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pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false},"responsivegrid":{"columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 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Timm","quickstartHeroForkRepoLink":{"id":"button-f915835ed4","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/hybrid-tables-write-optimization"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Fork Repo"},"quickstartHeroBreadcrumbs":[{"title":"Optimizing Writes to Hybrid Tables","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers/guides/hybrid-tables-write-optimization","currentPage":true},{"title":"Guides","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers/guides","currentPage":false},{"title":"Snowflake for Developers","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers","currentPage":false}]},"flexible_column_cont":{"id":"flexible-column-container-bc7520841e","propertiesId":"quickstart-template-main-flexible-container","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"id":"container-6f9affbc8e","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"contentfragment":{"id":"contentfragment-1604721319","paragraphs":["<!--\nkeywords: hybrid table, write optimization, bound variables, prepared statements, plan cache, parameterized hash, batch INSERT, stored procedure, COPY INTO, CTAS, compilation, latency, OLTP, Unistore, schema qualification\nrelated_concepts: query_parameterized_hash, plan cache hit, compilation overhead, AUTOCOMMIT, multi-statement transaction, bulk load optimization, ON_ERROR ABORT_STATEMENT\nprerequisite_guides: getting-started-with-hybrid-tables, hybrid-tables-application-connectors\nskill_level: intermediate\nestimated_time_minutes: 35\nsnowflake_features: hybrid_tables, query_parameterized_hash, autocommit, copy_into, tasks\n-->\n","\u003Ch1\u003EOptimizing Writes to Hybrid Tables\u003C/h1\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E This guide focuses on Snowflake-side write optimization. For driver-specific patterns (JDBC, Python, Node.js connection pooling and batch APIs), see \u003Ca href=\"https://www.snowflake.com/en/developers/guides/hybrid-tables-application-connectors/\"\u003EConnecting Applications to Hybrid Tables\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EHybrid Table write performance depends on three factors: \u003Cstrong\u003Eplan compilation\u003C/strong\u003E, \u003Cstrong\u003Erow store write throughput\u003C/strong\u003E, and \u003Cstrong\u003Eindex maintenance\u003C/strong\u003E. Of these, plan compilation is the most common source of avoidable latency. A well-optimized write path compiles a plan once and reuses it thousands of times per second. A poorly optimized path recompiles on every execution, adding 10-100ms of unnecessary overhead to every write.\u003C/p\u003E\n","\u003Cp\u003EThis quickstart teaches you how to measure and eliminate compilation overhead, choose the right write pattern for your workload, and avoid the DML anti-patterns that silently degrade performance.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Will Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow the plan cache works and why bound variables are critical\u003C/li\u003E\u003Cli\u003EHow to detect plan cache misses using \u003Ccode\u003Equery_parameterized_hash\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EWhy stored procedures add overhead and what to use instead\u003C/li\u003E\u003Cli\u003EHow COPY INTO behaves on Hybrid Tables (and when to use it)\u003C/li\u003E\u003Cli\u003EHow schema qualification inconsistency splits your plan cache\u003C/li\u003E\u003Cli\u003EThe performance hierarchy: AUTOCOMMIT > multi-statement txn > stored procedures\u003C/li\u003E\u003Cli\u003EHow to measure the write performance of your workload\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 SQL and Snowflake query profiles\u003C/li\u003E\u003Cli\u003ECompletion of \u003Ca href=\"https://www.snowflake.com/en/developers/guides/getting-started-with-hybrid-tables/\"\u003EGetting Started with Hybrid Tables\u003C/a\u003E (recommended)\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetup\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\n\nCREATE OR REPLACE ROLE HT_WRITE_QS_ROLE;\nGRANT ROLE HT_WRITE_QS_ROLE TO ROLE ACCOUNTADMIN;\n\nCREATE OR REPLACE WAREHOUSE HT_WRITE_QS_WH\n  WAREHOUSE_SIZE = XSMALL AUTO_SUSPEND = 300 AUTO_RESUME = TRUE;\nGRANT OWNERSHIP ON WAREHOUSE HT_WRITE_QS_WH TO ROLE HT_WRITE_QS_ROLE;\n\nCREATE OR REPLACE DATABASE HT_WRITE_QS_DB;\nGRANT OWNERSHIP ON DATABASE HT_WRITE_QS_DB TO ROLE HT_WRITE_QS_ROLE;\n\nUSE ROLE HT_WRITE_QS_ROLE;\nCREATE OR REPLACE SCHEMA HT_WRITE_QS_DB.DATA;\nUSE WAREHOUSE HT_WRITE_QS_WH;\nUSE DATABASE HT_WRITE_QS_DB;\nUSE SCHEMA DATA;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate the Orders Table\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE HYBRID TABLE orders (\n    order_id     NUMBER        NOT NULL,\n    customer_id  NUMBER        NOT NULL,\n    status       VARCHAR(20)   NOT NULL DEFAULT 'PENDING',\n    region       VARCHAR(10)   NOT NULL,\n    created_at   TIMESTAMP_NTZ NOT NULL,\n    total_amount NUMBER(12,2)  NOT NULL,\n    PRIMARY KEY (order_id),\n    INDEX idx_orders_customer (customer_id),\n    INDEX idx_orders_status_region (status, region)\n)\nAS SELECT\n    SEQ4(),\n    UNIFORM(1, 10000, RANDOM())::NUMBER,\n    ARRAY_CONSTRUCT('PENDING','SHIPPED','DELIVERED','CANCELLED')[UNIFORM(0,3,RANDOM())]::VARCHAR,\n    ARRAY_CONSTRUCT('US-EAST','US-WEST','EU','APAC')[UNIFORM(0,3,RANDOM())]::VARCHAR,\n    DATEADD(SECOND, UNIFORM(0,7776000,RANDOM()), DATEADD(DAY,-90,CURRENT_TIMESTAMP()))::TIMESTAMP_NTZ,\n    ROUND(UNIFORM(5.00,2500.00,RANDOM()),2)\nFROM TABLE(GENERATOR(ROWCOUNT => 100000));\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EStep 1: Why Plan Compilation Matters\u003C/h2\u003E\n","\u003Cp\u003EEvery SQL statement Snowflake executes goes through two phases: \u003Cstrong\u003Ecompilation\u003C/strong\u003E (parsing, optimization, plan generation) and \u003Cstrong\u003Eexecution\u003C/strong\u003E (running the plan against storage). For standard analytical queries that run for seconds to minutes, compilation time is negligible. For Hybrid Table OLTP queries that target single-digit milliseconds, compilation can be 50% or more of total latency.\u003C/p\u003E\n","\u003Ch3\u003EMeasure Compilation vs Execution\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESET next_id = (SELECT MAX(order_id) + 1 FROM orders);\n\nINSERT INTO orders (order_id, customer_id, status, region, created_at, total_amount)\nVALUES ($next_id, 4200, 'PENDING', 'US-EAST', CURRENT_TIMESTAMP()::TIMESTAMP_NTZ, 99.99);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOpen the Query Profile. In the Profile Overview, note:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ECompilation time\u003C/strong\u003E — time spent generating the query plan\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EExecution time\u003C/strong\u003E — time spent writing to the row store\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EFor a single-row INSERT into a Hybrid Table, you will typically see compilation taking 20-60ms and execution taking 5-20ms. The write itself is fast; the compilation dominates.\u003C/p\u003E\n","\u003Ch3\u003EThe Plan Cache\u003C/h3\u003E\n","\u003Cp\u003EWhen you use \u003Cstrong\u003Ebound variables\u003C/strong\u003E (parameterized queries), Snowflake stores the compiled plan in a cache. Subsequent executions with different parameter values skip compilation entirely and reuse the cached plan. This reduces per-query overhead from 30-60ms (compile + execute) to 5-20ms (execute only).\u003C/p\u003E\n","\u003Cp\u003EWithout bound variables (using string literals), every unique combination of values produces a new query text, which requires a new compilation. At 1,000 queries per second, that is 1,000 compilations per second — each adding unnecessary latency and consuming compilation resources.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EStep 2: Bound Variables and the Parameterized Hash\u003C/h2\u003E\n","\u003Ch3\u003EHow the Plan Cache Identifies Reusable Plans\u003C/h3\u003E\n","\u003Cp\u003ESnowflake computes a \u003Ccode\u003Equery_parameterized_hash\u003C/code\u003E for each query by replacing literal values in predicates with parameters, then hashing the canonicalized SQL text. Queries that produce the same hash reuse the same compiled plan.\u003C/p\u003E\n","\u003Ch3\u003EWhat Shares the Same Hash\u003C/h3\u003E\n","\u003Cp\u003ETwo queries share the same \u003Ccode\u003Equery_parameterized_hash\u003C/code\u003E if they differ only in:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ELiteral values used in comparison operators (\u003Ccode\u003E=\u003C/code\u003E, \u003Ccode\u003E!=\u003C/code\u003E, \u003Ccode\u003E>=\u003C/code\u003E, \u003Ccode\u003E<=\u003C/code\u003E)\u003C/li\u003E\u003Cli\u003EWhitespace differences\u003C/li\u003E\u003Cli\u003EComment differences\u003C/li\u003E\u003Cli\u003ECase-insensitive identifier, session variable, and stage name differences\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EDemonstrating Plan Cache Behavior\u003C/h3\u003E\n","\u003Cp\u003ERun several INSERTs with different literal values:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EINSERT INTO orders VALUES (200001, 1001, 'PENDING', 'US-EAST', CURRENT_TIMESTAMP()::TIMESTAMP_NTZ, 49.99);\nINSERT INTO orders VALUES (200002, 2002, 'SHIPPED', 'EU', CURRENT_TIMESTAMP()::TIMESTAMP_NTZ, 149.99);\nINSERT INTO orders VALUES (200003, 3003, 'DELIVERED', 'APAC', CURRENT_TIMESTAMP()::TIMESTAMP_NTZ, 299.99);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow check the parameterized hash — all three should share the same hash because only the literal values differ:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT query_parameterized_hash, query_text, compilation_time, execution_time, total_elapsed_time\nFROM TABLE(INFORMATION_SCHEMA.QUERY_HISTORY(\n    DATEADD('minutes', -5, CURRENT_TIMESTAMP()),\n    CURRENT_TIMESTAMP()\n))\nWHERE query_text LIKE 'INSERT INTO orders VALUES%'\nORDER BY start_time DESC\nLIMIT 5;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAll three INSERTs should show the same \u003Ccode\u003Equery_parameterized_hash\u003C/code\u003E. The first one has compilation time (plan was generated); subsequent ones may show reduced compilation (plan reused from cache).\u003C/p\u003E\n","\u003Ch3\u003EWhat Breaks the Hash\u003C/h3\u003E\n","\u003Cp\u003EThese changes produce a DIFFERENT parameterized hash and force recompilation:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Different table reference (schema-qualified vs unqualified)\nINSERT INTO orders VALUES (200004, 4004, 'PENDING', 'US-WEST', CURRENT_TIMESTAMP()::TIMESTAMP_NTZ, 59.99);\nINSERT INTO HT_WRITE_QS_DB.DATA.orders VALUES (200005, 5005, 'PENDING', 'EU', CURRENT_TIMESTAMP()::TIMESTAMP_NTZ, 79.99);\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT query_parameterized_hash, query_text\nFROM TABLE(INFORMATION_SCHEMA.QUERY_HISTORY(\n    DATEADD('minutes', -5, CURRENT_TIMESTAMP()),\n    CURRENT_TIMESTAMP()\n))\nWHERE query_text LIKE 'INSERT INTO%orders VALUES%'\nORDER BY start_time DESC\nLIMIT 5;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe schema-qualified INSERT produces a \u003Cstrong\u003Edifferent hash\u003C/strong\u003E from the unqualified one. Your application now has two plan cache entries for what is logically the same operation. Each one compiles independently, reducing your effective cache hit rate.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERule:\u003C/strong\u003E Pick one naming convention (fully-qualified or unqualified) and use it consistently across all application queries. Mixing them splits your plan cache.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EStep 3: Schema Qualification Consistency\u003C/h2\u003E\n","\u003Cp\u003EThis is one of the most common silent performance issues in Hybrid Table workloads. It produces no errors, no warnings — just doubled compilation overhead that is invisible unless you look at parameterized hashes.\u003C/p\u003E\n","\u003Ch3\u003EThe Problem\u003C/h3\u003E\n","\u003Cp\u003EIf your application has two code paths that write to the same table:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Path A (e.g., from a microservice using USE DATABASE/SCHEMA context)\nINSERT INTO orders VALUES (?, ?, ?, ?, ?, ?);\n\n-- Path B (e.g., from a stored procedure or different service using fully-qualified names)\nINSERT INTO HT_WRITE_QS_DB.DATA.orders VALUES (?, ?, ?, ?, ?, ?);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThese produce different parameterized hashes. The plan cache maintains two entries, each warming up independently. Your cache hit rate is cut in half.\u003C/p\u003E\n","\u003Ch3\u003EHow to Detect It\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    query_parameterized_hash,\n    COUNT(*) AS execution_count,\n    ANY_VALUE(query_text) AS sample_query,\n    AVG(compilation_time) AS avg_compile_ms,\n    AVG(total_elapsed_time) AS avg_total_ms\nFROM TABLE(INFORMATION_SCHEMA.QUERY_HISTORY(\n    DATEADD('hours', -1, CURRENT_TIMESTAMP()),\n    CURRENT_TIMESTAMP()\n))\nWHERE query_text ILIKE '%INSERT INTO%orders%'\nGROUP BY query_parameterized_hash\nORDER BY execution_count DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf you see multiple hashes for what is logically the same INSERT statement, check the \u003Ccode\u003Esample_query\u003C/code\u003E column — the difference is usually schema qualification.\u003C/p\u003E\n","\u003Ch3\u003EThe Fix\u003C/h3\u003E\n","\u003Cp\u003EStandardize on one convention. For applications that set session context (\u003Ccode\u003EUSE DATABASE\u003C/code\u003E, \u003Ccode\u003EUSE SCHEMA\u003C/code\u003E) at connection time, unqualified names are simpler:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- At connection init (once)\nUSE DATABASE HT_WRITE_QS_DB;\nUSE SCHEMA DATA;\n\n-- All subsequent queries (unqualified, same hash every time)\nINSERT INTO orders VALUES (?, ?, ?, ?, ?, ?);\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EStep 4: Stored Procedure Overhead\u003C/h2\u003E\n","\u003Cp\u003ESnowflake documentation explicitly states: executing with AUTOCOMMIT enabled or multi-statement transactions offers better performance than calling a stored procedure.\u003C/p\u003E\n","\u003Ch3\u003EWhy Stored Procedures Add Overhead\u003C/h3\u003E\n","\u003Cp\u003EA stored procedure introduces:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003ECALL compilation\u003C/strong\u003E — the SP call itself must be compiled\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EContext switching\u003C/strong\u003E — execution transfers from Cloud Services to the XP layer for the SP runtime\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EChild statement compilation\u003C/strong\u003E — each SQL statement inside the SP is compiled separately\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERound-trip overhead\u003C/strong\u003E — results pass back through the SP runtime to the caller\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EFor a simple single-INSERT stored procedure, this overhead can be 50-100ms on top of the INSERT's own execution time.\u003C/p\u003E\n","\u003Ch3\u003EDemonstration\u003C/h3\u003E\n","\u003Cp\u003ECreate a simple SP wrapper:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE PROCEDURE insert_order(\n    p_order_id NUMBER, p_customer_id NUMBER, p_status VARCHAR,\n    p_region VARCHAR, p_amount NUMBER\n)\nRETURNS VARCHAR\nLANGUAGE SQL\nAS\nBEGIN\n    INSERT INTO orders (order_id, customer_id, status, region, created_at, total_amount)\n    VALUES (:p_order_id, :p_customer_id, :p_status, :p_region, CURRENT_TIMESTAMP()::TIMESTAMP_NTZ, :p_amount);\n    RETURN 'OK';\nEND;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECompare the two approaches:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Direct INSERT (faster)\nSET direct_id = (SELECT MAX(order_id) + 1 FROM orders);\nINSERT INTO orders (order_id, customer_id, status, region, created_at, total_amount)\nVALUES ($direct_id, 7777, 'PENDING', 'US-EAST', CURRENT_TIMESTAMP()::TIMESTAMP_NTZ, 199.99);\n\n-- SP wrapper (slower)\nSET sp_id = (SELECT MAX(order_id) + 2 FROM orders);\nCALL insert_order($sp_id, 8888, 'PENDING', 'EU', 299.99);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECompare in query history:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT query_text, total_elapsed_time, compilation_time, execution_time\nFROM TABLE(INFORMATION_SCHEMA.QUERY_HISTORY(\n    DATEADD('minutes', -5, CURRENT_TIMESTAMP()),\n    CURRENT_TIMESTAMP()\n))\nWHERE query_text LIKE 'INSERT INTO orders%' OR query_text LIKE 'CALL insert_order%'\nORDER BY start_time DESC\nLIMIT 5;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe CALL will show higher \u003Ccode\u003Etotal_elapsed_time\u003C/code\u003E than the direct INSERT for the same logical operation.\u003C/p\u003E\n","\u003Ch3\u003EWhen Stored Procedures Are Appropriate\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EComplex business logic that cannot be expressed in a single SQL statement\u003C/li\u003E\u003Cli\u003EError handling and retry logic\u003C/li\u003E\u003Cli\u003EMulti-step workflows that need procedural flow control\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EFor simple DML wrappers (single INSERT, single UPDATE), the SP adds overhead with no benefit. Use direct parameterized SQL instead.\u003C/p\u003E\n","\u003Ch3\u003EThe Performance Hierarchy\u003C/h3\u003E\n","\u003Cp\u003EFrom best to worst for Hybrid Table single-statement DML:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EDirect DML with AUTOCOMMIT=TRUE\u003C/strong\u003E (default) — lowest overhead, plan cache works optimally\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EMulti-statement transaction\u003C/strong\u003E (explicit BEGIN/COMMIT) — slight overhead from transaction management, useful for batching related writes\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EStored procedure\u003C/strong\u003E — highest overhead, use only when procedural logic is required\u003C/li\u003E\u003C/ol\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EStep 5: COPY INTO Behavior on Hybrid Tables\u003C/h2\u003E\n","\u003Cp\u003ECOPY INTO is supported for Hybrid Tables with specific limitations:\u003C/p\u003E\n","\u003Ch3\u003EKey Behaviors\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003EON_ERROR\u003C/code\u003E\u003C/strong\u003E: Only \u003Ccode\u003EABORT_STATEMENT\u003C/code\u003E is supported. \u003Ccode\u003ESKIP_FILE\u003C/code\u003E and \u003Ccode\u003ECONTINUE\u003C/code\u003E are not available.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EOptimized bulk load\u003C/strong\u003E: COPY INTO an \u003Cstrong\u003Eempty\u003C/strong\u003E Hybrid Table uses the fast path (same as CTAS and INSERT INTO SELECT on empty tables). Loading into a non-empty table uses standard INSERT performance.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EApproximate throughput on non-empty tables\u003C/strong\u003E: ~1 million rows per minute (varies with row width and index count)\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhen to Use COPY INTO vs INSERT\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EScenario\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ERecommended Method\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EInitial table load (empty table)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECTAS (simplest) or COPY INTO (from stage)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBatch load from external stage (empty table)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECOPY INTO — benefits from optimized path\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBatch load into populated table\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECOPY INTO — works, but no fast path\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EApplication real-time writes\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDirect INSERT with bound variables\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EKafka/streaming micro-batches\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBatch INSERT via driver (see Connectors guide)\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EVerify the Optimized Path Was Used\u003C/h3\u003E\n","\u003Cp\u003EAfter a COPY INTO or INSERT INTO SELECT on an empty table, check the Query Profile statistics. If the optimized path was used, the statistics will show \u003Cstrong\u003E"Number of rows bulk loaded"\u003C/strong\u003E rather than "Number of rows inserted."\u003C/p\u003E\n","\u003Ch3\u003EDemonstration\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Create a stage and export sample data as CSV\nCREATE OR REPLACE FILE FORMAT csv_fmt TYPE = CSV FIELD_OPTIONALLY_ENCLOSED_BY = '"';\nCREATE OR REPLACE STAGE orders_stage;\n\nCOPY INTO @orders_stage/orders_export\nFROM (SELECT order_id, customer_id, status, region, created_at, total_amount FROM orders LIMIT 10000)\nFILE_FORMAT = csv_fmt\nHEADER = TRUE;\n\n-- Create an empty HT and COPY INTO it (uses optimized bulk load path)\nCREATE OR REPLACE HYBRID TABLE orders_copy (\n    order_id     NUMBER        NOT NULL,\n    customer_id  NUMBER        NOT NULL,\n    status       VARCHAR(20)   NOT NULL,\n    region       VARCHAR(10)   NOT NULL,\n    created_at   TIMESTAMP_NTZ NOT NULL,\n    total_amount NUMBER(12,2)  NOT NULL,\n    PRIMARY KEY (order_id)\n);\n\nCREATE OR REPLACE FILE FORMAT csv_load TYPE = CSV FIELD_OPTIONALLY_ENCLOSED_BY = '"' SKIP_HEADER = 1;\n\nCOPY INTO orders_copy\nFROM @orders_stage/orders_export\nFILE_FORMAT = csv_load;\n\nSELECT COUNT(*) FROM orders_copy;\n-- Expected: 10000\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECheck the Query Profile for "Number of rows bulk loaded" in the statistics to confirm the fast path was used.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EStep 6: DML Anti-Patterns\u003C/h2\u003E\n","\u003Ch3\u003EAnti-Pattern 1: String Concatenation Instead of Bound Variables\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- BAD: Every unique value creates a new parameterized hash\n-- (In application code, this looks like: f"INSERT INTO orders VALUES ({id}, ...)")\nINSERT INTO orders VALUES (300001, 1001, 'PENDING', 'US-EAST', '2026-06-16'::TIMESTAMP_NTZ, 49.99);\nINSERT INTO orders VALUES (300002, 1001, 'PENDING', 'US-EAST', '2026-06-16'::TIMESTAMP_NTZ, 49.99);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EBoth share the same hash in this case because the literals are in comparable positions. But if non-comparable positions change (e.g., table name constructed dynamically), each execution compiles separately.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- GOOD: Use session variables or driver-level bound parameters\nSET v_id = 300003;\nSET v_cust = 1001;\nINSERT INTO orders VALUES ($v_id, $v_cust, 'PENDING', 'US-EAST', CURRENT_TIMESTAMP()::TIMESTAMP_NTZ, 49.99);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIn application code, always use prepared statements (see the \u003Ca href=\"https://www.snowflake.com/en/developers/guides/hybrid-tables-application-connectors/\"\u003EConnectors guide\u003C/a\u003E).\u003C/p\u003E\n","\u003Ch3\u003EAnti-Pattern 2: CTAS Repeated Against a Populated Table\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- BAD: CREATE OR REPLACE on a non-empty HT re-creates indexes from scratch\n-- every execution. Appropriate for initial load, not for repeated refresh.\nCREATE OR REPLACE HYBRID TABLE orders (...) AS SELECT * FROM source_table;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf you need to periodically refresh a Hybrid Table from a source, use incremental patterns:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EMERGE\u003C/strong\u003E for upsert semantics\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EINSERT for new rows\u003C/strong\u003E + DELETE for removals\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECTAS+SWAP\u003C/strong\u003E only if you can tolerate the post-creation compaction window (see \u003Ca href=\"https://www.snowflake.com/en/developers/guides/hybrid-tables-streaming-patterns/\"\u003EStreaming Patterns\u003C/a\u003E guide, Step 3)\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EAnti-Pattern 3: Single-Row INSERTs in a Loop\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- BAD: 1000 round-trips, 1000 compilations\n-- (In application code: for row in rows: cursor.execute("INSERT ...", row))\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EEven with bound variables, each execution is a separate network round-trip. Use batch APIs instead:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EPython\u003C/strong\u003E: \u003Ccode\u003Ecursor.executemany(sql, list_of_tuples)\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EJDBC\u003C/strong\u003E: \u003Ccode\u003Estmt.addBatch()\u003C/code\u003E + \u003Ccode\u003Estmt.executeBatch()\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ENode.js\u003C/strong\u003E: \u003Ccode\u003Ebinds: [[row1], [row2], ...]\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ERecommended batch size: 500-1,000 rows per call.\u003C/p\u003E\n","\u003Ch3\u003EAnti-Pattern 4: BI Tool Running Full-Table CTAS on a HT\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- BAD: BI materialization running GROUP BY directly on HT\nCREATE OR REPLACE TABLE dashboard_data AS\nSELECT region, status, COUNT(*), SUM(total_amount)\nFROM orders  -- This is the Hybrid Table\nGROUP BY region, status;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis scans the entire HT via COLUMN_BASED mode on every materialization cycle. The fix: snapshot HT to a standard table first, then run analytics on the standard table. See \u003Ca href=\"https://www.snowflake.com/en/developers/guides/hybrid-tables-analytics-patterns/\"\u003EAnalytics Patterns for Hybrid Tables\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003EAnti-Pattern 5: Inconsistent Schema Qualification (Covered in Step 3)\u003C/h3\u003E\n","\u003Cp\u003EMixing \u003Ccode\u003EINSERT INTO orders\u003C/code\u003E and \u003Ccode\u003EINSERT INTO db.schema.orders\u003C/code\u003E splits the plan cache. Pick one and stick with it.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EStep 7: Measuring Write Performance\u003C/h2\u003E\n","\u003Ch3\u003EUse AGGREGATE_QUERY_HISTORY for High-Throughput Workloads\u003C/h3\u003E\n","\u003Cp\u003ESub-second HT queries may not appear in \u003Ccode\u003EQUERY_HISTORY\u003C/code\u003E. Use \u003Ccode\u003EAGGREGATE_QUERY_HISTORY\u003C/code\u003E for accurate measurement:\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_executions,\n    AVG(compilation_time:"avg"::FLOAT) AS avg_compile_ms,\n    AVG(execution_time:"avg"::FLOAT) AS avg_exec_ms,\n    AVG(total_elapsed_time:"avg"::FLOAT) AS avg_total_ms,\n    MAX(total_elapsed_time:"p99"::FLOAT) AS p99_total_ms\nFROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\nWHERE interval_start_time > DATEADD(HOUR, -1, CURRENT_TIMESTAMP())\n  AND warehouse_name = 'HT_WRITE_QS_WH'\n  AND query_text ILIKE '%INSERT%'\nGROUP BY query_parameterized_hash\nORDER BY total_executions DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EKey Metrics to Track\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EMetric\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EHealthy\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EInvestigate\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Eavg_compile_ms / avg_total_ms\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E< 30%\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E> 50% (plan cache not warming)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDistinct parameterized hashes for same logical operation\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E> 1 (schema qualification issue)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EP99 / P50 ratio\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E< 5x\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E> 10x (compaction or throttling interference)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Ehybrid_table_requests_throttled_count\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E> 0 (too many concurrent requests)\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EQuick Diagnostic\u003C/h3\u003E\n","\u003Cp\u003EIf writes are slower than expected:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ECheck \u003Ccode\u003Ecompilation_time\u003C/code\u003E vs \u003Ccode\u003Eexecution_time\u003C/code\u003E in query profile — if compilation dominates, it's a plan cache problem\u003C/li\u003E\u003Cli\u003ECheck \u003Ccode\u003Equery_parameterized_hash\u003C/code\u003E cardinality — multiple hashes for the same operation means cache splitting\u003C/li\u003E\u003Cli\u003ECheck for SP wrapper overhead — compare CALL latency vs child INSERT latency\u003C/li\u003E\u003Cli\u003ECheck for throttling — \u003Ccode\u003Ehybrid_table_requests_throttled_count > 0\u003C/code\u003E in AGGREGATE_QUERY_HISTORY\u003C/li\u003E\u003C/ol\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"Audit my Hybrid Table INSERT patterns for write amplification. Identify any bulk load anti-patterns and suggest fixes."\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E"Rewrite these INSERT statements to use bound variables and eliminate redundant recompilation: [paste SQL]."\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E"My Hybrid Table bulk load is slow. Review my approach and tell me whether I am using the optimized load path or the standard row-by-row path."\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECleanup\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\nDROP DATABASE IF EXISTS HT_WRITE_QS_DB;\nDROP WAREHOUSE IF EXISTS HT_WRITE_QS_WH;\nDROP ROLE IF EXISTS HT_WRITE_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\u003EUse bound variables to achieve plan cache reuse and eliminate unnecessary compilation\u003C/li\u003E\u003Cli\u003EDetect plan cache splits caused by schema qualification inconsistency\u003C/li\u003E\u003Cli\u003EAvoid stored procedure overhead for simple DML operations\u003C/li\u003E\u003Cli\u003EUse COPY INTO correctly (understanding ON_ERROR and fast-path limitations)\u003C/li\u003E\u003Cli\u003EMeasure write performance using \u003Ccode\u003Equery_parameterized_hash\u003C/code\u003E and AGGREGATE_QUERY_HISTORY\u003C/li\u003E\u003Cli\u003EDiagnose whether compilation or execution is the latency bottleneck\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWrite Optimization Checklist\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ECheck\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EAction\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUsing bound variables?\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAll application queries should use prepared statements\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOne parameterized hash per logical operation?\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStandardize schema qualification\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESP wrapping simple DML?\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERemove SP, use direct INSERT\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECompilation > 50% of total latency?\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPlan cache not warming — check hash cardinality\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELoading into non-empty HT?\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENo fast path available — ~1M rows/min throughput\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBI tool scanning HT directly?\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnapshot to standard table first\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\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/user-guide/tables-hybrid-test\"\u003EPerformance Testing for Hybrid Tables\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/developers/guides/hybrid-tables-application-connectors/\"\u003EConnecting Applications to Hybrid Tables\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/developers/guides/hybrid-tables-analytics-patterns/\"\u003EAnalytics Patterns for Hybrid Tables\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/developers/guides/hybrid-tables-streaming-patterns/\"\u003EStreaming and Change Detection Patterns\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/sql-reference/account-usage/aggregate_query_history\"\u003EAGGREGATE_QUERY_HISTORY View\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EFAQ\u003C/h2\u003E\n","\u003Cp\u003E\u003Cstrong\u003EQ: How do I know if my plan cache is working?\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003ECheck \u003Ccode\u003Ecompilation_time\u003C/code\u003E in query profiles. After the first execution, subsequent executions of the same parameterized query should show near-zero compilation time if the plan cache is active. You can also check \u003Ccode\u003EAGGREGATE_QUERY_HISTORY\u003C/code\u003E — if \u003Ccode\u003Ecompilation_time:"avg"\u003C/code\u003E is consistently high across thousands of executions, the cache is not being used.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EQ: Should I worry about plan cache eviction?\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EThe plan cache has a finite size. Extremely diverse workloads (thousands of distinct query shapes) may experience eviction. For typical OLTP workloads with a small number of distinct INSERT/SELECT/UPDATE patterns, eviction is unlikely. Reducing the number of distinct parameterized hashes (by fixing schema qualification issues) reduces eviction pressure.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EQ: Can I force a plan cache clear?\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003ENo public mechanism exists to explicitly clear the plan cache. Suspending and resuming the warehouse will clear warehouse-level caches, but the plan cache (managed at the Cloud Services layer) has its own lifecycle.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EQ: Is there a plan cache hit rate metric?\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003ENo public metric exposes plan cache hit rate directly. The closest proxy is monitoring \u003Ccode\u003Ecompilation_time\u003C/code\u003E across repeated executions of the same parameterized hash — consistently low compilation time indicates the cache is working.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EQ: Why does COPY INTO only support ON_ERROR = ABORT_STATEMENT for Hybrid Tables?\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EHybrid Table writes are transactional and enforce constraints (PK uniqueness, FK integrity, NOT NULL). Partial-file loading (\u003Ccode\u003ESKIP_FILE\u003C/code\u003E, \u003Ccode\u003ECONTINUE\u003C/code\u003E) would require rolling back some rows while keeping others, which conflicts with the row-level constraint enforcement model. The entire COPY operation must succeed or fail atomically.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EQ: What batch size should I use for INSERT?\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EThere is no single answer — it depends on row width and index count. A practical starting point is 500-1,000 rows per batch call. Profile your specific workload: too small (10-50 rows) wastes round-trips; too large (10,000+) risks hitting query size limits or holding transactions too long.\u003C/p\u003E"],"description":"","title":"Optimizing Writes to Hybrid Tables",":type":"snowflake-site/components/contentfragment","isDeveloperGuidesPage":false,":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"value":"\u003C!--\nkeywords: hybrid table, write optimization, bound variables, prepared statements, plan cache, parameterized hash, batch INSERT, stored procedure, COPY INTO, CTAS, compilation, latency, OLTP, Unistore, schema qualification\nrelated_concepts: query_parameterized_hash, plan cache hit, compilation overhead, AUTOCOMMIT, multi-statement transaction, bulk load optimization, ON_ERROR ABORT_STATEMENT\nprerequisite_guides: getting-started-with-hybrid-tables, hybrid-tables-application-connectors\nskill_level: intermediate\nestimated_time_minutes: 35\nsnowflake_features: hybrid_tables, query_parameterized_hash, autocommit, copy_into, tasks\n--\u003E\n\n# Optimizing Writes to Hybrid Tables\n\u003C!-- ------------------------ --\u003E\n## Overview\n\n\u003E **Note:** This guide focuses on Snowflake-side write optimization. For driver-specific patterns (JDBC, Python, Node.js connection pooling and batch APIs), see [Connecting Applications to Hybrid Tables](https://www.snowflake.com/en/developers/guides/hybrid-tables-application-connectors/).\n\nHybrid Table write performance depends on three factors: **plan compilation**, **row store write throughput**, and **index maintenance**. Of these, plan compilation is the most common source of avoidable latency. A well-optimized write path compiles a plan once and reuses it thousands of times per second. A poorly optimized path recompiles on every execution, adding 10-100ms of unnecessary overhead to every write.\n\nThis quickstart teaches you how to measure and eliminate compilation overhead, choose the right write pattern for your workload, and avoid the DML anti-patterns that silently degrade performance.\n\n### What You Will Learn\n\n- How the plan cache works and why bound variables are critical\n- How to detect plan cache misses using `query_parameterized_hash`\n- Why stored procedures add overhead and what to use instead\n- How COPY INTO behaves on Hybrid Tables (and when to use it)\n- How schema qualification inconsistency splits your plan cache\n- The performance hierarchy: AUTOCOMMIT \u003E multi-statement txn \u003E stored procedures\n- How to measure the write performance of your workload\n\n### Prerequisites\n\n- A Snowflake paid account in an AWS or Azure commercial region\n- Familiarity with SQL and Snowflake query profiles\n- Completion of [Getting Started with Hybrid Tables](https://www.snowflake.com/en/developers/guides/getting-started-with-hybrid-tables/) (recommended)\n\n\u003C!-- ------------------------ --\u003E\n## Setup\n\n```sql\nUSE ROLE ACCOUNTADMIN;\n\nCREATE OR REPLACE ROLE HT_WRITE_QS_ROLE;\nGRANT ROLE HT_WRITE_QS_ROLE TO ROLE ACCOUNTADMIN;\n\nCREATE OR REPLACE WAREHOUSE HT_WRITE_QS_WH\n  WAREHOUSE_SIZE = XSMALL AUTO_SUSPEND = 300 AUTO_RESUME = TRUE;\nGRANT OWNERSHIP ON WAREHOUSE HT_WRITE_QS_WH TO ROLE HT_WRITE_QS_ROLE;\n\nCREATE OR REPLACE DATABASE HT_WRITE_QS_DB;\nGRANT OWNERSHIP ON DATABASE HT_WRITE_QS_DB TO ROLE HT_WRITE_QS_ROLE;\n\nUSE ROLE HT_WRITE_QS_ROLE;\nCREATE OR REPLACE SCHEMA HT_WRITE_QS_DB.DATA;\nUSE WAREHOUSE HT_WRITE_QS_WH;\nUSE DATABASE HT_WRITE_QS_DB;\nUSE SCHEMA DATA;\n```\n\n### Create the Orders Table\n\n```sql\nCREATE OR REPLACE HYBRID TABLE orders (\n    order_id     NUMBER        NOT NULL,\n    customer_id  NUMBER        NOT NULL,\n    status       VARCHAR(20)   NOT NULL DEFAULT 'PENDING',\n    region       VARCHAR(10)   NOT NULL,\n    created_at   TIMESTAMP_NTZ NOT NULL,\n    total_amount NUMBER(12,2)  NOT NULL,\n    PRIMARY KEY (order_id),\n    INDEX idx_orders_customer (customer_id),\n    INDEX idx_orders_status_region (status, region)\n)\nAS SELECT\n    SEQ4(),\n    UNIFORM(1, 10000, RANDOM())::NUMBER,\n    ARRAY_CONSTRUCT('PENDING','SHIPPED','DELIVERED','CANCELLED')[UNIFORM(0,3,RANDOM())]::VARCHAR,\n    ARRAY_CONSTRUCT('US-EAST','US-WEST','EU','APAC')[UNIFORM(0,3,RANDOM())]::VARCHAR,\n    DATEADD(SECOND, UNIFORM(0,7776000,RANDOM()), DATEADD(DAY,-90,CURRENT_TIMESTAMP()))::TIMESTAMP_NTZ,\n    ROUND(UNIFORM(5.00,2500.00,RANDOM()),2)\nFROM TABLE(GENERATOR(ROWCOUNT =\u003E 100000));\n```\n\n\u003C!-- ------------------------ --\u003E\n## Step 1: Why Plan Compilation Matters\n\nEvery SQL statement Snowflake executes goes through two phases: **compilation** (parsing, optimization, plan generation) and **execution** (running the plan against storage). For standard analytical queries that run for seconds to minutes, compilation time is negligible. For Hybrid Table OLTP queries that target single-digit milliseconds, compilation can be 50% or more of total latency.\n\n### Measure Compilation vs Execution\n\n```sql\nSET next_id = (SELECT MAX(order_id) + 1 FROM orders);\n\nINSERT INTO orders (order_id, customer_id, status, region, created_at, total_amount)\nVALUES ($next_id, 4200, 'PENDING', 'US-EAST', CURRENT_TIMESTAMP()::TIMESTAMP_NTZ, 99.99);\n```\n\nOpen the Query Profile. In the Profile Overview, note:\n- **Compilation time** — time spent generating the query plan\n- **Execution time** — time spent writing to the row store\n\nFor a single-row INSERT into a Hybrid Table, you will typically see compilation taking 20-60ms and execution taking 5-20ms. The write itself is fast; the compilation dominates.\n\n### The Plan Cache\n\nWhen you use **bound variables** (parameterized queries), Snowflake stores the compiled plan in a cache. Subsequent executions with different parameter values skip compilation entirely and reuse the cached plan. This reduces per-query overhead from 30-60ms (compile + execute) to 5-20ms (execute only).\n\nWithout bound variables (using string literals), every unique combination of values produces a new query text, which requires a new compilation. At 1,000 queries per second, that is 1,000 compilations per second — each adding unnecessary latency and consuming compilation resources.\n\n\u003C!-- ------------------------ --\u003E\n## Step 2: Bound Variables and the Parameterized Hash\n\n### How the Plan Cache Identifies Reusable Plans\n\nSnowflake computes a `query_parameterized_hash` for each query by replacing literal values in predicates with parameters, then hashing the canonicalized SQL text. Queries that produce the same hash reuse the same compiled plan.\n\n### What Shares the Same Hash\n\nTwo queries share the same `query_parameterized_hash` if they differ only in:\n- Literal values used in comparison operators (`=`, `!=`, `\u003E=`, `\u003C=`)\n- Whitespace differences\n- Comment differences\n- Case-insensitive identifier, session variable, and stage name differences\n\n### Demonstrating Plan Cache Behavior\n\nRun several INSERTs with different literal values:\n\n```sql\nINSERT INTO orders VALUES (200001, 1001, 'PENDING', 'US-EAST', CURRENT_TIMESTAMP()::TIMESTAMP_NTZ, 49.99);\nINSERT INTO orders VALUES (200002, 2002, 'SHIPPED', 'EU', CURRENT_TIMESTAMP()::TIMESTAMP_NTZ, 149.99);\nINSERT INTO orders VALUES (200003, 3003, 'DELIVERED', 'APAC', CURRENT_TIMESTAMP()::TIMESTAMP_NTZ, 299.99);\n```\n\nNow check the parameterized hash — all three should share the same hash because only the literal values differ:\n\n```sql\nSELECT query_parameterized_hash, query_text, compilation_time, execution_time, total_elapsed_time\nFROM TABLE(INFORMATION_SCHEMA.QUERY_HISTORY(\n    DATEADD('minutes', -5, CURRENT_TIMESTAMP()),\n    CURRENT_TIMESTAMP()\n))\nWHERE query_text LIKE 'INSERT INTO orders VALUES%'\nORDER BY start_time DESC\nLIMIT 5;\n```\n\nAll three INSERTs should show the same `query_parameterized_hash`. The first one has compilation time (plan was generated); subsequent ones may show reduced compilation (plan reused from cache).\n\n### What Breaks the Hash\n\nThese changes produce a DIFFERENT parameterized hash and force recompilation:\n\n```sql\n-- Different table reference (schema-qualified vs unqualified)\nINSERT INTO orders VALUES (200004, 4004, 'PENDING', 'US-WEST', CURRENT_TIMESTAMP()::TIMESTAMP_NTZ, 59.99);\nINSERT INTO HT_WRITE_QS_DB.DATA.orders VALUES (200005, 5005, 'PENDING', 'EU', CURRENT_TIMESTAMP()::TIMESTAMP_NTZ, 79.99);\n```\n\n```sql\nSELECT query_parameterized_hash, query_text\nFROM TABLE(INFORMATION_SCHEMA.QUERY_HISTORY(\n    DATEADD('minutes', -5, CURRENT_TIMESTAMP()),\n    CURRENT_TIMESTAMP()\n))\nWHERE query_text LIKE 'INSERT INTO%orders VALUES%'\nORDER BY start_time DESC\nLIMIT 5;\n```\n\nThe schema-qualified INSERT produces a **different hash** from the unqualified one. Your application now has two plan cache entries for what is logically the same operation. Each one compiles independently, reducing your effective cache hit rate.\n\n\u003E **Rule:** Pick one naming convention (fully-qualified or unqualified) and use it consistently across all application queries. Mixing them splits your plan cache.\n\n\u003C!-- ------------------------ --\u003E\n## Step 3: Schema Qualification Consistency\n\nThis is one of the most common silent performance issues in Hybrid Table workloads. It produces no errors, no warnings — just doubled compilation overhead that is invisible unless you look at parameterized hashes.\n\n### The Problem\n\nIf your application has two code paths that write to the same table:\n\n```sql\n-- Path A (e.g., from a microservice using USE DATABASE/SCHEMA context)\nINSERT INTO orders VALUES (?, ?, ?, ?, ?, ?);\n\n-- Path B (e.g., from a stored procedure or different service using fully-qualified names)\nINSERT INTO HT_WRITE_QS_DB.DATA.orders VALUES (?, ?, ?, ?, ?, ?);\n```\n\nThese produce different parameterized hashes. The plan cache maintains two entries, each warming up independently. Your cache hit rate is cut in half.\n\n### How to Detect It\n\n```sql\nSELECT\n    query_parameterized_hash,\n    COUNT(*) AS execution_count,\n    ANY_VALUE(query_text) AS sample_query,\n    AVG(compilation_time) AS avg_compile_ms,\n    AVG(total_elapsed_time) AS avg_total_ms\nFROM TABLE(INFORMATION_SCHEMA.QUERY_HISTORY(\n    DATEADD('hours', -1, CURRENT_TIMESTAMP()),\n    CURRENT_TIMESTAMP()\n))\nWHERE query_text ILIKE '%INSERT INTO%orders%'\nGROUP BY query_parameterized_hash\nORDER BY execution_count DESC;\n```\n\nIf you see multiple hashes for what is logically the same INSERT statement, check the `sample_query` column — the difference is usually schema qualification.\n\n### The Fix\n\nStandardize on one convention. For applications that set session context (`USE DATABASE`, `USE SCHEMA`) at connection time, unqualified names are simpler:\n\n```sql\n-- At connection init (once)\nUSE DATABASE HT_WRITE_QS_DB;\nUSE SCHEMA DATA;\n\n-- All subsequent queries (unqualified, same hash every time)\nINSERT INTO orders VALUES (?, ?, ?, ?, ?, ?);\n```\n\n\u003C!-- ------------------------ --\u003E\n## Step 4: Stored Procedure Overhead\n\nSnowflake documentation explicitly states: executing with AUTOCOMMIT enabled or multi-statement transactions offers better performance than calling a stored procedure.\n\n### Why Stored Procedures Add Overhead\n\nA stored procedure introduces:\n1. **CALL compilation** — the SP call itself must be compiled\n2. **Context switching** — execution transfers from Cloud Services to the XP layer for the SP runtime\n3. **Child statement compilation** — each SQL statement inside the SP is compiled separately\n4. **Round-trip overhead** — results pass back through the SP runtime to the caller\n\nFor a simple single-INSERT stored procedure, this overhead can be 50-100ms on top of the INSERT's own execution time.\n\n### Demonstration\n\nCreate a simple SP wrapper:\n\n```sql\nCREATE OR REPLACE PROCEDURE insert_order(\n    p_order_id NUMBER, p_customer_id NUMBER, p_status VARCHAR,\n    p_region VARCHAR, p_amount NUMBER\n)\nRETURNS VARCHAR\nLANGUAGE SQL\nAS\nBEGIN\n    INSERT INTO orders (order_id, customer_id, status, region, created_at, total_amount)\n    VALUES (:p_order_id, :p_customer_id, :p_status, :p_region, CURRENT_TIMESTAMP()::TIMESTAMP_NTZ, :p_amount);\n    RETURN 'OK';\nEND;\n```\n\nCompare the two approaches:\n\n```sql\n-- Direct INSERT (faster)\nSET direct_id = (SELECT MAX(order_id) + 1 FROM orders);\nINSERT INTO orders (order_id, customer_id, status, region, created_at, total_amount)\nVALUES ($direct_id, 7777, 'PENDING', 'US-EAST', CURRENT_TIMESTAMP()::TIMESTAMP_NTZ, 199.99);\n\n-- SP wrapper (slower)\nSET sp_id = (SELECT MAX(order_id) + 2 FROM orders);\nCALL insert_order($sp_id, 8888, 'PENDING', 'EU', 299.99);\n```\n\nCompare in query history:\n\n```sql\nSELECT query_text, total_elapsed_time, compilation_time, execution_time\nFROM TABLE(INFORMATION_SCHEMA.QUERY_HISTORY(\n    DATEADD('minutes', -5, CURRENT_TIMESTAMP()),\n    CURRENT_TIMESTAMP()\n))\nWHERE query_text LIKE 'INSERT INTO orders%' OR query_text LIKE 'CALL insert_order%'\nORDER BY start_time DESC\nLIMIT 5;\n```\n\nThe CALL will show higher `total_elapsed_time` than the direct INSERT for the same logical operation.\n\n### When Stored Procedures Are Appropriate\n\n- Complex business logic that cannot be expressed in a single SQL statement\n- Error handling and retry logic\n- Multi-step workflows that need procedural flow control\n\nFor simple DML wrappers (single INSERT, single UPDATE), the SP adds overhead with no benefit. Use direct parameterized SQL instead.\n\n### The Performance Hierarchy\n\nFrom best to worst for Hybrid Table single-statement DML:\n\n1. **Direct DML with AUTOCOMMIT=TRUE** (default) — lowest overhead, plan cache works optimally\n2. **Multi-statement transaction** (explicit BEGIN/COMMIT) — slight overhead from transaction management, useful for batching related writes\n3. **Stored procedure** — highest overhead, use only when procedural logic is required\n\n\u003C!-- ------------------------ --\u003E\n## Step 5: COPY INTO Behavior on Hybrid Tables\n\nCOPY INTO is supported for Hybrid Tables with specific limitations:\n\n### Key Behaviors\n\n- **`ON_ERROR`**: Only `ABORT_STATEMENT` is supported. `SKIP_FILE` and `CONTINUE` are not available.\n- **Optimized bulk load**: COPY INTO an **empty** Hybrid Table uses the fast path (same as CTAS and INSERT INTO SELECT on empty tables). Loading into a non-empty table uses standard INSERT performance.\n- **Approximate throughput on non-empty tables**: ~1 million rows per minute (varies with row width and index count)\n\n### When to Use COPY INTO vs INSERT\n\n| Scenario | Recommended Method |\n|----------|-------------------|\n| Initial table load (empty table) | CTAS (simplest) or COPY INTO (from stage) |\n| Batch load from external stage (empty table) | COPY INTO — benefits from optimized path |\n| Batch load into populated table | COPY INTO — works, but no fast path |\n| Application real-time writes | Direct INSERT with bound variables |\n| Kafka/streaming micro-batches | Batch INSERT via driver (see Connectors guide) |\n\n### Verify the Optimized Path Was Used\n\nAfter a COPY INTO or INSERT INTO SELECT on an empty table, check the Query Profile statistics. If the optimized path was used, the statistics will show **\"Number of rows bulk loaded\"** rather than \"Number of rows inserted.\"\n\n### Demonstration\n\n```sql\n-- Create a stage and export sample data as CSV\nCREATE OR REPLACE FILE FORMAT csv_fmt TYPE = CSV FIELD_OPTIONALLY_ENCLOSED_BY = '\"';\nCREATE OR REPLACE STAGE orders_stage;\n\nCOPY INTO @orders_stage/orders_export\nFROM (SELECT order_id, customer_id, status, region, created_at, total_amount FROM orders LIMIT 10000)\nFILE_FORMAT = csv_fmt\nHEADER = TRUE;\n\n-- Create an empty HT and COPY INTO it (uses optimized bulk load path)\nCREATE OR REPLACE HYBRID TABLE orders_copy (\n    order_id     NUMBER        NOT NULL,\n    customer_id  NUMBER        NOT NULL,\n    status       VARCHAR(20)   NOT NULL,\n    region       VARCHAR(10)   NOT NULL,\n    created_at   TIMESTAMP_NTZ NOT NULL,\n    total_amount NUMBER(12,2)  NOT NULL,\n    PRIMARY KEY (order_id)\n);\n\nCREATE OR REPLACE FILE FORMAT csv_load TYPE = CSV FIELD_OPTIONALLY_ENCLOSED_BY = '\"' SKIP_HEADER = 1;\n\nCOPY INTO orders_copy\nFROM @orders_stage/orders_export\nFILE_FORMAT = csv_load;\n\nSELECT COUNT(*) FROM orders_copy;\n-- Expected: 10000\n```\n\nCheck the Query Profile for \"Number of rows bulk loaded\" in the statistics to confirm the fast path was used.\n\n\u003C!-- ------------------------ --\u003E\n## Step 6: DML Anti-Patterns\n\n### Anti-Pattern 1: String Concatenation Instead of Bound Variables\n\n```sql\n-- BAD: Every unique value creates a new parameterized hash\n-- (In application code, this looks like: f\"INSERT INTO orders VALUES ({id}, ...)\")\nINSERT INTO orders VALUES (300001, 1001, 'PENDING', 'US-EAST', '2026-06-16'::TIMESTAMP_NTZ, 49.99);\nINSERT INTO orders VALUES (300002, 1001, 'PENDING', 'US-EAST', '2026-06-16'::TIMESTAMP_NTZ, 49.99);\n```\n\nBoth share the same hash in this case because the literals are in comparable positions. But if non-comparable positions change (e.g., table name constructed dynamically), each execution compiles separately.\n\n```sql\n-- GOOD: Use session variables or driver-level bound parameters\nSET v_id = 300003;\nSET v_cust = 1001;\nINSERT INTO orders VALUES ($v_id, $v_cust, 'PENDING', 'US-EAST', CURRENT_TIMESTAMP()::TIMESTAMP_NTZ, 49.99);\n```\n\nIn application code, always use prepared statements (see the [Connectors guide](https://www.snowflake.com/en/developers/guides/hybrid-tables-application-connectors/)).\n\n### Anti-Pattern 2: CTAS Repeated Against a Populated Table\n\n```sql\n-- BAD: CREATE OR REPLACE on a non-empty HT re-creates indexes from scratch\n-- every execution. Appropriate for initial load, not for repeated refresh.\nCREATE OR REPLACE HYBRID TABLE orders (...) AS SELECT * FROM source_table;\n```\n\nIf you need to periodically refresh a Hybrid Table from a source, use incremental patterns:\n- **MERGE** for upsert semantics\n- **INSERT for new rows** + DELETE for removals\n- **CTAS+SWAP** only if you can tolerate the post-creation compaction window (see [Streaming Patterns](https://www.snowflake.com/en/developers/guides/hybrid-tables-streaming-patterns/) guide, Step 3)\n\n### Anti-Pattern 3: Single-Row INSERTs in a Loop\n\n```sql\n-- BAD: 1000 round-trips, 1000 compilations\n-- (In application code: for row in rows: cursor.execute(\"INSERT ...\", row))\n```\n\nEven with bound variables, each execution is a separate network round-trip. Use batch APIs instead:\n- **Python**: `cursor.executemany(sql, list_of_tuples)`\n- **JDBC**: `stmt.addBatch()` + `stmt.executeBatch()`\n- **Node.js**: `binds: [[row1], [row2], ...]`\n\nRecommended batch size: 500-1,000 rows per call.\n\n### Anti-Pattern 4: BI Tool Running Full-Table CTAS on a HT\n\n```sql\n-- BAD: BI materialization running GROUP BY directly on HT\nCREATE OR REPLACE TABLE dashboard_data AS\nSELECT region, status, COUNT(*), SUM(total_amount)\nFROM orders  -- This is the Hybrid Table\nGROUP BY region, status;\n```\n\nThis scans the entire HT via COLUMN_BASED mode on every materialization cycle. The fix: snapshot HT to a standard table first, then run analytics on the standard table. See [Analytics Patterns for Hybrid Tables](https://www.snowflake.com/en/developers/guides/hybrid-tables-analytics-patterns/).\n\n### Anti-Pattern 5: Inconsistent Schema Qualification (Covered in Step 3)\n\nMixing `INSERT INTO orders` and `INSERT INTO db.schema.orders` splits the plan cache. Pick one and stick with it.\n\n\u003C!-- ------------------------ --\u003E\n## Step 7: Measuring Write Performance\n\n### Use AGGREGATE_QUERY_HISTORY for High-Throughput Workloads\n\nSub-second HT queries may not appear in `QUERY_HISTORY`. Use `AGGREGATE_QUERY_HISTORY` for accurate measurement:\n\n```sql\nSELECT\n    query_parameterized_hash,\n    ANY_VALUE(query_text) AS sample_query,\n    SUM(calls) AS total_executions,\n    AVG(compilation_time:\"avg\"::FLOAT) AS avg_compile_ms,\n    AVG(execution_time:\"avg\"::FLOAT) AS avg_exec_ms,\n    AVG(total_elapsed_time:\"avg\"::FLOAT) AS avg_total_ms,\n    MAX(total_elapsed_time:\"p99\"::FLOAT) AS p99_total_ms\nFROM SNOWFLAKE.ACCOUNT_USAGE.AGGREGATE_QUERY_HISTORY\nWHERE interval_start_time \u003E DATEADD(HOUR, -1, CURRENT_TIMESTAMP())\n  AND warehouse_name = 'HT_WRITE_QS_WH'\n  AND query_text ILIKE '%INSERT%'\nGROUP BY query_parameterized_hash\nORDER BY total_executions DESC;\n```\n\n### Key Metrics to Track\n\n| Metric | Healthy | Investigate |\n|--------|---------|-------------|\n| avg_compile_ms / avg_total_ms | \u003C 30% | \u003E 50% (plan cache not warming) |\n| Distinct parameterized hashes for same logical operation | 1 | \u003E 1 (schema qualification issue) |\n| P99 / P50 ratio | \u003C 5x | \u003E 10x (compaction or throttling interference) |\n| `hybrid_table_requests_throttled_count` | 0 | \u003E 0 (too many concurrent requests) |\n\n### Quick Diagnostic\n\nIf writes are slower than expected:\n\n1. Check `compilation_time` vs `execution_time` in query profile — if compilation dominates, it's a plan cache problem\n2. Check `query_parameterized_hash` cardinality — multiple hashes for the same operation means cache splitting\n3. Check for SP wrapper overhead — compare CALL latency vs child INSERT latency\n4. Check for throttling — `hybrid_table_requests_throttled_count \u003E 0` in AGGREGATE_QUERY_HISTORY\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 \"Audit my Hybrid Table INSERT patterns for write amplification. Identify any bulk load anti-patterns and suggest fixes.\"\n\n\u003E \"Rewrite these INSERT statements to use bound variables and eliminate redundant recompilation: [paste SQL].\"\n\n\u003E \"My Hybrid Table bulk load is slow. Review my approach and tell me whether I am using the optimized load path or the standard row-by-row path.\"\n\n\u003C!-- ------------------------ --\u003E\n## Cleanup\n\n```sql\nUSE ROLE ACCOUNTADMIN;\nDROP DATABASE IF EXISTS HT_WRITE_QS_DB;\nDROP WAREHOUSE IF EXISTS HT_WRITE_QS_WH;\nDROP ROLE IF EXISTS HT_WRITE_QS_ROLE;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion and Resources\n\nYou can now:\n- Use bound variables to achieve plan cache reuse and eliminate unnecessary compilation\n- Detect plan cache splits caused by schema qualification inconsistency\n- Avoid stored procedure overhead for simple DML operations\n- Use COPY INTO correctly (understanding ON_ERROR and fast-path limitations)\n- Measure write performance using `query_parameterized_hash` and AGGREGATE_QUERY_HISTORY\n- Diagnose whether compilation or execution is the latency bottleneck\n\n### Write Optimization Checklist\n\n| Check | Action |\n|-------|--------|\n| Using bound variables? | All application queries should use prepared statements |\n| One parameterized hash per logical operation? | Standardize schema qualification |\n| SP wrapping simple DML? | Remove SP, use direct INSERT |\n| Compilation \u003E 50% of total latency? | Plan cache not warming — check hash cardinality |\n| Loading into non-empty HT? | No fast path available — ~1M rows/min throughput |\n| BI tool scanning HT directly? | Snapshot to standard table first |\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- [Performance Testing for Hybrid Tables](https://docs.snowflake.com/en/user-guide/tables-hybrid-test)\n- [Connecting Applications to Hybrid Tables](https://www.snowflake.com/en/developers/guides/hybrid-tables-application-connectors/)\n- [Analytics Patterns for Hybrid Tables](https://www.snowflake.com/en/developers/guides/hybrid-tables-analytics-patterns/)\n- [Streaming and Change Detection Patterns](https://www.snowflake.com/en/developers/guides/hybrid-tables-streaming-patterns/)\n- [Secondary Index Design for Hybrid Tables](https://www.snowflake.com/en/developers/guides/hybrid-tables-secondary-index-design/)\n- [AGGREGATE_QUERY_HISTORY View](https://docs.snowflake.com/en/sql-reference/account-usage/aggregate_query_history)\n\n\u003C!-- ------------------------ --\u003E\n## FAQ\n\n**Q: How do I know if my plan cache is working?**\n\nCheck `compilation_time` in query profiles. After the first execution, subsequent executions of the same parameterized query should show near-zero compilation time if the plan cache is active. You can also check `AGGREGATE_QUERY_HISTORY` — if `compilation_time:\"avg\"` is consistently high across thousands of executions, the cache is not being used.\n\n**Q: Should I worry about plan cache eviction?**\n\nThe plan cache has a finite size. Extremely diverse workloads (thousands of distinct query shapes) may experience eviction. For typical OLTP workloads with a small number of distinct INSERT/SELECT/UPDATE patterns, eviction is unlikely. Reducing the number of distinct parameterized hashes (by fixing schema qualification issues) reduces eviction pressure.\n\n**Q: Can I force a plan cache clear?**\n\nNo public mechanism exists to explicitly clear the plan cache. Suspending and resuming the warehouse will clear warehouse-level caches, but the plan cache (managed at the Cloud Services layer) has its own lifecycle.\n\n**Q: Is there a plan cache hit rate metric?**\n\nNo public metric exposes plan cache hit rate directly. The closest proxy is monitoring `compilation_time` across repeated executions of the same parameterized hash — consistently low compilation time indicates the cache is working.\n\n**Q: Why does COPY INTO only support ON_ERROR = ABORT_STATEMENT for Hybrid Tables?**\n\nHybrid Table writes are transactional and enforce constraints (PK uniqueness, FK integrity, NOT NULL). Partial-file loading (`SKIP_FILE`, `CONTINUE`) would require rolling back some rows while keeping others, which conflicts with the row-level constraint enforcement model. The entire COPY operation must succeed or fail atomically.\n\n**Q: What batch size should I use for INSERT?**\n\nThere is no single answer — it depends on row width and index count. 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