バージョン: 3.3

query_dump インターフェース

このトピックでは、query_dump インターフェースを使用して SQL クエリの詳細と関連情報を取得する方法について説明します。

StarRocks で SQL クエリを実行する際に次のような問題が発生した場合、query_dump を使用して SQL の詳細を取得し、StarRocks の技術サポートに情報を送信してトラブルシューティングを行うことができます。

SQL クエリまたは EXPLAIN を実行すると Unknown Error が返される。
SQL クエリを実行するとエラーメッセージや例外が返される。
SQL クエリの実行が期待通りに効率的でない、または実行プランを最適化できる（例えば、パーティションをプルーニングしたり、Join の順序を調整したりできる）。

機能概要

query_dump インターフェースは、SQL を実行する際に FE が依存する情報を返します。これには以下が含まれます。

クエリ文
テーブル作成文
セッション変数
BEs の数
統計情報（列の最小値、最大値）
例外情報（異常スタック）
Explain コスト情報

データのプライバシーを確保するため、データベース名、テーブル名、列名などのメタ情報をマスキングします。また、マスキングされたメタデータを使用してクエリ文を再作成します。

メタ情報のマスキングはデフォルトで有効になっています。マスキング処理中に例外が発生した場合、元の情報が使用されます。マスキングをバイパスする必要がある場合は、HTTP URI に "mock=false" を追加できます。

構文

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}

パラメータの説明:

query_file: クエリを含むファイル
dump_file: 出力ファイル
db: SQL クエリが実行されるデータベース。クエリに use db が含まれている場合は db パラメータはオプションです。それ以外の場合は指定する必要があります。
mock: マスキングを有効または無効にするかどうか

例

マスキングを無効にする

コマンド:

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

返されるデータ:

データは JSON 形式で返されます。

{
  "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"
}

マスキングを有効にする（デフォルト）

コマンド:

wget --user=root --password=123 --post-file query_file "http://127.0.0.1:8030/api/query_dump?db=tpch -O dump_file

返されるデータ:

マスキングされたデータは JSON 形式で返されます。

{
  "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"
}

機能概要​

構文​

例​

マスキングを無効にする​

マスキングを有効にする（デフォルト）​

機能概要

構文

例

マスキングを無効にする

マスキングを有効にする（デフォルト）