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p99, throttling, credits, storage, quota, alert, dashboard, Datadog, Grafana, Streamlit, Native App, Unistore, OLTP\nrelated_concepts: AGGREGATE_QUERY_HISTORY, HYBRID_TABLE_USAGE_HISTORY, TABLE_STORAGE_METRICS, query_parameterized_hash, hybrid_table_requests_throttled_count, CREATE ALERT, METERING_HISTORY\nprerequisite_guides: getting-started-with-hybrid-tables\nskill_level: intermediate\nestimated_time_minutes: 45\nsnowflake_features: hybrid_tables, alerts, aggregate_query_history, account_usage\n-->\n","\u003Ch1\u003EMonitoring and Observability for Hybrid Tables\u003C/h1\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote on Production Workloads:\u003C/strong\u003E The monitoring queries in this guide use \u003Ccode\u003ESNOWFLAKE.ACCOUNT_USAGE\u003C/code\u003E views which have up to 3 hours of latency. For real-time diagnosis of individual queries, use Query Profile in Snowsight.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EHybrid Table workloads are invisible to traditional Snowflake monitoring. Queries completing in under one second do not appear in \u003Ccode\u003EQUERY_HISTORY\u003C/code\u003E, and standard dashboards built on that view will show zero activity even while thousands of operations per second are executing successfully.\u003C/p\u003E\n","\u003Cp\u003EThis guide builds a complete observability stack for Hybrid Table workloads:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EUnderstanding the monitoring gap and where to find HT telemetry\u003C/li\u003E\u003Cli\u003EExtracting latency percentiles, throughput, and error rates from AGGREGATE_QUERY_HISTORY\u003C/li\u003E\u003Cli\u003EBuilding monitoring dashboards for ongoing visibility\u003C/li\u003E\u003Cli\u003ETracking credits and storage against quotas\u003C/li\u003E\u003Cli\u003ECreating multi-signal alerts (throttling, latency regression, error spikes)\u003C/li\u003E\u003Cli\u003EBuilding custom monitoring apps (Streamlit, Snowflake Native Apps, OpenTelemetry)\u003C/li\u003E\u003Cli\u003EExporting metrics to external tools (Datadog, Grafana, Prometheus)\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EWhat You Will Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EWhy \u003Ccode\u003EQUERY_HISTORY\u003C/code\u003E misses most HT activity and what to use instead\u003C/li\u003E\u003Cli\u003EHow to read OBJECT-typed columns (avg, median, p90, p99, max) in AGGREGATE_QUERY_HISTORY\u003C/li\u003E\u003Cli\u003EHow to build monitoring dashboards with latency trends and throughput\u003C/li\u003E\u003Cli\u003EHow to track HT credit consumption and storage against the 2 TB per-database quota\u003C/li\u003E\u003Cli\u003EHow to create alerts for throttling, latency regression, and error spikes\u003C/li\u003E\u003Cli\u003EHow to export HT metrics to external observability platforms\u003C/li\u003E\u003Cli\u003EHow to build custom monitoring dashboards with Streamlit in Snowflake\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake paid account in an AWS or Azure commercial region\u003C/li\u003E\u003Cli\u003EFamiliarity with \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-hybrid\"\u003EHybrid Tables\u003C/a\u003E and basic SQL\u003C/li\u003E\u003Cli\u003EACCOUNTADMIN role (used in this guide for simplicity) or any role with access to \u003Ccode\u003ESNOWFLAKE.ACCOUNT_USAGE\u003C/code\u003E views and the ability to create alerts\u003C/li\u003E\u003Cli\u003EAn existing Hybrid Table workload with some query activity (or run the setup below to simulate one)\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote on Roles:\u003C/strong\u003E This quickstart uses ACCOUNTADMIN for simplicity. In production, grant \u003Ccode\u003EIMPORTED PRIVILEGES\u003C/code\u003E on the \u003Ccode\u003ESNOWFLAKE\u003C/code\u003E database to a custom role and use that instead. Any role with access to \u003Ccode\u003ESNOWFLAKE.ACCOUNT_USAGE\u003C/code\u003E views can run the monitoring queries.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetup\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\n\nCREATE OR REPLACE ROLE HT_MON_QS_ROLE;\nGRANT ROLE HT_MON_QS_ROLE TO ROLE ACCOUNTADMIN;\n\nCREATE OR REPLACE WAREHOUSE HT_MON_QS_WH\n  WAREHOUSE_SIZE = XSMALL\n  AUTO_SUSPEND = 300\n  AUTO_RESUME = TRUE;\nGRANT OWNERSHIP ON WAREHOUSE HT_MON_QS_WH TO ROLE HT_MON_QS_ROLE;\n\nCREATE OR REPLACE DATABASE HT_MON_QS_DB;\nGRANT OWNERSHIP ON DATABASE HT_MON_QS_DB TO ROLE HT_MON_QS_ROLE;\n\nUSE ROLE HT_MON_QS_ROLE;\nCREATE OR REPLACE SCHEMA HT_MON_QS_DB.DATA;\nUSE WAREHOUSE HT_MON_QS_WH;\nUSE DATABASE HT_MON_QS_DB;\nUSE SCHEMA DATA;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate a Sample Workload Table\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE HYBRID TABLE session_state (\n    session_id   VARCHAR(36)    NOT NULL,\n    user_id      NUMBER         NOT NULL,\n    device_type  VARCHAR(20)    NOT NULL,\n    last_active  TIMESTAMP_NTZ  NOT NULL,\n    payload      VARIANT,\n    PRIMARY KEY (session_id)\n);\n\nCREATE INDEX idx_session_user ON session_state (user_id);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EGenerate Sample Data\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EINSERT INTO session_state\nSELECT\n    UUID_STRING() AS session_id,\n    UNIFORM(1, 10000, RANDOM()) AS user_id,\n    ARRAY_CONSTRUCT('mobile','desktop','tablet')[UNIFORM(0,2,RANDOM())]::VARCHAR AS device_type,\n    DATEADD(MINUTE, -UNIFORM(1, 1440, RANDOM()), CURRENT_TIMESTAMP())::TIMESTAMP_NTZ AS last_active,\n    OBJECT_CONSTRUCT('ip', '10.0.' || UNIFORM(0,255,RANDOM())::VARCHAR || '.' || UNIFORM(0,255,RANDOM())::VARCHAR) AS payload\nFROM TABLE(GENERATOR(ROWCOUNT => 50000));\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ESimulate Workload Activity\u003C/h3\u003E\n","\u003Cp\u003ERun several queries to generate telemetry in AGGREGATE_QUERY_HISTORY (visible after up to 3 hours):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * FROM session_state WHERE session_id = (SELECT session_id FROM session_state LIMIT 1);\nSELECT * FROM session_state WHERE user_id = 42;\nUPDATE session_state SET last_active = CURRENT_TIMESTAMP()::TIMESTAMP_NTZ WHERE session_id = (SELECT session_id FROM session_state LIMIT 1);\nDELETE FROM session_state WHERE last_active < DATEADD(DAY, -1, CURRENT_TIMESTAMP())::TIMESTAMP_NTZ;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E AGGREGATE_QUERY_HISTORY has up to 3 hours of latency. If you are running this guide for the first time, you may need to wait before the monitoring queries return data. The SQL patterns are valid regardless — apply them to your own existing HT workloads for immediate results.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EStep 1: The Monitoring Gap\u003C/h2\u003E\n","\u003Ch3\u003EWhy QUERY_HISTORY Misses Hybrid Table Activity\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\n\nSELECT COUNT(*) AS queries_in_history\nFROM SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY\nWHERE start_time > DATEADD(DAY, -1, CURRENT_TIMESTAMP())\n  AND total_elapsed_time < 1000\n  AND query_type IN ('SELECT', 'INSERT', 'UPDATE', 'DELETE', 'MERGE');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EStandard \u003Ccode\u003EQUERY_HISTORY\u003C/code\u003E captures individual query executions — but queries completing in under ~1 second are often excluded or sampled. For OLTP workloads executing thousands of sub-second operations per minute, this creates a blind spot.\u003C/p\u003E\n","\u003Ch3\u003EWhere HT Telemetry Lives\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EView\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhat It Shows\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ELatency\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EGranularity\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EAGGREGATE_QUERY_HISTORY\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAll queries (including sub-second), aggregated per minute per query shape\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUp to 3 hours\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1-minute windows\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EHYBRID_TABLE_USAGE_HISTORY\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECredits consumed by HT background operations\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUp to 3 hours\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPer-table, hourly\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ETABLE_STORAGE_METRICS\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStorage bytes per table (active, time travel, failsafe)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUp to 3 hours\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPer-table\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EMETERING_HISTORY\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOverall warehouse credits (includes HT query compute)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUp to 3 hours\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPer-warehouse, hourly\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EQuery Profile (Snowsight)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EReal-time single-query diagnosis\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EImmediate\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPer-query\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EThe Key View: AGGREGATE_QUERY_HISTORY\u003C/h3\u003E\n","\u003Cp\u003EThis view captures \u003Cstrong\u003Eevery\u003C/strong\u003E query regardless of duration, aggregated into 1-minute intervals grouped by \u003Ccode\u003Equery_parameterized_hash\u003C/code\u003E. Each row represents all executions of a specific query shape within a 1-minute window.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    interval_start_time,\n    query_parameterized_hash,\n    query_text,\n    calls,\n    total_elapsed_time,\n    hybrid_table_requests_throttled_count\nFROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\nWHERE interval_start_time > DATEADD(DAY, -1, CURRENT_TIMESTAMP())\nORDER BY interval_start_time DESC\nLIMIT 5;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EStep 2: Reading AGGREGATE_QUERY_HISTORY\u003C/h2\u003E\n","\u003Ch3\u003EUnderstanding OBJECT-Typed Columns\u003C/h3\u003E\n","\u003Cp\u003EMost numeric columns in AGGREGATE_QUERY_HISTORY are \u003Cstrong\u003EOBJECT\u003C/strong\u003E typed, containing statistical aggregates:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-json\"\u003E{\n  "avg": 23.5,\n  "count": 150,\n  "max": 89.0,\n  "min": 12.0,\n  "median": 21.0,\n  "p90": 45.0,\n  "p99": 78.0,\n  "p99.9": 85.0,\n  "sum": 3525.0\n}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EExtract specific percentiles using the \u003Ccode\u003E:\u003C/code\u003E operator:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    interval_start_time,\n    query_parameterized_hash,\n    ANY_VALUE(query_text) AS sample_query,\n    SUM(calls) AS total_calls,\n    AVG(total_elapsed_time:"avg"::FLOAT) AS avg_latency_ms,\n    MAX(total_elapsed_time:"median"::FLOAT) AS p50_latency_ms,\n    MAX(total_elapsed_time:"p90"::FLOAT) AS p90_latency_ms,\n    MAX(total_elapsed_time:"p99"::FLOAT) AS p99_latency_ms,\n    MAX(total_elapsed_time:"max"::FLOAT) AS max_latency_ms\nFROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\nWHERE interval_start_time > DATEADD(HOUR, -6, CURRENT_TIMESTAMP())\nGROUP BY interval_start_time, query_parameterized_hash\nORDER BY total_calls DESC\nLIMIT 20;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECompilation vs Execution Time Split\u003C/h3\u003E\n","\u003Cp\u003EFor HT workloads, high compilation time relative to execution time indicates plan cache misses:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    query_parameterized_hash,\n    ANY_VALUE(query_text) AS sample_query,\n    SUM(calls) AS total_calls,\n    AVG(compilation_time:"avg"::FLOAT) AS avg_compile_ms,\n    AVG(execution_time:"avg"::FLOAT) AS avg_exec_ms,\n    ROUND(AVG(compilation_time:"avg"::FLOAT) /\n      NULLIF(AVG(total_elapsed_time:"avg"::FLOAT), 0) * 100, 1) AS compile_pct\nFROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\nWHERE interval_start_time > DATEADD(DAY, -1, CURRENT_TIMESTAMP())\n  AND calls > 10\nGROUP BY query_parameterized_hash\nHAVING AVG(compilation_time:"avg"::FLOAT) > 50\nORDER BY compile_pct DESC\nLIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EInterpretation:\u003C/strong\u003E If \u003Ccode\u003Ecompile_pct\u003C/code\u003E is above 50%, the query is spending more time compiling than executing. This usually means plan cache is not being utilized — check that you are using bound variables and consistent schema qualification. See \u003Ca href=\"https://www.snowflake.com/en/developers/guides/hybrid-tables-write-optimization/\"\u003EWrite Optimization\u003C/a\u003E for details.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EError Analysis\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Ccode\u003EERRORS\u003C/code\u003E column is an ARRAY of error objects. Query shapes with recurring errors:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    query_parameterized_hash,\n    ANY_VALUE(query_text) AS sample_query,\n    SUM(calls) AS total_calls,\n    SUM(ARRAY_SIZE(errors)) AS total_errors,\n    ROUND(SUM(ARRAY_SIZE(errors)) / NULLIF(SUM(calls), 0) * 100, 2) AS error_rate_pct\nFROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\nWHERE interval_start_time > DATEADD(DAY, -1, CURRENT_TIMESTAMP())\n  AND ARRAY_SIZE(errors) > 0\nGROUP BY query_parameterized_hash\nORDER BY total_errors DESC\nLIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EThrottling Detection\u003C/h3\u003E\n","\u003Cp\u003EWhen HT request throughput exceeds the database-level quota (~16K ops/sec), requests are throttled:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    interval_start_time,\n    SUM(calls) AS total_calls,\n    SUM(hybrid_table_requests_throttled_count) AS throttled_count,\n    ROUND(SUM(hybrid_table_requests_throttled_count) / NULLIF(SUM(calls), 0) * 100, 2) AS throttle_pct\nFROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\nWHERE interval_start_time > DATEADD(HOUR, -24, CURRENT_TIMESTAMP())\nGROUP BY interval_start_time\nHAVING throttled_count > 0\nORDER BY interval_start_time DESC;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EStep 3: Monitoring Dashboard Queries\u003C/h2\u003E\n","\u003Cp\u003EThese queries can be used in any visualization tool: Streamlit in Snowflake (see Step 6), Grafana (see Step 7), or any BI tool connected to your account.\u003C/p\u003E\n","\u003Ch3\u003ETile 1: Throughput Over Time (QPS)\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    interval_start_time AS ts,\n    SUM(calls) / 60.0 AS queries_per_second\nFROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\nWHERE interval_start_time > DATEADD(HOUR, -24, CURRENT_TIMESTAMP())\n  AND database_name = 'HT_MON_QS_DB'\nGROUP BY interval_start_time\nORDER BY ts;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ETile 2: Latency Percentiles Over Time\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    interval_start_time AS ts,\n    AVG(total_elapsed_time:"median"::FLOAT) AS p50_ms,\n    AVG(total_elapsed_time:"p90"::FLOAT) AS p90_ms,\n    AVG(total_elapsed_time:"p99"::FLOAT) AS p99_ms\nFROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\nWHERE interval_start_time > DATEADD(HOUR, -24, CURRENT_TIMESTAMP())\n  AND database_name = 'HT_MON_QS_DB'\n  AND query_type IN ('SELECT', 'INSERT', 'UPDATE', 'DELETE', 'MERGE')\nGROUP BY interval_start_time\nORDER BY ts;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ETile 3: Top 10 Query Shapes by Volume\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    query_parameterized_hash,\n    ANY_VALUE(LEFT(query_text, 80)) AS query_preview,\n    SUM(calls) AS total_calls,\n    AVG(total_elapsed_time:"avg"::FLOAT) AS avg_ms,\n    MAX(total_elapsed_time:"p99"::FLOAT) AS p99_ms\nFROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\nWHERE interval_start_time > DATEADD(DAY, -1, CURRENT_TIMESTAMP())\n  AND database_name = 'HT_MON_QS_DB'\nGROUP BY query_parameterized_hash\nORDER BY total_calls DESC\nLIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ETile 4: Read vs Write Mix\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    interval_start_time AS ts,\n    SUM(CASE WHEN query_type = 'SELECT' THEN calls ELSE 0 END) AS reads,\n    SUM(CASE WHEN query_type IN ('INSERT','UPDATE','DELETE','MERGE') THEN calls ELSE 0 END) AS writes\nFROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\nWHERE interval_start_time > DATEADD(HOUR, -24, CURRENT_TIMESTAMP())\n  AND database_name = 'HT_MON_QS_DB'\nGROUP BY interval_start_time\nORDER BY ts;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ETile 5: Error Rate Over Time\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    interval_start_time AS ts,\n    SUM(calls) AS total_calls,\n    SUM(ARRAY_SIZE(errors)) AS total_errors,\n    ROUND(SUM(ARRAY_SIZE(errors)) / NULLIF(SUM(calls), 0) * 100, 2) AS error_rate_pct\nFROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\nWHERE interval_start_time > DATEADD(HOUR, -24, CURRENT_TIMESTAMP())\n  AND database_name = 'HT_MON_QS_DB'\nGROUP BY interval_start_time\nORDER BY ts;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ETile 6: Throttle Events\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    interval_start_time AS ts,\n    SUM(hybrid_table_requests_throttled_count) AS throttled_requests\nFROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\nWHERE interval_start_time > DATEADD(HOUR, -24, CURRENT_TIMESTAMP())\n  AND database_name = 'HT_MON_QS_DB'\n  AND hybrid_table_requests_throttled_count > 0\nGROUP BY interval_start_time\nORDER BY ts;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EHere is an example of these queries visualized in Grafana (connected to \u003Ccode\u003EAGGREGATE_QUERY_HISTORY\u003C/code\u003E):\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hybrid-tables-monitoring-observability/grafana-dashboard-overview.png?v=853eec33\" alt=\"Dashboard Overview\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe latency percentiles panel shows p99 spikes that may indicate cold cache hits or lock contention:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hybrid-tables-monitoring-observability/grafana-latency-percentiles.png?v=853eec33\" alt=\"Latency Percentiles\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe top query shapes table identifies which parameterized queries dominate your workload:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hybrid-tables-monitoring-observability/grafana-top-query-shapes.png?v=853eec33\" alt=\"Top Query Shapes\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EStep 4: Credit and Storage Monitoring\u003C/h2\u003E\n","\u003Ch3\u003EHT Background Credits (HYBRID_TABLE_USAGE_HISTORY)\u003C/h3\u003E\n","\u003Cp\u003EHybrid Tables consume credits for background operations (compaction, index maintenance) separate from query execution credits:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    object_name AS table_name,\n    DATE_TRUNC('DAY', start_time) AS day,\n    SUM(credits_used) AS daily_credits\nFROM SNOWFLAKE.ACCOUNT_USAGE.HYBRID_TABLE_USAGE_HISTORY\nWHERE start_time > DATEADD(DAY, -30, CURRENT_TIMESTAMP())\nGROUP BY object_name, DATE_TRUNC('DAY', start_time)\nORDER BY day DESC, daily_credits DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStorage Per Table (TABLE_STORAGE_METRICS)\u003C/h3\u003E\n","\u003Cp\u003ETrack storage growth to stay within the 2 TB per-database quota:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    table_catalog AS database_name,\n    table_schema,\n    table_name,\n    ROUND(active_bytes / POWER(1024, 3), 3) AS active_gb,\n    ROUND(time_travel_bytes / POWER(1024, 3), 3) AS time_travel_gb,\n    ROUND(failsafe_bytes / POWER(1024, 3), 3) AS failsafe_gb,\n    ROUND((active_bytes + time_travel_bytes + failsafe_bytes) / POWER(1024, 3), 3) AS total_gb\nFROM SNOWFLAKE.ACCOUNT_USAGE.TABLE_STORAGE_METRICS\nWHERE table_catalog = 'HT_MON_QS_DB'\n  AND deleted IS NULL\nORDER BY active_bytes DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EDatabase-Level Quota Tracking\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    table_catalog AS database_name,\n    ROUND(SUM(active_bytes) / POWER(1024, 4), 4) AS active_tb,\n    ROUND(2.0 - SUM(active_bytes) / POWER(1024, 4), 4) AS remaining_tb,\n    ROUND(SUM(active_bytes) / POWER(1024, 4) / 2.0 * 100, 1) AS quota_used_pct\nFROM SNOWFLAKE.ACCOUNT_USAGE.TABLE_STORAGE_METRICS\nWHERE table_catalog = 'HT_MON_QS_DB'\n  AND deleted IS NULL\nGROUP BY table_catalog;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EQuota Reference:\u003C/strong\u003E Each database has a 2 TB limit for Hybrid Table storage. If \u003Ccode\u003Equota_used_pct\u003C/code\u003E exceeds 80%, plan for data tiering or archival. See \u003Ca href=\"https://www.snowflake.com/en/developers/guides/hybrid-tables-operations-patterns/\"\u003EOperations Patterns\u003C/a\u003E for hot/cold tiering strategies.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EWarehouse Credit Consumption (Query Compute)\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    warehouse_name,\n    DATE_TRUNC('DAY', start_time) AS day,\n    SUM(credits_used) AS query_credits\nFROM SNOWFLAKE.ACCOUNT_USAGE.WAREHOUSE_METERING_HISTORY\nWHERE start_time > DATEADD(DAY, -30, CURRENT_TIMESTAMP())\n  AND warehouse_name ILIKE '%HT%'\nGROUP BY warehouse_name, DATE_TRUNC('DAY', start_time)\nORDER BY day DESC;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EStep 5: Alerting Framework\u003C/h2\u003E\n","\u003Ch3\u003EAlert Infrastructure\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE HT_MON_QS_ROLE;\n\nCREATE OR REPLACE TABLE HT_MON_QS_DB.DATA.alert_log (\n    alert_ts    TIMESTAMP_NTZ DEFAULT CURRENT_TIMESTAMP(),\n    alert_type  VARCHAR(50),\n    severity    VARCHAR(20),\n    message     VARCHAR(2000),\n    metric_value FLOAT\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EAlert 1: Throttling Spike\u003C/h3\u003E\n","\u003Cp\u003EFires when more than 100 requests are throttled in a 10-minute window:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\n\nCREATE OR REPLACE ALERT ht_throttle_alert\n  WAREHOUSE = HT_MON_QS_WH\n  SCHEDULE = '5 MINUTE'\n  IF( EXISTS(\n    SELECT 1\n    FROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\n    WHERE hybrid_table_requests_throttled_count > 100\n      AND interval_start_time > DATEADD(MINUTE, -10, CURRENT_TIMESTAMP())\n  ))\n  THEN\n    INSERT INTO HT_MON_QS_DB.DATA.alert_log (alert_type, severity, message, metric_value)\n    SELECT 'THROTTLE', 'HIGH',\n      'Throttling detected: ' || SUM(hybrid_table_requests_throttled_count)::VARCHAR || ' throttled requests in last 10 min',\n      SUM(hybrid_table_requests_throttled_count)\n    FROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\n    WHERE hybrid_table_requests_throttled_count > 0\n      AND interval_start_time > DATEADD(MINUTE, -10, CURRENT_TIMESTAMP());\n\nALTER ALERT ht_throttle_alert RESUME;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EAlert 2: Latency Regression\u003C/h3\u003E\n","\u003Cp\u003EFires when p99 latency exceeds 500ms (adjust threshold for your SLA):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE ALERT ht_latency_alert\n  WAREHOUSE = HT_MON_QS_WH\n  SCHEDULE = '5 MINUTE'\n  IF( EXISTS(\n    SELECT 1\n    FROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\n    WHERE total_elapsed_time:"p99"::FLOAT > 500\n      AND calls > 5\n      AND interval_start_time > DATEADD(MINUTE, -10, CURRENT_TIMESTAMP())\n      AND database_name = 'HT_MON_QS_DB'\n      AND query_type IN ('SELECT', 'INSERT', 'UPDATE', 'DELETE')\n  ))\n  THEN\n    INSERT INTO HT_MON_QS_DB.DATA.alert_log (alert_type, severity, message, metric_value)\n    SELECT 'LATENCY_REGRESSION', 'MEDIUM',\n      'p99 latency exceeded 500ms for hash ' || query_parameterized_hash || ': ' || ROUND(total_elapsed_time:"p99"::FLOAT, 1)::VARCHAR || 'ms',\n      total_elapsed_time:"p99"::FLOAT\n    FROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\n    WHERE total_elapsed_time:"p99"::FLOAT > 500\n      AND calls > 5\n      AND interval_start_time > DATEADD(MINUTE, -10, CURRENT_TIMESTAMP())\n      AND database_name = 'HT_MON_QS_DB'\n    ORDER BY total_elapsed_time:"p99"::FLOAT DESC\n    LIMIT 1;\n\nALTER ALERT ht_latency_alert RESUME;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EAlert 3: Error Spike\u003C/h3\u003E\n","\u003Cp\u003EFires when error rate exceeds 5% of total calls:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE ALERT ht_error_alert\n  WAREHOUSE = HT_MON_QS_WH\n  SCHEDULE = '5 MINUTE'\n  IF( EXISTS(\n    SELECT 1\n    FROM (\n      SELECT\n        SUM(calls) AS total_calls,\n        SUM(ARRAY_SIZE(errors)) AS total_errors\n      FROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\n      WHERE interval_start_time > DATEADD(MINUTE, -10, CURRENT_TIMESTAMP())\n        AND database_name = 'HT_MON_QS_DB'\n    )\n    WHERE total_errors > 0\n      AND total_errors::FLOAT / NULLIF(total_calls, 0) > 0.05\n  ))\n  THEN\n    INSERT INTO HT_MON_QS_DB.DATA.alert_log (alert_type, severity, message, metric_value)\n    SELECT 'ERROR_SPIKE', 'HIGH',\n      'Error rate exceeded 5%: ' || SUM(ARRAY_SIZE(errors))::VARCHAR || ' errors out of ' || SUM(calls)::VARCHAR || ' calls',\n      ROUND(SUM(ARRAY_SIZE(errors))::FLOAT / NULLIF(SUM(calls), 0) * 100, 2)\n    FROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\n    WHERE interval_start_time > DATEADD(MINUTE, -10, CURRENT_TIMESTAMP())\n      AND database_name = 'HT_MON_QS_DB';\n\nALTER ALERT ht_error_alert RESUME;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EAlert 4: Storage Quota Warning\u003C/h3\u003E\n","\u003Cp\u003EFires when database storage exceeds 80% of the 2 TB quota:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE ALERT ht_storage_alert\n  WAREHOUSE = HT_MON_QS_WH\n  SCHEDULE = 'USING CRON 0 */6 * * * UTC'\n  IF( EXISTS(\n    SELECT 1\n    FROM (\n      SELECT SUM(active_bytes) / POWER(1024, 4) AS active_tb\n      FROM SNOWFLAKE.ACCOUNT_USAGE.TABLE_STORAGE_METRICS\n      WHERE table_catalog = 'HT_MON_QS_DB'\n        AND deleted IS NULL\n    )\n    WHERE active_tb > 1.6\n  ))\n  THEN\n    INSERT INTO HT_MON_QS_DB.DATA.alert_log (alert_type, severity, message, metric_value)\n    SELECT 'STORAGE_QUOTA', 'HIGH',\n      'HT database storage at ' || ROUND(SUM(active_bytes) / POWER(1024, 4) * 100 / 2.0, 1)::VARCHAR || '% of 2TB quota',\n      SUM(active_bytes) / POWER(1024, 4)\n    FROM SNOWFLAKE.ACCOUNT_USAGE.TABLE_STORAGE_METRICS\n    WHERE table_catalog = 'HT_MON_QS_DB'\n      AND deleted IS NULL;\n\nALTER ALERT ht_storage_alert RESUME;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EReview Alerts\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESHOW ALERTS;\nSELECT * FROM HT_MON_QS_DB.DATA.alert_log ORDER BY alert_ts DESC LIMIT 20;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EStep 6: Custom Dashboards and Observability Apps\u003C/h2\u003E\n","\u003Cp\u003EDuration: 10\u003C/p\u003E\n","\u003Cp\u003EBuild custom monitoring experiences using Snowflake-native application frameworks — no external infrastructure required.\u003C/p\u003E\n","\u003Ch3\u003EOption A: Streamlit in Snowflake\u003C/h3\u003E\n","\u003Cp\u003EBuild an interactive monitoring dashboard that runs entirely within your Snowflake account.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hybrid-tables-monitoring-observability/streamlit-dashboard-overview.png?v=853eec33\" alt=\"Streamlit Monitoring Dashboard\"\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport streamlit as st\nfrom snowflake.snowpark.context import get_active_session\n\nsession = get_active_session()\n\nst.set_page_config(page_title="Hybrid Table Monitor", layout="wide")\nst.title("🔍 Hybrid Table Monitoring Dashboard")\n\nlookback = st.selectbox("Lookback Window", ["6 hours", "24 hours", "7 days"], index=1)\nhours_map = {"6 hours": 6, "24 hours": 24, "7 days": 168}\nhours = hours_map[lookback]\n\nqps_df = session.sql(f"""\n    SELECT interval_start_time AS ts,\n           SUM(calls) / 60.0 AS qps\n    FROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\n    WHERE interval_start_time > DATEADD(HOUR, -{hours}, CURRENT_TIMESTAMP())\n    GROUP BY interval_start_time ORDER BY ts\n""").to_pandas()\n\nlatency_df = session.sql(f"""\n    SELECT interval_start_time AS ts,\n           AVG(total_elapsed_time:"median"::FLOAT) AS p50_ms,\n           AVG(total_elapsed_time:"p90"::FLOAT) AS p90_ms,\n           AVG(total_elapsed_time:"p99"::FLOAT) AS p99_ms\n    FROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\n    WHERE interval_start_time > DATEADD(HOUR, -{hours}, CURRENT_TIMESTAMP())\n      AND calls > 0\n    GROUP BY interval_start_time ORDER BY ts\n""").to_pandas()\n\nthrottle_df = session.sql(f"""\n    SELECT SUM(hybrid_table_requests_throttled_count) AS throttled\n    FROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\n    WHERE interval_start_time > DATEADD(HOUR, -{hours}, CURRENT_TIMESTAMP())\n""").to_pandas()\n\nm1, m2, m3 = st.columns(3)\nwith m1:\n    total = int(qps_df["QPS"].sum() * 60) if not qps_df.empty else 0\n    st.metric("Total Queries", f"{total:,}")\nwith m2:\n    throttled = int(throttle_df["THROTTLED"].iloc[0] or 0)\n    st.metric("Throttled Requests", f"{throttled:,}")\nwith m3:\n    st.metric("Avg Error Rate", "—")\n\ncol1, col2 = st.columns(2)\nwith col1:\n    st.subheader("Throughput (QPS)")\n    st.area_chart(qps_df.set_index("TS")["QPS"])\nwith col2:\n    st.subheader("Latency Percentiles (ms)")\n    st.line_chart(latency_df.set_index("TS")[["P50_MS", "P90_MS", "P99_MS"]])\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EDeploy this as a Streamlit in Snowflake app for a zero-infrastructure monitoring solution that your team can access from the Snowsight UI.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote on OBJECT accessor syntax:\u003C/strong\u003E When accessing keys inside OBJECT columns like \u003Ccode\u003Etotal_elapsed_time\u003C/code\u003E, use double-quote notation (\u003Ccode\u003Etotal_elapsed_time:"median"::FLOAT\u003C/code\u003E) or bracket notation (\u003Ccode\u003Etotal_elapsed_time['median']::FLOAT\u003C/code\u003E). Single-quote notation may fail in some connectors.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EOption B: Snowflake Apps (Snowflake Native App Framework)\u003C/h3\u003E\n","\u003Cp\u003EFor a packaged, shareable monitoring experience, build a \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/native-apps/native-apps-about\"\u003ESnowflake Native App\u003C/a\u003E that:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EBundles the dashboard SQL, alerts, and tasks into a single installable package\u003C/li\u003E\u003Cli\u003ECan be shared across accounts (e.g., from a central platform team to application teams)\u003C/li\u003E\u003Cli\u003EIncludes Streamlit pages for visualization and a setup wizard for configuring database filters\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThis approach works well when you want to standardize HT monitoring across multiple teams or accounts.\u003C/p\u003E\n","\u003Ch3\u003EOption C: OpenTelemetry Export\u003C/h3\u003E\n","\u003Cp\u003EFor teams already invested in OpenTelemetry, export HT metrics as OTLP data:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003ESnowflake Task\u003C/strong\u003E exports metrics to a stage on a schedule (e.g., every 5 minutes)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EOpenTelemetry Collector\u003C/strong\u003E with the \u003Ccode\u003Efilereceiver\u003C/code\u003E reads staged JSON files\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EYour OTLP backend\u003C/strong\u003E (Jaeger, Honeycomb, Grafana Tempo) receives the traces/metrics\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThis integrates HT monitoring into your existing distributed tracing infrastructure alongside application-level spans.\u003C/p\u003E\n","\u003Ch3\u003EChoosing the Right Approach\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EApproach\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EBest For\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPros\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ECons\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ESnowflake Worksheets\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EQuick ad-hoc queries\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EZero config, native\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENo persistent dashboards\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EStreamlit in Snowflake\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECustom team dashboards\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERich interactivity, runs in SF\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERequires SiS entitlement\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ESnowflake Native App\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMulti-account standardization\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EShareable, versioned\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMore setup complexity\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EGrafana/Datadog\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUnified observability stack\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECorrelate with app metrics\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EExternal dependency\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EOTLP Export\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDistributed tracing teams\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFits existing pipelines\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECustom integration work\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EStep 7: External Observability Integration\u003C/h2\u003E\n","\u003Cp\u003EFor teams using Datadog, Grafana, or Prometheus, export HT metrics from Snowflake on a schedule.\u003C/p\u003E\n","\u003Ch3\u003EDatadog Integration\u003C/h3\u003E\n","\u003Cp\u003EWith the \u003Ca href=\"https://docs.datadoghq.com/integrations/snowflake/\"\u003ESnowflake Datadog integration\u003C/a\u003E, you can query \u003Ccode\u003EAGGREGATE_QUERY_HISTORY\u003C/code\u003E directly. Configure a custom query in the integration YAML:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yaml\"\u003E# Example Datadog custom query (datadog-agent snowflake.d/conf.yaml)\ncustom_queries:\n  - query: >\n      SELECT\n        SUM(calls) / 60.0 AS qps,\n        AVG(total_elapsed_time:"p99"::FLOAT) AS p99_ms,\n        SUM(hybrid_table_requests_throttled_count) AS throttled\n      FROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\n      WHERE interval_start_time > DATEADD(MINUTE, -5, CURRENT_TIMESTAMP())\n        AND database_name = 'YOUR_HT_DATABASE'\n    columns:\n      - name: snowflake.hybrid_table.qps\n        type: gauge\n      - name: snowflake.hybrid_table.p99_ms\n        type: gauge\n      - name: snowflake.hybrid_table.throttled\n        type: count\n    tags:\n      - service:hybrid-tables\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EGrafana Integration\u003C/h3\u003E\n","\u003Cp\u003EUse the \u003Ca href=\"https://github.com/michelin/snowflake-grafana-datasource\"\u003EMichelin Snowflake Grafana data source plugin\u003C/a\u003E (open-source, community-supported) to query \u003Ccode\u003EAGGREGATE_QUERY_HISTORY\u003C/code\u003E directly. Configure the data source with your account identifier, warehouse, and credentials, then build panels using the same SQL from Step 3.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E The official Grafana Snowflake plugin requires a Grafana Enterprise license. The Michelin community plugin is free and supports password, key-pair, and OAuth authentication.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hybrid-tables-monitoring-observability/grafana-qps.png?v=853eec33\" alt=\"Grafana QPS Panel\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hybrid-tables-monitoring-observability/grafana-read-write-mix.png?v=853eec33\" alt=\"Grafana Read/Write Mix\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EMonitoring Playbook\u003C/h2\u003E\n","\u003Cp\u003EWhen alerts fire, use this decision tree:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EAlert\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EFirst Response\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ERoot Cause Investigation\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ETHROTTLE\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECheck if workload has spiked vs normal\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EReview \u003Ccode\u003Ecalls\u003C/code\u003E by \u003Ccode\u003Equery_parameterized_hash\u003C/code\u003E — one runaway loop? Distribute across databases if sustained.\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ELATENCY_REGRESSION\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECheck compilation_time vs execution_time\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHigh compile_time → plan cache miss (bound variables, schema qualification). High exec_time → check Query Profile for full table scans or lock contention.\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EERROR_SPIKE\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EInspect error array contents\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECommon: PK violations (duplicate inserts), lock timeouts (contention), quota exceeded.\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ESTORAGE_QUOTA\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECheck per-table storage\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EArchive cold data to standard table (see \u003Ca href=\"https://www.snowflake.com/en/developers/guides/hybrid-tables-operations-patterns/\"\u003EOperations Patterns\u003C/a\u003E).\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EUseful Diagnostic Queries\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFind the noisiest query shape:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    query_parameterized_hash,\n    ANY_VALUE(query_text) AS query_sample,\n    SUM(calls) AS total_calls,\n    AVG(total_elapsed_time:"avg"::FLOAT) AS avg_ms\nFROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\nWHERE interval_start_time > DATEADD(HOUR, -1, CURRENT_TIMESTAMP())\nGROUP BY query_parameterized_hash\nORDER BY total_calls DESC\nLIMIT 5;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFind queries with high tail latency (p99/median ratio > 5x):\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    query_parameterized_hash,\n    ANY_VALUE(LEFT(query_text, 100)) AS query_sample,\n    SUM(calls) AS total_calls,\n    AVG(total_elapsed_time:"median"::FLOAT) AS p50_ms,\n    AVG(total_elapsed_time:"p99"::FLOAT) AS p99_ms,\n    ROUND(AVG(total_elapsed_time:"p99"::FLOAT) / NULLIF(AVG(total_elapsed_time:"median"::FLOAT), 0), 1) AS tail_ratio\nFROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\nWHERE interval_start_time > DATEADD(HOUR, -6, CURRENT_TIMESTAMP())\n  AND calls > 10\nGROUP BY query_parameterized_hash\nHAVING tail_ratio > 5\nORDER BY tail_ratio DESC\nLIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EHigh tail ratio\u003C/strong\u003E means inconsistent performance. Common causes: some lookups hit compacted data (fast) while others hit recently written data (slower), or intermittent lock contention from concurrent writes.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EGet Started Faster with Cortex Code\u003C/h2\u003E\n","\u003Cp\u003EDuration: 1\u003C/p\u003E\n","\u003Cp\u003EUse these prompts in \u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code\"\u003ECortex Code\u003C/a\u003E to apply this guide to your own workload:\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E"Run an AGGREGATE_QUERY_HISTORY analysis for my Hybrid Table workload over the past 7 days. Identify my top 3 performance issues and provide specific remediation steps."\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E"I have a Hybrid Table called [TABLE_NAME]. Adapt the 6 monitoring dashboard queries from the HT Monitoring quickstart to my schema and return ready-to-run SQL."\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E"My Hybrid Table queries have high compilation time. Diagnose the root cause and rewrite the top offending queries to use bound variables."\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECleanup\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\nALTER ALERT IF EXISTS ht_throttle_alert SUSPEND;\nALTER ALERT IF EXISTS ht_latency_alert SUSPEND;\nALTER ALERT IF EXISTS ht_error_alert SUSPEND;\nALTER ALERT IF EXISTS ht_storage_alert SUSPEND;\nDROP ALERT IF EXISTS ht_throttle_alert;\nDROP ALERT IF EXISTS ht_latency_alert;\nDROP ALERT IF EXISTS ht_error_alert;\nDROP ALERT IF EXISTS ht_storage_alert;\nDROP DATABASE IF EXISTS HT_MON_QS_DB;\nDROP WAREHOUSE IF EXISTS HT_MON_QS_WH;\nDROP ROLE IF EXISTS HT_MON_QS_ROLE;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Cp\u003EYou can now:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EFind HT telemetry in AGGREGATE_QUERY_HISTORY (not QUERY_HISTORY)\u003C/li\u003E\u003Cli\u003EExtract latency percentiles (median, p90, p99) from OBJECT-typed columns\u003C/li\u003E\u003Cli\u003EBuild dashboards with throughput, latency, read/write mix, and error rate tiles\u003C/li\u003E\u003Cli\u003ETrack HT credit consumption and storage against the 2 TB quota\u003C/li\u003E\u003Cli\u003ECreate multi-signal alerts (throttle, latency, errors, storage)\u003C/li\u003E\u003Cli\u003EExport metrics to external observability platforms (Datadog, Grafana)\u003C/li\u003E\u003Cli\u003EBuild custom monitoring apps with Streamlit in Snowflake or Snowflake Native Apps\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENeed help with your Hybrid Table architecture?\u003C/strong\u003E Book a 30-minute session with our specialist team to discuss your use case, review your schema design, or troubleshoot performance: \u003Ca href=\"https://calendar.app.google/cGfVnKFe7xbeDqDo8\"\u003ESchedule a session\u003C/a\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-hybrid-best-practices\"\u003EHybrid Tables Best Practices\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/sql-reference/account-usage/aggregate_query_history\"\u003EAGGREGATE_QUERY_HISTORY Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/developers/guides/hybrid-tables-write-optimization/\"\u003EOptimizing Writes to Hybrid Tables\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/developers/guides/hybrid-tables-operations-patterns/\"\u003EOperations Patterns for Hybrid Tables\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/developers/guides/hybrid-tables-secondary-index-design/\"\u003ESecondary Index Design for Hybrid Tables\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/alerts\"\u003ESnowflake Alerts Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/streamlit/about-streamlit\"\u003EStreamlit in Snowflake\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/native-apps/native-apps-about\"\u003ESnowflake Native App Framework\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/michelin/snowflake-grafana-datasource\"\u003EMichelin Snowflake Grafana Plugin\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Monitoring and Observability for Hybrid Tables","isDeveloperGuidesPage":false,"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!--\nkeywords: hybrid table, monitoring, observability, AGGREGATE_QUERY_HISTORY, latency, p99, throttling, credits, storage, quota, alert, dashboard, Datadog, Grafana, Streamlit, Native App, Unistore, OLTP\nrelated_concepts: AGGREGATE_QUERY_HISTORY, HYBRID_TABLE_USAGE_HISTORY, TABLE_STORAGE_METRICS, query_parameterized_hash, hybrid_table_requests_throttled_count, CREATE ALERT, METERING_HISTORY\nprerequisite_guides: getting-started-with-hybrid-tables\nskill_level: intermediate\nestimated_time_minutes: 45\nsnowflake_features: hybrid_tables, alerts, aggregate_query_history, account_usage\n--\u003E\n\n# Monitoring and Observability for Hybrid Tables\n\u003C!-- ------------------------ --\u003E\n## Overview\n\n\u003E **Note on Production Workloads:** The monitoring queries in this guide use `SNOWFLAKE.ACCOUNT_USAGE` views which have up to 3 hours of latency. For real-time diagnosis of individual queries, use Query Profile in Snowsight.\n\nHybrid Table workloads are invisible to traditional Snowflake monitoring. Queries completing in under one second do not appear in `QUERY_HISTORY`, and standard dashboards built on that view will show zero activity even while thousands of operations per second are executing successfully.\n\nThis guide builds a complete observability stack for Hybrid Table workloads:\n\n1. Understanding the monitoring gap and where to find HT telemetry\n2. Extracting latency percentiles, throughput, and error rates from AGGREGATE_QUERY_HISTORY\n3. Building monitoring dashboards for ongoing visibility\n4. Tracking credits and storage against quotas\n5. Creating multi-signal alerts (throttling, latency regression, error spikes)\n6. Building custom monitoring apps (Streamlit, Snowflake Native Apps, OpenTelemetry)\n7. Exporting metrics to external tools (Datadog, Grafana, Prometheus)\n\n### What You Will Learn\n\n- Why `QUERY_HISTORY` misses most HT activity and what to use instead\n- How to read OBJECT-typed columns (avg, median, p90, p99, max) in AGGREGATE_QUERY_HISTORY\n- How to build monitoring dashboards with latency trends and throughput\n- How to track HT credit consumption and storage against the 2 TB per-database quota\n- How to create alerts for throttling, latency regression, and error spikes\n- How to export HT metrics to external observability platforms\n- How to build custom monitoring dashboards with Streamlit in Snowflake\n\n### Prerequisites\n\n- A Snowflake paid account in an AWS or Azure commercial region\n- Familiarity with [Hybrid Tables](https://docs.snowflake.com/en/user-guide/tables-hybrid) and basic SQL\n- ACCOUNTADMIN role (used in this guide for simplicity) or any role with access to `SNOWFLAKE.ACCOUNT_USAGE` views and the ability to create alerts\n- An existing Hybrid Table workload with some query activity (or run the setup below to simulate one)\n\n\u003E **Note on Roles:** This quickstart uses ACCOUNTADMIN for simplicity. In production, grant `IMPORTED PRIVILEGES` on the `SNOWFLAKE` database to a custom role and use that instead. Any role with access to `SNOWFLAKE.ACCOUNT_USAGE` views can run the monitoring queries.\n\n\u003C!-- ------------------------ --\u003E\n## Setup\n\n```sql\nUSE ROLE ACCOUNTADMIN;\n\nCREATE OR REPLACE ROLE HT_MON_QS_ROLE;\nGRANT ROLE HT_MON_QS_ROLE TO ROLE ACCOUNTADMIN;\n\nCREATE OR REPLACE WAREHOUSE HT_MON_QS_WH\n  WAREHOUSE_SIZE = XSMALL\n  AUTO_SUSPEND = 300\n  AUTO_RESUME = TRUE;\nGRANT OWNERSHIP ON WAREHOUSE HT_MON_QS_WH TO ROLE HT_MON_QS_ROLE;\n\nCREATE OR REPLACE DATABASE HT_MON_QS_DB;\nGRANT OWNERSHIP ON DATABASE HT_MON_QS_DB TO ROLE HT_MON_QS_ROLE;\n\nUSE ROLE HT_MON_QS_ROLE;\nCREATE OR REPLACE SCHEMA HT_MON_QS_DB.DATA;\nUSE WAREHOUSE HT_MON_QS_WH;\nUSE DATABASE HT_MON_QS_DB;\nUSE SCHEMA DATA;\n```\n\n### Create a Sample Workload Table\n\n```sql\nCREATE OR REPLACE HYBRID TABLE session_state (\n    session_id   VARCHAR(36)    NOT NULL,\n    user_id      NUMBER         NOT NULL,\n    device_type  VARCHAR(20)    NOT NULL,\n    last_active  TIMESTAMP_NTZ  NOT NULL,\n    payload      VARIANT,\n    PRIMARY KEY (session_id)\n);\n\nCREATE INDEX idx_session_user ON session_state (user_id);\n```\n\n### Generate Sample Data\n\n```sql\nINSERT INTO session_state\nSELECT\n    UUID_STRING() AS session_id,\n    UNIFORM(1, 10000, RANDOM()) AS user_id,\n    ARRAY_CONSTRUCT('mobile','desktop','tablet')[UNIFORM(0,2,RANDOM())]::VARCHAR AS device_type,\n    DATEADD(MINUTE, -UNIFORM(1, 1440, RANDOM()), CURRENT_TIMESTAMP())::TIMESTAMP_NTZ AS last_active,\n    OBJECT_CONSTRUCT('ip', '10.0.' || UNIFORM(0,255,RANDOM())::VARCHAR || '.' || UNIFORM(0,255,RANDOM())::VARCHAR) AS payload\nFROM TABLE(GENERATOR(ROWCOUNT =\u003E 50000));\n```\n\n### Simulate Workload Activity\n\nRun several queries to generate telemetry in AGGREGATE_QUERY_HISTORY (visible after up to 3 hours):\n\n```sql\nSELECT * FROM session_state WHERE session_id = (SELECT session_id FROM session_state LIMIT 1);\nSELECT * FROM session_state WHERE user_id = 42;\nUPDATE session_state SET last_active = CURRENT_TIMESTAMP()::TIMESTAMP_NTZ WHERE session_id = (SELECT session_id FROM session_state LIMIT 1);\nDELETE FROM session_state WHERE last_active \u003C DATEADD(DAY, -1, CURRENT_TIMESTAMP())::TIMESTAMP_NTZ;\n```\n\n\u003E **Note:** AGGREGATE_QUERY_HISTORY has up to 3 hours of latency. If you are running this guide for the first time, you may need to wait before the monitoring queries return data. The SQL patterns are valid regardless — apply them to your own existing HT workloads for immediate results.\n\n\u003C!-- ------------------------ --\u003E\n## Step 1: The Monitoring Gap\n\n### Why QUERY_HISTORY Misses Hybrid Table Activity\n\n```sql\nUSE ROLE ACCOUNTADMIN;\n\nSELECT COUNT(*) AS queries_in_history\nFROM SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY\nWHERE start_time \u003E DATEADD(DAY, -1, CURRENT_TIMESTAMP())\n  AND total_elapsed_time \u003C 1000\n  AND query_type IN ('SELECT', 'INSERT', 'UPDATE', 'DELETE', 'MERGE');\n```\n\nStandard `QUERY_HISTORY` captures individual query executions — but queries completing in under ~1 second are often excluded or sampled. For OLTP workloads executing thousands of sub-second operations per minute, this creates a blind spot.\n\n### Where HT Telemetry Lives\n\n| View | What It Shows | Latency | Granularity |\n|------|--------------|---------|-------------|\n| `AGGREGATE_QUERY_HISTORY` | All queries (including sub-second), aggregated per minute per query shape | Up to 3 hours | 1-minute windows |\n| `HYBRID_TABLE_USAGE_HISTORY` | Credits consumed by HT background operations | Up to 3 hours | Per-table, hourly |\n| `TABLE_STORAGE_METRICS` | Storage bytes per table (active, time travel, failsafe) | Up to 3 hours | Per-table |\n| `METERING_HISTORY` | Overall warehouse credits (includes HT query compute) | Up to 3 hours | Per-warehouse, hourly |\n| Query Profile (Snowsight) | Real-time single-query diagnosis | Immediate | Per-query |\n\n### The Key View: AGGREGATE_QUERY_HISTORY\n\nThis view captures **every** query regardless of duration, aggregated into 1-minute intervals grouped by `query_parameterized_hash`. Each row represents all executions of a specific query shape within a 1-minute window.\n\n```sql\nSELECT\n    interval_start_time,\n    query_parameterized_hash,\n    query_text,\n    calls,\n    total_elapsed_time,\n    hybrid_table_requests_throttled_count\nFROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\nWHERE interval_start_time \u003E DATEADD(DAY, -1, CURRENT_TIMESTAMP())\nORDER BY interval_start_time DESC\nLIMIT 5;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Step 2: Reading AGGREGATE_QUERY_HISTORY\n\n### Understanding OBJECT-Typed Columns\n\nMost numeric columns in AGGREGATE_QUERY_HISTORY are **OBJECT** typed, containing statistical aggregates:\n\n```json\n{\n  \"avg\": 23.5,\n  \"count\": 150,\n  \"max\": 89.0,\n  \"min\": 12.0,\n  \"median\": 21.0,\n  \"p90\": 45.0,\n  \"p99\": 78.0,\n  \"p99.9\": 85.0,\n  \"sum\": 3525.0\n}\n```\n\nExtract specific percentiles using the `:` operator:\n\n```sql\nSELECT\n    interval_start_time,\n    query_parameterized_hash,\n    ANY_VALUE(query_text) AS sample_query,\n    SUM(calls) AS total_calls,\n    AVG(total_elapsed_time:\"avg\"::FLOAT) AS avg_latency_ms,\n    MAX(total_elapsed_time:\"median\"::FLOAT) AS p50_latency_ms,\n    MAX(total_elapsed_time:\"p90\"::FLOAT) AS p90_latency_ms,\n    MAX(total_elapsed_time:\"p99\"::FLOAT) AS p99_latency_ms,\n    MAX(total_elapsed_time:\"max\"::FLOAT) AS max_latency_ms\nFROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\nWHERE interval_start_time \u003E DATEADD(HOUR, -6, CURRENT_TIMESTAMP())\nGROUP BY interval_start_time, query_parameterized_hash\nORDER BY total_calls DESC\nLIMIT 20;\n```\n\n### Compilation vs Execution Time Split\n\nFor HT workloads, high compilation time relative to execution time indicates plan cache misses:\n\n```sql\nSELECT\n    query_parameterized_hash,\n    ANY_VALUE(query_text) AS sample_query,\n    SUM(calls) AS total_calls,\n    AVG(compilation_time:\"avg\"::FLOAT) AS avg_compile_ms,\n    AVG(execution_time:\"avg\"::FLOAT) AS avg_exec_ms,\n    ROUND(AVG(compilation_time:\"avg\"::FLOAT) /\n      NULLIF(AVG(total_elapsed_time:\"avg\"::FLOAT), 0) * 100, 1) AS compile_pct\nFROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\nWHERE interval_start_time \u003E DATEADD(DAY, -1, CURRENT_TIMESTAMP())\n  AND calls \u003E 10\nGROUP BY query_parameterized_hash\nHAVING AVG(compilation_time:\"avg\"::FLOAT) \u003E 50\nORDER BY compile_pct DESC\nLIMIT 10;\n```\n\n\u003E **Interpretation:** If `compile_pct` is above 50%, the query is spending more time compiling than executing. This usually means plan cache is not being utilized — check that you are using bound variables and consistent schema qualification. See [Write Optimization](https://www.snowflake.com/en/developers/guides/hybrid-tables-write-optimization/) for details.\n\n### Error Analysis\n\nThe `ERRORS` column is an ARRAY of error objects. Query shapes with recurring errors:\n\n```sql\nSELECT\n    query_parameterized_hash,\n    ANY_VALUE(query_text) AS sample_query,\n    SUM(calls) AS total_calls,\n    SUM(ARRAY_SIZE(errors)) AS total_errors,\n    ROUND(SUM(ARRAY_SIZE(errors)) / NULLIF(SUM(calls), 0) * 100, 2) AS error_rate_pct\nFROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\nWHERE interval_start_time \u003E DATEADD(DAY, -1, CURRENT_TIMESTAMP())\n  AND ARRAY_SIZE(errors) \u003E 0\nGROUP BY query_parameterized_hash\nORDER BY total_errors DESC\nLIMIT 10;\n```\n\n### Throttling Detection\n\nWhen HT request throughput exceeds the database-level quota (~16K ops/sec), requests are throttled:\n\n```sql\nSELECT\n    interval_start_time,\n    SUM(calls) AS total_calls,\n    SUM(hybrid_table_requests_throttled_count) AS throttled_count,\n    ROUND(SUM(hybrid_table_requests_throttled_count) / NULLIF(SUM(calls), 0) * 100, 2) AS throttle_pct\nFROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\nWHERE interval_start_time \u003E DATEADD(HOUR, -24, CURRENT_TIMESTAMP())\nGROUP BY interval_start_time\nHAVING throttled_count \u003E 0\nORDER BY interval_start_time DESC;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Step 3: Monitoring Dashboard Queries\n\nThese queries can be used in any visualization tool: Streamlit in Snowflake (see Step 6), Grafana (see Step 7), or any BI tool connected to your account.\n\n### Tile 1: Throughput Over Time (QPS)\n\n```sql\nSELECT\n    interval_start_time AS ts,\n    SUM(calls) / 60.0 AS queries_per_second\nFROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\nWHERE interval_start_time \u003E DATEADD(HOUR, -24, CURRENT_TIMESTAMP())\n  AND database_name = 'HT_MON_QS_DB'\nGROUP BY interval_start_time\nORDER BY ts;\n```\n\n### Tile 2: Latency Percentiles Over Time\n\n```sql\nSELECT\n    interval_start_time AS ts,\n    AVG(total_elapsed_time:\"median\"::FLOAT) AS p50_ms,\n    AVG(total_elapsed_time:\"p90\"::FLOAT) AS p90_ms,\n    AVG(total_elapsed_time:\"p99\"::FLOAT) AS p99_ms\nFROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\nWHERE interval_start_time \u003E DATEADD(HOUR, -24, CURRENT_TIMESTAMP())\n  AND database_name = 'HT_MON_QS_DB'\n  AND query_type IN ('SELECT', 'INSERT', 'UPDATE', 'DELETE', 'MERGE')\nGROUP BY interval_start_time\nORDER BY ts;\n```\n\n### Tile 3: Top 10 Query Shapes by Volume\n\n```sql\nSELECT\n    query_parameterized_hash,\n    ANY_VALUE(LEFT(query_text, 80)) AS query_preview,\n    SUM(calls) AS total_calls,\n    AVG(total_elapsed_time:\"avg\"::FLOAT) AS avg_ms,\n    MAX(total_elapsed_time:\"p99\"::FLOAT) AS p99_ms\nFROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\nWHERE interval_start_time \u003E DATEADD(DAY, -1, CURRENT_TIMESTAMP())\n  AND database_name = 'HT_MON_QS_DB'\nGROUP BY query_parameterized_hash\nORDER BY total_calls DESC\nLIMIT 10;\n```\n\n### Tile 4: Read vs Write Mix\n\n```sql\nSELECT\n    interval_start_time AS ts,\n    SUM(CASE WHEN query_type = 'SELECT' THEN calls ELSE 0 END) AS reads,\n    SUM(CASE WHEN query_type IN ('INSERT','UPDATE','DELETE','MERGE') THEN calls ELSE 0 END) AS writes\nFROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\nWHERE interval_start_time \u003E DATEADD(HOUR, -24, CURRENT_TIMESTAMP())\n  AND database_name = 'HT_MON_QS_DB'\nGROUP BY interval_start_time\nORDER BY ts;\n```\n\n### Tile 5: Error Rate Over Time\n\n```sql\nSELECT\n    interval_start_time AS ts,\n    SUM(calls) AS total_calls,\n    SUM(ARRAY_SIZE(errors)) AS total_errors,\n    ROUND(SUM(ARRAY_SIZE(errors)) / NULLIF(SUM(calls), 0) * 100, 2) AS error_rate_pct\nFROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\nWHERE interval_start_time \u003E DATEADD(HOUR, -24, CURRENT_TIMESTAMP())\n  AND database_name = 'HT_MON_QS_DB'\nGROUP BY interval_start_time\nORDER BY ts;\n```\n\n### Tile 6: Throttle Events\n\n```sql\nSELECT\n    interval_start_time AS ts,\n    SUM(hybrid_table_requests_throttled_count) AS throttled_requests\nFROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\nWHERE interval_start_time \u003E DATEADD(HOUR, -24, CURRENT_TIMESTAMP())\n  AND database_name = 'HT_MON_QS_DB'\n  AND hybrid_table_requests_throttled_count \u003E 0\nGROUP BY interval_start_time\nORDER BY ts;\n```\n\nHere is an example of these queries visualized in Grafana (connected to `AGGREGATE_QUERY_HISTORY`):\n\n![Dashboard Overview](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hybrid-tables-monitoring-observability/grafana-dashboard-overview.png?v=853eec33)\n\nThe latency percentiles panel shows p99 spikes that may indicate cold cache hits or lock contention:\n\n![Latency Percentiles](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hybrid-tables-monitoring-observability/grafana-latency-percentiles.png?v=853eec33)\n\nThe top query shapes table identifies which parameterized queries dominate your workload:\n\n![Top Query Shapes](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hybrid-tables-monitoring-observability/grafana-top-query-shapes.png?v=853eec33)\n\n\u003C!-- ------------------------ --\u003E\n## Step 4: Credit and Storage Monitoring\n\n### HT Background Credits (HYBRID_TABLE_USAGE_HISTORY)\n\nHybrid Tables consume credits for background operations (compaction, index maintenance) separate from query execution credits:\n\n```sql\nSELECT\n    object_name AS table_name,\n    DATE_TRUNC('DAY', start_time) AS day,\n    SUM(credits_used) AS daily_credits\nFROM SNOWFLAKE.ACCOUNT_USAGE.HYBRID_TABLE_USAGE_HISTORY\nWHERE start_time \u003E DATEADD(DAY, -30, CURRENT_TIMESTAMP())\nGROUP BY object_name, DATE_TRUNC('DAY', start_time)\nORDER BY day DESC, daily_credits DESC;\n```\n\n### Storage Per Table (TABLE_STORAGE_METRICS)\n\nTrack storage growth to stay within the 2 TB per-database quota:\n\n```sql\nSELECT\n    table_catalog AS database_name,\n    table_schema,\n    table_name,\n    ROUND(active_bytes / POWER(1024, 3), 3) AS active_gb,\n    ROUND(time_travel_bytes / POWER(1024, 3), 3) AS time_travel_gb,\n    ROUND(failsafe_bytes / POWER(1024, 3), 3) AS failsafe_gb,\n    ROUND((active_bytes + time_travel_bytes + failsafe_bytes) / POWER(1024, 3), 3) AS total_gb\nFROM SNOWFLAKE.ACCOUNT_USAGE.TABLE_STORAGE_METRICS\nWHERE table_catalog = 'HT_MON_QS_DB'\n  AND deleted IS NULL\nORDER BY active_bytes DESC;\n```\n\n### Database-Level Quota Tracking\n\n```sql\nSELECT\n    table_catalog AS database_name,\n    ROUND(SUM(active_bytes) / POWER(1024, 4), 4) AS active_tb,\n    ROUND(2.0 - SUM(active_bytes) / POWER(1024, 4), 4) AS remaining_tb,\n    ROUND(SUM(active_bytes) / POWER(1024, 4) / 2.0 * 100, 1) AS quota_used_pct\nFROM SNOWFLAKE.ACCOUNT_USAGE.TABLE_STORAGE_METRICS\nWHERE table_catalog = 'HT_MON_QS_DB'\n  AND deleted IS NULL\nGROUP BY table_catalog;\n```\n\n\u003E **Quota Reference:** Each database has a 2 TB limit for Hybrid Table storage. If `quota_used_pct` exceeds 80%, plan for data tiering or archival. See [Operations Patterns](https://www.snowflake.com/en/developers/guides/hybrid-tables-operations-patterns/) for hot/cold tiering strategies.\n\n### Warehouse Credit Consumption (Query Compute)\n\n```sql\nSELECT\n    warehouse_name,\n    DATE_TRUNC('DAY', start_time) AS day,\n    SUM(credits_used) AS query_credits\nFROM SNOWFLAKE.ACCOUNT_USAGE.WAREHOUSE_METERING_HISTORY\nWHERE start_time \u003E DATEADD(DAY, -30, CURRENT_TIMESTAMP())\n  AND warehouse_name ILIKE '%HT%'\nGROUP BY warehouse_name, DATE_TRUNC('DAY', start_time)\nORDER BY day DESC;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Step 5: Alerting Framework\n\n### Alert Infrastructure\n\n```sql\nUSE ROLE HT_MON_QS_ROLE;\n\nCREATE OR REPLACE TABLE HT_MON_QS_DB.DATA.alert_log (\n    alert_ts    TIMESTAMP_NTZ DEFAULT CURRENT_TIMESTAMP(),\n    alert_type  VARCHAR(50),\n    severity    VARCHAR(20),\n    message     VARCHAR(2000),\n    metric_value FLOAT\n);\n```\n\n### Alert 1: Throttling Spike\n\nFires when more than 100 requests are throttled in a 10-minute window:\n\n```sql\nUSE ROLE ACCOUNTADMIN;\n\nCREATE OR REPLACE ALERT ht_throttle_alert\n  WAREHOUSE = HT_MON_QS_WH\n  SCHEDULE = '5 MINUTE'\n  IF( EXISTS(\n    SELECT 1\n    FROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\n    WHERE hybrid_table_requests_throttled_count \u003E 100\n      AND interval_start_time \u003E DATEADD(MINUTE, -10, CURRENT_TIMESTAMP())\n  ))\n  THEN\n    INSERT INTO HT_MON_QS_DB.DATA.alert_log (alert_type, severity, message, metric_value)\n    SELECT 'THROTTLE', 'HIGH',\n      'Throttling detected: ' || SUM(hybrid_table_requests_throttled_count)::VARCHAR || ' throttled requests in last 10 min',\n      SUM(hybrid_table_requests_throttled_count)\n    FROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\n    WHERE hybrid_table_requests_throttled_count \u003E 0\n      AND interval_start_time \u003E DATEADD(MINUTE, -10, CURRENT_TIMESTAMP());\n\nALTER ALERT ht_throttle_alert RESUME;\n```\n\n### Alert 2: Latency Regression\n\nFires when p99 latency exceeds 500ms (adjust threshold for your SLA):\n\n```sql\nCREATE OR REPLACE ALERT ht_latency_alert\n  WAREHOUSE = HT_MON_QS_WH\n  SCHEDULE = '5 MINUTE'\n  IF( EXISTS(\n    SELECT 1\n    FROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\n    WHERE total_elapsed_time:\"p99\"::FLOAT \u003E 500\n      AND calls \u003E 5\n      AND interval_start_time \u003E DATEADD(MINUTE, -10, CURRENT_TIMESTAMP())\n      AND database_name = 'HT_MON_QS_DB'\n      AND query_type IN ('SELECT', 'INSERT', 'UPDATE', 'DELETE')\n  ))\n  THEN\n    INSERT INTO HT_MON_QS_DB.DATA.alert_log (alert_type, severity, message, metric_value)\n    SELECT 'LATENCY_REGRESSION', 'MEDIUM',\n      'p99 latency exceeded 500ms for hash ' || query_parameterized_hash || ': ' || ROUND(total_elapsed_time:\"p99\"::FLOAT, 1)::VARCHAR || 'ms',\n      total_elapsed_time:\"p99\"::FLOAT\n    FROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\n    WHERE total_elapsed_time:\"p99\"::FLOAT \u003E 500\n      AND calls \u003E 5\n      AND interval_start_time \u003E DATEADD(MINUTE, -10, CURRENT_TIMESTAMP())\n      AND database_name = 'HT_MON_QS_DB'\n    ORDER BY total_elapsed_time:\"p99\"::FLOAT DESC\n    LIMIT 1;\n\nALTER ALERT ht_latency_alert RESUME;\n```\n\n### Alert 3: Error Spike\n\nFires when error rate exceeds 5% of total calls:\n\n```sql\nCREATE OR REPLACE ALERT ht_error_alert\n  WAREHOUSE = HT_MON_QS_WH\n  SCHEDULE = '5 MINUTE'\n  IF( EXISTS(\n    SELECT 1\n    FROM (\n      SELECT\n        SUM(calls) AS total_calls,\n        SUM(ARRAY_SIZE(errors)) AS total_errors\n      FROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\n      WHERE interval_start_time \u003E DATEADD(MINUTE, -10, CURRENT_TIMESTAMP())\n        AND database_name = 'HT_MON_QS_DB'\n    )\n    WHERE total_errors \u003E 0\n      AND total_errors::FLOAT / NULLIF(total_calls, 0) \u003E 0.05\n  ))\n  THEN\n    INSERT INTO HT_MON_QS_DB.DATA.alert_log (alert_type, severity, message, metric_value)\n    SELECT 'ERROR_SPIKE', 'HIGH',\n      'Error rate exceeded 5%: ' || SUM(ARRAY_SIZE(errors))::VARCHAR || ' errors out of ' || SUM(calls)::VARCHAR || ' calls',\n      ROUND(SUM(ARRAY_SIZE(errors))::FLOAT / NULLIF(SUM(calls), 0) * 100, 2)\n    FROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\n    WHERE interval_start_time \u003E DATEADD(MINUTE, -10, CURRENT_TIMESTAMP())\n      AND database_name = 'HT_MON_QS_DB';\n\nALTER ALERT ht_error_alert RESUME;\n```\n\n### Alert 4: Storage Quota Warning\n\nFires when database storage exceeds 80% of the 2 TB quota:\n\n```sql\nCREATE OR REPLACE ALERT ht_storage_alert\n  WAREHOUSE = HT_MON_QS_WH\n  SCHEDULE = 'USING CRON 0 */6 * * * UTC'\n  IF( EXISTS(\n    SELECT 1\n    FROM (\n      SELECT SUM(active_bytes) / POWER(1024, 4) AS active_tb\n      FROM SNOWFLAKE.ACCOUNT_USAGE.TABLE_STORAGE_METRICS\n      WHERE table_catalog = 'HT_MON_QS_DB'\n        AND deleted IS NULL\n    )\n    WHERE active_tb \u003E 1.6\n  ))\n  THEN\n    INSERT INTO HT_MON_QS_DB.DATA.alert_log (alert_type, severity, message, metric_value)\n    SELECT 'STORAGE_QUOTA', 'HIGH',\n      'HT database storage at ' || ROUND(SUM(active_bytes) / POWER(1024, 4) * 100 / 2.0, 1)::VARCHAR || '% of 2TB quota',\n      SUM(active_bytes) / POWER(1024, 4)\n    FROM SNOWFLAKE.ACCOUNT_USAGE.TABLE_STORAGE_METRICS\n    WHERE table_catalog = 'HT_MON_QS_DB'\n      AND deleted IS NULL;\n\nALTER ALERT ht_storage_alert RESUME;\n```\n\n### Review Alerts\n\n```sql\nSHOW ALERTS;\nSELECT * FROM HT_MON_QS_DB.DATA.alert_log ORDER BY alert_ts DESC LIMIT 20;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Step 6: Custom Dashboards and Observability Apps\nDuration: 10\n\nBuild custom monitoring experiences using Snowflake-native application frameworks — no external infrastructure required.\n\n### Option A: Streamlit in Snowflake\n\nBuild an interactive monitoring dashboard that runs entirely within your Snowflake account.\n\n![Streamlit Monitoring Dashboard](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hybrid-tables-monitoring-observability/streamlit-dashboard-overview.png?v=853eec33)\n\n```python\nimport streamlit as st\nfrom snowflake.snowpark.context import get_active_session\n\nsession = get_active_session()\n\nst.set_page_config(page_title=\"Hybrid Table Monitor\", layout=\"wide\")\nst.title(\"🔍 Hybrid Table Monitoring Dashboard\")\n\nlookback = st.selectbox(\"Lookback Window\", [\"6 hours\", \"24 hours\", \"7 days\"], index=1)\nhours_map = {\"6 hours\": 6, \"24 hours\": 24, \"7 days\": 168}\nhours = hours_map[lookback]\n\nqps_df = session.sql(f\"\"\"\n    SELECT interval_start_time AS ts,\n           SUM(calls) / 60.0 AS qps\n    FROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\n    WHERE interval_start_time \u003E DATEADD(HOUR, -{hours}, CURRENT_TIMESTAMP())\n    GROUP BY interval_start_time ORDER BY ts\n\"\"\").to_pandas()\n\nlatency_df = session.sql(f\"\"\"\n    SELECT interval_start_time AS ts,\n           AVG(total_elapsed_time:\"median\"::FLOAT) AS p50_ms,\n           AVG(total_elapsed_time:\"p90\"::FLOAT) AS p90_ms,\n           AVG(total_elapsed_time:\"p99\"::FLOAT) AS p99_ms\n    FROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\n    WHERE interval_start_time \u003E DATEADD(HOUR, -{hours}, CURRENT_TIMESTAMP())\n      AND calls \u003E 0\n    GROUP BY interval_start_time ORDER BY ts\n\"\"\").to_pandas()\n\nthrottle_df = session.sql(f\"\"\"\n    SELECT SUM(hybrid_table_requests_throttled_count) AS throttled\n    FROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\n    WHERE interval_start_time \u003E DATEADD(HOUR, -{hours}, CURRENT_TIMESTAMP())\n\"\"\").to_pandas()\n\nm1, m2, m3 = st.columns(3)\nwith m1:\n    total = int(qps_df[\"QPS\"].sum() * 60) if not qps_df.empty else 0\n    st.metric(\"Total Queries\", f\"{total:,}\")\nwith m2:\n    throttled = int(throttle_df[\"THROTTLED\"].iloc[0] or 0)\n    st.metric(\"Throttled Requests\", f\"{throttled:,}\")\nwith m3:\n    st.metric(\"Avg Error Rate\", \"—\")\n\ncol1, col2 = st.columns(2)\nwith col1:\n    st.subheader(\"Throughput (QPS)\")\n    st.area_chart(qps_df.set_index(\"TS\")[\"QPS\"])\nwith col2:\n    st.subheader(\"Latency Percentiles (ms)\")\n    st.line_chart(latency_df.set_index(\"TS\")[[\"P50_MS\", \"P90_MS\", \"P99_MS\"]])\n```\n\nDeploy this as a Streamlit in Snowflake app for a zero-infrastructure monitoring solution that your team can access from the Snowsight UI.\n\n\u003E **Note on OBJECT accessor syntax:** When accessing keys inside OBJECT columns like `total_elapsed_time`, use double-quote notation (`total_elapsed_time:\"median\"::FLOAT`) or bracket notation (`total_elapsed_time['median']::FLOAT`). Single-quote notation may fail in some connectors.\n\n### Option B: Snowflake Apps (Snowflake Native App Framework)\n\nFor a packaged, shareable monitoring experience, build a [Snowflake Native App](https://docs.snowflake.com/en/developer-guide/native-apps/native-apps-about) that:\n\n- Bundles the dashboard SQL, alerts, and tasks into a single installable package\n- Can be shared across accounts (e.g., from a central platform team to application teams)\n- Includes Streamlit pages for visualization and a setup wizard for configuring database filters\n\nThis approach works well when you want to standardize HT monitoring across multiple teams or accounts.\n\n### Option C: OpenTelemetry Export\n\nFor teams already invested in OpenTelemetry, export HT metrics as OTLP data:\n\n1. **Snowflake Task** exports metrics to a stage on a schedule (e.g., every 5 minutes)\n2. **OpenTelemetry Collector** with the `filereceiver` reads staged JSON files\n3. **Your OTLP backend** (Jaeger, Honeycomb, Grafana Tempo) receives the traces/metrics\n\nThis integrates HT monitoring into your existing distributed tracing infrastructure alongside application-level spans.\n\n### Choosing the Right Approach\n\n| Approach | Best For | Pros | Cons |\n|----------|----------|------|------|\n| **Snowflake Worksheets** | Quick ad-hoc queries | Zero config, native | No persistent dashboards |\n| **Streamlit in Snowflake** | Custom team dashboards | Rich interactivity, runs in SF | Requires SiS entitlement |\n| **Snowflake Native App** | Multi-account standardization | Shareable, versioned | More setup complexity |\n| **Grafana/Datadog** | Unified observability stack | Correlate with app metrics | External dependency |\n| **OTLP Export** | Distributed tracing teams | Fits existing pipelines | Custom integration work |\n\n\u003C!-- ------------------------ --\u003E\n## Step 7: External Observability Integration\n\nFor teams using Datadog, Grafana, or Prometheus, export HT metrics from Snowflake on a schedule.\n\n### Datadog Integration\n\nWith the [Snowflake Datadog integration](https://docs.datadoghq.com/integrations/snowflake/), you can query `AGGREGATE_QUERY_HISTORY` directly. Configure a custom query in the integration YAML:\n\n```yaml\n# Example Datadog custom query (datadog-agent snowflake.d/conf.yaml)\ncustom_queries:\n  - query: \u003E\n      SELECT\n        SUM(calls) / 60.0 AS qps,\n        AVG(total_elapsed_time:\"p99\"::FLOAT) AS p99_ms,\n        SUM(hybrid_table_requests_throttled_count) AS throttled\n      FROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\n      WHERE interval_start_time \u003E DATEADD(MINUTE, -5, CURRENT_TIMESTAMP())\n        AND database_name = 'YOUR_HT_DATABASE'\n    columns:\n      - name: snowflake.hybrid_table.qps\n        type: gauge\n      - name: snowflake.hybrid_table.p99_ms\n        type: gauge\n      - name: snowflake.hybrid_table.throttled\n        type: count\n    tags:\n      - service:hybrid-tables\n```\n\n### Grafana Integration\n\nUse the [Michelin Snowflake Grafana data source plugin](https://github.com/michelin/snowflake-grafana-datasource) (open-source, community-supported) to query `AGGREGATE_QUERY_HISTORY` directly. Configure the data source with your account identifier, warehouse, and credentials, then build panels using the same SQL from Step 3.\n\n\u003E **Note:** The official Grafana Snowflake plugin requires a Grafana Enterprise license. The Michelin community plugin is free and supports password, key-pair, and OAuth authentication.\n\n![Grafana QPS Panel](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hybrid-tables-monitoring-observability/grafana-qps.png?v=853eec33)\n\n![Grafana Read/Write Mix](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hybrid-tables-monitoring-observability/grafana-read-write-mix.png?v=853eec33)\n\n\u003C!-- ------------------------ --\u003E\n## Monitoring Playbook\n\nWhen alerts fire, use this decision tree:\n\n| Alert | First Response | Root Cause Investigation |\n|-------|---------------|------------------------|\n| **THROTTLE** | Check if workload has spiked vs normal | Review `calls` by `query_parameterized_hash` — one runaway loop? Distribute across databases if sustained. |\n| **LATENCY_REGRESSION** | Check compilation_time vs execution_time | High compile_time → plan cache miss (bound variables, schema qualification). High exec_time → check Query Profile for full table scans or lock contention. |\n| **ERROR_SPIKE** | Inspect error array contents | Common: PK violations (duplicate inserts), lock timeouts (contention), quota exceeded. |\n| **STORAGE_QUOTA** | Check per-table storage | Archive cold data to standard table (see [Operations Patterns](https://www.snowflake.com/en/developers/guides/hybrid-tables-operations-patterns/)). |\n\n### Useful Diagnostic Queries\n\n**Find the noisiest query shape:**\n\n```sql\nSELECT\n    query_parameterized_hash,\n    ANY_VALUE(query_text) AS query_sample,\n    SUM(calls) AS total_calls,\n    AVG(total_elapsed_time:\"avg\"::FLOAT) AS avg_ms\nFROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\nWHERE interval_start_time \u003E DATEADD(HOUR, -1, CURRENT_TIMESTAMP())\nGROUP BY query_parameterized_hash\nORDER BY total_calls DESC\nLIMIT 5;\n```\n\n**Find queries with high tail latency (p99/median ratio \u003E 5x):**\n\n```sql\nSELECT\n    query_parameterized_hash,\n    ANY_VALUE(LEFT(query_text, 100)) AS query_sample,\n    SUM(calls) AS total_calls,\n    AVG(total_elapsed_time:\"median\"::FLOAT) AS p50_ms,\n    AVG(total_elapsed_time:\"p99\"::FLOAT) AS p99_ms,\n    ROUND(AVG(total_elapsed_time:\"p99\"::FLOAT) / NULLIF(AVG(total_elapsed_time:\"median\"::FLOAT), 0), 1) AS tail_ratio\nFROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\nWHERE interval_start_time \u003E DATEADD(HOUR, -6, CURRENT_TIMESTAMP())\n  AND calls \u003E 10\nGROUP BY query_parameterized_hash\nHAVING tail_ratio \u003E 5\nORDER BY tail_ratio DESC\nLIMIT 10;\n```\n\n\u003E **High tail ratio** means inconsistent performance. Common causes: some lookups hit compacted data (fast) while others hit recently written data (slower), or intermittent lock contention from concurrent writes.\n\n\u003C!-- ------------------------ --\u003E\n## Get Started Faster with Cortex Code\nDuration: 1\n\nUse these prompts in [Cortex Code](https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code) to apply this guide to your own workload:\n\n\u003E \"Run an AGGREGATE_QUERY_HISTORY analysis for my Hybrid Table workload over the past 7 days. Identify my top 3 performance issues and provide specific remediation steps.\"\n\n\u003E \"I have a Hybrid Table called [TABLE_NAME]. Adapt the 6 monitoring dashboard queries from the HT Monitoring quickstart to my schema and return ready-to-run SQL.\"\n\n\u003E \"My Hybrid Table queries have high compilation time. Diagnose the root cause and rewrite the top offending queries to use bound variables.\"\n\n\u003C!-- ------------------------ --\u003E\n## Cleanup\n\n```sql\nUSE ROLE ACCOUNTADMIN;\nALTER ALERT IF EXISTS ht_throttle_alert SUSPEND;\nALTER ALERT IF EXISTS ht_latency_alert SUSPEND;\nALTER ALERT IF EXISTS ht_error_alert SUSPEND;\nALTER ALERT IF EXISTS ht_storage_alert SUSPEND;\nDROP ALERT IF EXISTS ht_throttle_alert;\nDROP ALERT IF EXISTS ht_latency_alert;\nDROP ALERT IF EXISTS ht_error_alert;\nDROP ALERT IF EXISTS ht_storage_alert;\nDROP DATABASE IF EXISTS HT_MON_QS_DB;\nDROP WAREHOUSE IF EXISTS HT_MON_QS_WH;\nDROP ROLE IF EXISTS HT_MON_QS_ROLE;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion and Resources\n\nYou can now:\n\n- Find HT telemetry in AGGREGATE_QUERY_HISTORY (not QUERY_HISTORY)\n- Extract latency percentiles (median, p90, p99) from OBJECT-typed columns\n- Build dashboards with throughput, latency, read/write mix, and error rate tiles\n- Track HT credit consumption and storage against the 2 TB quota\n- Create multi-signal alerts (throttle, latency, errors, storage)\n- Export metrics to external observability platforms (Datadog, Grafana)\n- Build custom monitoring apps with Streamlit in Snowflake or Snowflake Native Apps\n\n\u003E **Need help with your Hybrid Table architecture?** Book a 30-minute session with our specialist team to discuss your use case, review your schema design, or troubleshoot performance: [Schedule a session](https://calendar.app.google/cGfVnKFe7xbeDqDo8)\n\n### Related Resources\n\n- [Hybrid Tables Best Practices](https://docs.snowflake.com/en/user-guide/tables-hybrid-best-practices)\n- [AGGREGATE_QUERY_HISTORY Documentation](https://docs.snowflake.com/en/sql-reference/account-usage/aggregate_query_history)\n- [Optimizing Writes to Hybrid Tables](https://www.snowflake.com/en/developers/guides/hybrid-tables-write-optimization/)\n- [Operations Patterns for Hybrid Tables](https://www.snowflake.com/en/developers/guides/hybrid-tables-operations-patterns/)\n- [Secondary Index Design for Hybrid Tables](https://www.snowflake.com/en/developers/guides/hybrid-tables-secondary-index-design/)\n- [Snowflake Alerts Documentation](https://docs.snowflake.com/en/user-guide/alerts)\n- [Streamlit in Snowflake](https://docs.snowflake.com/en/developer-guide/streamlit/about-streamlit)\n- [Snowflake Native App Framework](https://docs.snowflake.com/en/developer-guide/native-apps/native-apps-about)\n- [Michelin Snowflake Grafana Plugin](https://github.com/michelin/snowflake-grafana-datasource)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},":type":"snowflake-site/components/contentfragment","elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":items":{},":itemsOrder":[],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-871a3ad8db","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-e5e852d566",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-c403083a06","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-06-22",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-6c372cebf5","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. 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