to_union_all\":false,\"transmission_compression_type\":\"NO_COMPRESSION\",\"interactive_timeout\":3600,\"use_page_cache\":true,\"big_query_log_scan_bytes_threshold\":10737418240,\"collation_server\":\"utf8_general_ci\",\"tablet_internal_parallel_mode\":\"auto\",\"enable_pipeline\":true,\"spill_mode\":\"auto\",\"enable_query_debug_trace\":false,\"enable_show_all_variables\":false,\"full_sort_max_buffered_bytes\":16777216,\"wait_timeout\":28800,\"transmission_encode_level\":7,\"query_including_mv_names\":\"\",\"transaction_isolation\":\"REPEATABLE-READ\",\"enable_global_runtime_filter\":true,\"enable_load_profile\":false,\"enable_plan_validation\":true,\"load_transmission_compression_type\":\"NO_COMPRESSION\",\"cbo_enable_low_cardinality_optimize\":true,\"scan_use_query_mem_ratio\":0.3,\"new_planner_optimize_timeout\":3000,\"enable_outer_join_reorder\":true,\"force_schedule_local\":false,\"hudi_mor_force_jni_reader\":false,\"cbo_enable_greedy_join_reorder\":true,\"range_pruner_max_predicate\":100,\"enable_rbo_table_prune\":false,\"spillable_operator_mask\":-1,\"rpc_http_min_size\":2147482624,\"cbo_debug_alive_backend_number\":0,\"global_runtime_filter_probe_min_size\":102400,\"scan_or_to_union_limit\":4,\"enable_cbo_table_prune\":false,\"enable_parallel_merge\":true,\"nested_mv_rewrite_max_level\":3,\"net_write_timeout\":60,\"cbo_prune_shuffle_column_rate\":0.1,\"spill_revocable_max_bytes\":0,\"hash_join_push_down_right_table\":true,\"pipeline_sink_dop\":0,\"broadcast_row_limit\":15000000,\"enable_populate_block_cache\":true,\"exec_mem_limit\":2147483648,\"enable_sort_aggregate\":false,\"query_cache_force_populate\":false,\"runtime_filter_on_exchange_node\":false,\"disable_join_reorder\":false,\"enable_rule_based_materialized_view_rewrite\":true,\"connector_scan_use_query_mem_ratio\":0.3,\"net_buffer_length\":16384,\"cbo_prune_subfield\":true,\"full_sort_max_buffered_rows\":1024000,\"query_timeout\":300,\"connector_io_tasks_slow_io_latency_ms\":50,\"cbo_max_reorder_node\":50,\"enable_distinct_column_bucketization\":false,\"enable_big_query_log\":true,\"analyze_mv\":\"sample\",\"runtime_filter_scan_wait_time\":20,\"enable_sync_materialized_view_rewrite\":true,\"prefer_compute_node\":false,\"enable_strict_type\":false,\"group_concat_max_len\":65535,\"parse_tokens_limit\":3500000,\"chunk_size\":4096,\"global_runtime_filter_probe_min_selectivity\":0.5,\"query_mem_limit\":0,\"enable_filter_unused_columns_in_scan_stage\":true,\"enable_scan_block_cache\":false,\"enable_materialized_view_single_table_view_delta_rewrite\":false,\"auto_increment_increment\":1,\"sql_dialect\":\"StarRocks\",\"big_query_log_scan_rows_threshold\":1000000000,\"character_set_client\":\"utf8\",\"autocommit\":true,\"enable_column_expr_predicate\":true,\"enable_runtime_adaptive_dop\":false,\"cbo_cte_max_limit\":10,\"storage_engine\":\"olap\",\"enable_optimizer_trace_log\":false,\"spill_operator_min_bytes\":52428800,\"cbo_enable_dp_join_reorder\":true,\"tx_visible_wait_timeout\":10,\"enable_materialized_view_view_delta_rewrite\":true,\"cbo_max_reorder_node_use_exhaustive\":4,\"enable_sql_digest\":false,\"spill_mem_table_num\":2,\"enable_spill\":false,\"pipeline_dop\":0,\"single_node_exec_plan\":false,\"full_sort_late_materialization_v2\":true,\"join_implementation_mode_v2\":\"auto\",\"sql_select_limit\":9223372036854775807,\"enable_materialized_view_rewrite\":true,\"statistic_collect_parallel\":1,\"hdfs_backend_selector_hash_algorithm\":\"consistent\",\"disable_colocate_join\":false,\"max_pushdown_conditions_per_column\":-1,\"default_table_compression\":\"lz4_frame\",\"runtime_adaptive_dop_max_output_amplification_factor\":0,\"innodb_read_only\":true,\"spill_mem_limit_threshold\":0.8,\"cbo_reorder_threshold_use_exhaustive\":6,\"enable_predicate_reorder\":false,\"enable_query_cache\":false,\"max_allowed_packet\":33554432,\"time_zone\":\"Asia/Shanghai\",\"enable_multicolumn_global_runtime_filter\":false,\"character_set_server\":\"utf8\",\"cbo_use_nth_exec_plan\":0,\"io_tasks_per_scan_operator\":4,\"parallel_exchange_instance_num\":-1,\"enable_shared_scan\":false,\"allow_default_partition\":false}",
"be_number": 1,
"be_core_stat": {
"numOfHardwareCoresPerBe": "{\"10004\":104}",
"cachedAvgNumOfHardwareCores": 104
},
"exception": [],
"version": "main_querydump",
"commit_version": "0c4d8c8d3e"
}