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bound variables, TIMESTAMP_NTZ\nrelated_concepts: primary key, B-tree index, predicate pushdown, plan cache, query optimization, data type matching, cardinality, selectivity\nprerequisite_guides: getting-started-with-hybrid-tables, hybrid-tables-performance-optimization-primer\nskill_level: intermediate\nestimated_time_minutes: 45\nsnowflake_features: hybrid_tables, secondary_indexes, include_columns, create_index, show_indexes, query_profile\n-->\n","\u003Ch1\u003ESecondary Index Design 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 SQL examples in this quickstart use string literals and session variables for clarity. Production OLTP workloads should use bound variables (parameterized queries) so Snowflake can cache and reuse query plans, which is critical for high-throughput workloads. See \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-hybrid-best-practices\"\u003EHybrid Tables Best Practices\u003C/a\u003E and \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-hybrid-test\"\u003EPerformance Testing for Hybrid Tables\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EA \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-hybrid\"\u003EHybrid Table\u003C/a\u003E stores its data in a row-oriented store. Queries against a Hybrid Table typically resolve to one of three access patterns: row-based primary-key access, row-based secondary-index access, or column-based scans, depending on predicates, indexes, and query shape.\u003C/p\u003E\n","\u003Cp\u003EUnlike standard Snowflake tables, which can use micro-partition pruning, result cache, and clustering, Hybrid Tables depend heavily on well-designed primary and secondary indexes, along with plan cache warm-up and warehouse cache, to achieve low latency.\u003C/p\u003E\n","\u003Cp\u003EThis quickstart teaches you how to design secondary indexes correctly, validate them using query profiles, and avoid the most common index anti-patterns that cause full table scans.\u003C/p\u003E\n","\u003Ch3\u003EScenario\u003C/h3\u003E\n","\u003Cp\u003EYou are building the order management backend for an e-commerce platform. Orders are written by your application in real time and read by customer service agents, fulfillment systems, and operational dashboards. You will model this workload using Hybrid Tables and progressively improve query performance through index design.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Will Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow Hybrid Table indexes differ from standard table clustering\u003C/li\u003E\u003Cli\u003EHow to create single-column and composite secondary indexes\u003C/li\u003E\u003Cli\u003EThe \u003Cstrong\u003Eequality-first, range-last\u003C/strong\u003E rule for composite index column ordering\u003C/li\u003E\u003Cli\u003EHow to create \u003Cstrong\u003Ecovering indexes\u003C/strong\u003E with \u003Ccode\u003EINCLUDE\u003C/code\u003E columns to eliminate probe scans\u003C/li\u003E\u003Cli\u003EHow to add indexes to a live, actively-used Hybrid Table without downtime\u003C/li\u003E\u003Cli\u003EHow to read query profiles to confirm index usage\u003C/li\u003E\u003Cli\u003EThe predicates and patterns that \u003Cstrong\u003Edisqualify\u003C/strong\u003E a query from index use\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 (Hybrid Tables are not available in GCP, government regions, or trial accounts)\u003C/li\u003E\u003Cli\u003EFamiliarity with SQL and Snowflake Snowsight\u003C/li\u003E\u003Cli\u003EACCOUNTADMIN is used in this quickstart to set up the role, warehouse, and database. Hybrid Tables can be created on any existing database or schema as long as your role has \u003Ccode\u003ECREATE TABLE\u003C/code\u003E privileges on that schema — ACCOUNTADMIN is not required for the Hybrid Table itself. Contact your Snowflake administrator if you need \u003Ccode\u003ECREATE TABLE\u003C/code\u003E granted on an existing schema.\u003C/li\u003E\u003Cli\u003ETo add secondary indexes to an existing Hybrid Table, the role must also have \u003Ccode\u003ESELECT\u003C/code\u003E on the table\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E Queries executed in Snowsight carry additional overhead compared to driver-based access. The absolute latency numbers you observe here will be higher than what your application achieves via JDBC/Python/Node.js. The \u003Cem\u003Erelative\u003C/em\u003E difference between indexed and unindexed queries is the key signal to observe.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetup\u003C/h2\u003E\n","\u003Cp\u003EOpen a new SQL Worksheet in Snowsight and run the following to create an isolated environment for this quickstart.\u003C/p\u003E\n","\u003Ch3\u003ECreate Role, Warehouse, and Database\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\n\nCREATE OR REPLACE ROLE HT_INDEX_QS_ROLE;\nGRANT ROLE HT_INDEX_QS_ROLE TO ROLE ACCOUNTADMIN;\n\nCREATE OR REPLACE WAREHOUSE HT_INDEX_QS_WH\n  WAREHOUSE_SIZE = XSMALL\n  AUTO_SUSPEND = 300\n  AUTO_RESUME = TRUE;\nGRANT OWNERSHIP ON WAREHOUSE HT_INDEX_QS_WH TO ROLE HT_INDEX_QS_ROLE;\n\nCREATE OR REPLACE DATABASE HT_INDEX_QS_DB;\nGRANT OWNERSHIP ON DATABASE HT_INDEX_QS_DB TO ROLE HT_INDEX_QS_ROLE;\n\nCREATE OR REPLACE SCHEMA HT_INDEX_QS_DB.ORDERS;\nGRANT OWNERSHIP ON SCHEMA HT_INDEX_QS_DB.ORDERS TO ROLE HT_INDEX_QS_ROLE;\n\nUSE ROLE HT_INDEX_QS_ROLE;\nUSE WAREHOUSE HT_INDEX_QS_WH;\nUSE DATABASE HT_INDEX_QS_DB;\nUSE SCHEMA ORDERS;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate the Orders Table (No Secondary Indexes Yet)\u003C/h3\u003E\n","\u003Cp\u003EYou will start with a Hybrid Table that has \u003Cstrong\u003Eonly a primary key\u003C/strong\u003E. The primary key creates an index automatically; all other columns will require full table scans until you add secondary indexes.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E In production, best practice is to define secondary indexes at table creation time using the \u003Ccode\u003EINDEX\u003C/code\u003E clause in the CREATE HYBRID TABLE statement (e.g., \u003Ccode\u003EINDEX idx_customer (customer_id)\u003C/code\u003E). This quickstart adds indexes separately after the table is created so you can observe the before/after effect in query profiles. See \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-hybrid-index\"\u003EIndexing Hybrid Tables\u003C/a\u003E for the inline syntax.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE HYBRID TABLE orders (\n    order_id     NUMBER        NOT NULL AUTOINCREMENT PRIMARY KEY,\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    updated_at   TIMESTAMP_NTZ,\n    total_amount NUMBER(12,2)  NOT NULL,\n    created_by   VARCHAR(100)  NOT NULL\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ELoad Sample Data\u003C/h3\u003E\n","\u003Cp\u003EInsert 500,000 orders spread across 10,000 customers, four statuses, and four regions, spanning the past 90 days.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EINSERT INTO orders (customer_id, status, region, created_at, updated_at, total_amount, created_by)\nSELECT\n    UNIFORM(1, 10000, RANDOM())::NUMBER               AS customer_id,\n    ARRAY_CONSTRUCT('PENDING','SHIPPED','DELIVERED','CANCELLED')\n        [UNIFORM(0, 3, RANDOM())]::VARCHAR            AS status,\n    ARRAY_CONSTRUCT('US-EAST','US-WEST','EU','APAC')\n        [UNIFORM(0, 3, RANDOM())]::VARCHAR            AS region,\n    DATEADD(SECOND,\n        UNIFORM(0, 7776000, RANDOM()),\n        DATEADD(DAY, -90, CURRENT_TIMESTAMP())::TIMESTAMP_NTZ)  AS created_at,\n    NULL                                              AS updated_at,\n    ROUND(UNIFORM(5.00, 2500.00, RANDOM()), 2)        AS total_amount,\n    'user_' || UNIFORM(1, 500, RANDOM())::VARCHAR     AS created_by\nFROM TABLE(GENERATOR(ROWCOUNT => 500000));\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EConfirm the row count:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT COUNT(*) FROM orders;\n-- Expected: 500000\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EConfirm there are no secondary indexes yet:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESHOW INDEXES IN TABLE orders;\n-- Should show only the PRIMARY KEY index\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n<!-- INTENT: Show that queries without a matching index cause COLUMN_BASED full scans -->\n<!-- EXPECTED_BEHAVIOR: Query profile shows TableScan with Scan Mode = COLUMN_BASED -->\n<!-- COMMON_MISTAKE: Assuming Hybrid Tables use micro-partition pruning like standard tables -->\n","\u003Ch2\u003EStep 1: The Cost of a Missing Index\u003C/h2\u003E\n","\u003Cp\u003EBefore adding any secondary indexes, run a query that is typical for a customer service lookup: \u003Cstrong\u003Efind all orders for a specific customer\u003C/strong\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Pick a real customer_id from the data\nSET SAMPLE_CUSTOMER = (SELECT customer_id FROM orders LIMIT 1);\n\n-- Customer lookup: no secondary index exists\nSELECT order_id, status, created_at, total_amount\nFROM orders\nWHERE customer_id = $SAMPLE_CUSTOMER\nORDER BY created_at DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAfter this query runs, open the \u003Cstrong\u003EQuery Profile\u003C/strong\u003E in Snowsight (click the query ID, then select View Query Profile). Select the \u003Cstrong\u003ETableScan\u003C/strong\u003E operator at the bottom of the plan tree.\u003C/p\u003E\n","\u003Cp\u003EYou will see:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EAttribute\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EValue\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EScan Mode\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ECOLUMN_BASED\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ERows scanned\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E~500,000 (the entire table)\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003E\u003Ccode\u003ECOLUMN_BASED\u003C/code\u003E scan mode means Snowflake could not use the row store or any index, so it fell back to scanning the object storage copy of the data. This is the equivalent of a full table scan on a traditional RDBMS.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E Any query on a Hybrid Table that does not filter on the primary key or a secondary index column will produce a \u003Ccode\u003ECOLUMN_BASED\u003C/code\u003E scan. For an OLTP workload with thousands of queries per second, this is the primary cause of high latency (hundreds of milliseconds instead of single-digit milliseconds).\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n<!-- INTENT: Demonstrate CREATE INDEX on a single column and confirm ROW_BASED IndexScan in profile -->\n<!-- EXPECTED_BEHAVIOR: IndexScan operator appears; Scan Mode = ROW_BASED; rows scanned proportional to matching rows only -->\n<!-- COMMON_MISTAKE: Not waiting for index build to complete (status must be ACTIVE before testing) -->\n","\u003Ch2\u003EStep 2: Single-Column Secondary Index\u003C/h2\u003E\n","\u003Cp\u003EAdd a secondary index on \u003Ccode\u003Ecustomer_id\u003C/code\u003E, the most frequent lookup predicate in this workload.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE INDEX idx_orders_customer_id ON orders (customer_id);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe index is built concurrently, so the table remains available for reads and writes. Check the build status:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESHOW INDEXES IN TABLE orders;\n-- Wait until the status column shows ACTIVE for idx_orders_customer_id\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E Only one index build can run at a time. If you submit multiple \u003Ccode\u003ECREATE INDEX\u003C/code\u003E statements, they will queue.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ENow re-run the same customer lookup:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT order_id, status, created_at, total_amount\nFROM orders\nWHERE customer_id = $SAMPLE_CUSTOMER\nORDER BY created_at DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOpen the Query Profile again. The plan tree now shows an \u003Cstrong\u003EIndexScan\u003C/strong\u003E operator (not TableScan) at the bottom.\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EAttribute\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EValue\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EScan Mode\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EROW_BASED\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EIndex name\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EIDX_ORDERS_CUSTOMER_ID\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EAccess predicates\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EORDERS.CUSTOMER_ID = (:SFAP_PRE_NR_1)\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ERows scanned\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E~50 (rows matching that customer_id)\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003E\u003Ccode\u003EROW_BASED\u003C/code\u003E scan mode with an IndexScan means the query went directly to the relevant rows in the row store, with no full scan. You should see a significant latency improvement; exact numbers vary by data distribution, row count, and warehouse state.\u003C/p\u003E\n","\u003Ch3\u003EHow It Works\u003C/h3\u003E\n","\u003Cp\u003EWhen you create \u003Ccode\u003Eidx_orders_customer_id\u003C/code\u003E, Hybrid Table storage builds a separate B-tree structure in the row store keyed by \u003Ccode\u003Ecustomer_id\u003C/code\u003E. When a query filters on \u003Ccode\u003Ecustomer_id = <value>\u003C/code\u003E, the query optimizer seeks directly to matching entries in this index B-tree, then fetches only those rows from the primary store. The number of rows scanned is proportional to how many orders that customer has, not the total table size.\u003C/p\u003E\n<!-- ------------------------ -->\n<!-- INTENT: Teach composite index column ordering — equality columns first, range column last -->\n<!-- EXPECTED_BEHAVIOR: All three predicates pushed as access predicates when order is correct -->\n<!-- COMMON_MISTAKE: Putting range predicate (timestamp) as leading column; putting low-cardinality boolean first -->\n","\u003Ch2\u003EStep 3: Composite Indexes: Equality First, Range Last\u003C/h2\u003E\n","\u003Cp\u003EA common operational query is: \u003Cstrong\u003Efind all PENDING orders in a specific region, created in the last 7 days\u003C/strong\u003E. This query has:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ETwo equality predicates (\u003Ccode\u003Estatus\u003C/code\u003E, \u003Ccode\u003Eregion\u003C/code\u003E)\u003C/li\u003E\u003Cli\u003EOne range predicate (\u003Ccode\u003Ecreated_at >\u003C/code\u003E)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe column ordering in a composite index matters. The rule is:\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERule:\u003C/strong\u003E Equality predicates come first. A range predicate comes last. Only one range predicate can benefit from the index.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ECorrect Design\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE INDEX idx_orders_status_region_ts\n    ON orders (status, region, created_at);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis index can be used when:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EQuerying on \u003Ccode\u003Estatus\u003C/code\u003E alone\u003C/li\u003E\u003Cli\u003EQuerying on \u003Ccode\u003Estatus\u003C/code\u003E + \u003Ccode\u003Eregion\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EQuerying on \u003Ccode\u003Estatus\u003C/code\u003E + \u003Ccode\u003Eregion\u003C/code\u003E + a range on \u003Ccode\u003Ecreated_at\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ERun the operational query:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT order_id, customer_id, created_at, total_amount\nFROM orders\nWHERE status   = 'PENDING'\n  AND region   = 'US-EAST'\n  AND created_at > DATEADD(DAY, -7, CURRENT_TIMESTAMP())::TIMESTAMP_NTZ\nORDER BY created_at DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe query profile will show an \u003Cstrong\u003EIndexScan\u003C/strong\u003E on \u003Ccode\u003EIDX_ORDERS_STATUS_REGION_TS\u003C/code\u003E with all three predicates pushed as access predicates.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E Predicate values must match the data type of the indexed column. \u003Ccode\u003ECURRENT_TIMESTAMP()\u003C/code\u003E returns \u003Ccode\u003ETIMESTAMP_LTZ\u003C/code\u003E. Since \u003Ccode\u003Ecreated_at\u003C/code\u003E is declared as \u003Ccode\u003ETIMESTAMP_NTZ\u003C/code\u003E, the \u003Ccode\u003E::TIMESTAMP_NTZ\u003C/code\u003E cast is required for the predicate to be pushed to the index. Without it, the implicit type conversion causes a full table scan.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EWhy Column Order Matters\u003C/h3\u003E\n","\u003Cp\u003EConsider what would happen if you reversed the columns to \u003Ccode\u003EINDEX (created_at, status, region)\u003C/code\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EThe index B-tree is sorted by \u003Ccode\u003Ecreated_at\u003C/code\u003E first.\u003C/li\u003E\u003Cli\u003EA filter on \u003Ccode\u003Estatus = 'PENDING'\u003C/code\u003E \u003Cstrong\u003Ecannot\u003C/strong\u003E be pushed to the index seek position because \u003Ccode\u003Estatus\u003C/code\u003E is not a leading key.\u003C/li\u003E\u003Cli\u003EThe query would require scanning the entire time range to find matching statuses.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe rule is: order composite indexes to match your most common query patterns. Equality predicates go first, and the range predicate goes last.\u003C/p\u003E\n","\u003Ch3\u003EVerify Index Selectivity\u003C/h3\u003E\n","\u003Cp\u003EBefore investing in an index, check how selective your predicate is:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- How many distinct customer_ids? High = good selectivity\nSELECT APPROX_COUNT_DISTINCT(customer_id) AS distinct_customers FROM orders;\n\n-- Distribution of status values? Low cardinality -- index alone on status is weak\nSELECT status, COUNT(*) FROM orders GROUP BY status ORDER BY 2 DESC;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E A single-column index on a low-cardinality column like \u003Ccode\u003Estatus\u003C/code\u003E (only 4 values) is a poor standalone index because it still scans a large fraction of the table. It becomes more selective as the \u003Cstrong\u003Eleading\u003C/strong\u003E column of a composite index that also constrains \u003Ccode\u003Eregion\u003C/code\u003E and \u003Ccode\u003Ecreated_at\u003C/code\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EOrdering within equality columns:\u003C/strong\u003E Within the equality prefix of a composite index, higher-cardinality columns should generally lead. A boolean column like \u003Ccode\u003Eis_active\u003C/code\u003E (2 distinct values) as the leading key means every seek still touches roughly half the index. A higher-cardinality column like \u003Ccode\u003Eregion\u003C/code\u003E (4 values) or \u003Ccode\u003Ecustomer_id\u003C/code\u003E (10,000 values) narrows the seek range considerably.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Less effective: boolean column leads, large fraction of index scanned on every seek\nCREATE INDEX idx_poor ON orders (is_active, region, created_at);\n\n-- Better: higher-cardinality column leads, much smaller seek range\nCREATE INDEX idx_better ON orders (region, is_active, created_at);\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n<!-- INTENT: Show how INCLUDE columns create a covering index that eliminates the probe scan -->\n<!-- EXPECTED_BEHAVIOR: No probe scan in query profile; IndexScan returns all projected columns directly -->\n<!-- COMMON_MISTAKE: Including too many columns (increases storage and write overhead for marginal benefit) -->\n","\u003Ch2\u003EStep 4: Covering Indexes with INCLUDE\u003C/h2\u003E\n","\u003Cp\u003EWhen a query uses a secondary index, Snowflake performs two operations. First, it seeks through the index to find rows matching the WHERE clause. Second, it goes back to the main table to fetch the columns in the SELECT that are not part of the index key. That second step is called a \u003Cstrong\u003Eprobe scan\u003C/strong\u003E, and it adds a row store round-trip for every row the index returns.\u003C/p\u003E\n","\u003Cp\u003E\u003Ccode\u003EINCLUDE\u003C/code\u003E lets you store additional columns inside the index itself. When all columns referenced by a query are available in the index, Snowflake can skip the probe scan entirely and return results from the index alone.\u003C/p\u003E\n","\u003Cp\u003EThink of it this way: without \u003Ccode\u003EINCLUDE\u003C/code\u003E, the index is an address book -- it tells you where each row lives, then you go and fetch it. With \u003Ccode\u003EINCLUDE\u003C/code\u003E, the index carries a copy of the data you need, so you never leave the index to get your answer.\u003C/p\u003E\n","\u003Cp\u003EThe queries above retrieve \u003Ccode\u003Eorder_id, customer_id, created_at, total_amount\u003C/code\u003E, but the index only stores \u003Ccode\u003Estatus\u003C/code\u003E, \u003Ccode\u003Eregion\u003C/code\u003E, and \u003Ccode\u003Ecreated_at\u003C/code\u003E. For each row matched by the index, Hybrid Table storage must perform a \u003Cstrong\u003Eprobe scan\u003C/strong\u003E back to the primary store to fetch the remaining columns (\u003Ccode\u003Ecustomer_id\u003C/code\u003E, \u003Ccode\u003Etotal_amount\u003C/code\u003E).\u003C/p\u003E\n","\u003Cp\u003EFor hot query paths with very tight latency requirements, you can eliminate this probe scan by using \u003Ccode\u003EINCLUDE\u003C/code\u003E columns.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Drop the existing index\nDROP INDEX orders.idx_orders_status_region_ts;\n\n-- Recreate with INCLUDE columns for the most common projected columns\nCREATE INDEX idx_orders_status_region_ts\n    ON orders (status, region, created_at)\n    INCLUDE (customer_id, total_amount);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow re-run the query:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT order_id, customer_id, created_at, total_amount\nFROM orders\nWHERE status   = 'PENDING'\n  AND region   = 'US-EAST'\n  AND created_at > DATEADD(DAY, -7, CURRENT_TIMESTAMP())::TIMESTAMP_NTZ\nORDER BY created_at DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIn the Query Profile, the IndexScan will no longer have a \u003Cstrong\u003Eprobe scan\u003C/strong\u003E step above it -- the index itself provides all the projected columns. This eliminates the round-trip back to the primary store for each matching row.\u003C/p\u003E\n","\u003Ch3\u003EWhen to Use INCLUDE\u003C/h3\u003E\n","\u003Cp\u003EUse \u003Ccode\u003EINCLUDE\u003C/code\u003E when:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EThe query path is very hot (thousands of executions per second)\u003C/li\u003E\u003Cli\u003EThe projected columns are stable and predictable (always the same set)\u003C/li\u003E\u003Cli\u003EYou want to minimize row store round-trips for latency-sensitive reads\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EBe aware that \u003Ccode\u003EINCLUDE\u003C/code\u003E columns increase the storage footprint of the index because those column values are stored twice (in the primary store and in the index). Do not use \u003Ccode\u003EINCLUDE\u003C/code\u003E for wide rows or infrequently-queried paths.\u003C/p\u003E\n<!-- ------------------------ -->\n<!-- INTENT: Show that CREATE INDEX on existing tables is non-blocking (concurrent) -->\n<!-- EXPECTED_BEHAVIOR: SHOW INDEXES shows status transitioning from BUILD IN PROGRESS to ACTIVE -->\n<!-- COMMON_MISTAKE: Submitting multiple CREATE INDEX concurrently (only one build at a time) -->\n","\u003Ch2\u003EStep 5: Adding Indexes to a Live Table\u003C/h2\u003E\n","\u003Cp\u003EIn production, you will often need to add an index to a Hybrid Table that is already serving traffic. The \u003Ccode\u003ECREATE INDEX\u003C/code\u003E command builds the index concurrently, so reads and writes continue normally during the build.\u003C/p\u003E\n","\u003Cp\u003EAdd a final index for the scenario where the fulfillment system needs all orders for a specific region sorted by creation time:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE INDEX idx_orders_region_created\n    ON orders (region, created_at);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECheck the build status by running \u003Ccode\u003ESHOW INDEXES\u003C/code\u003E periodically. The index is live once the \u003Ccode\u003Estatus\u003C/code\u003E column shows \u003Ccode\u003EACTIVE\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESHOW INDEXES IN TABLE orders;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOnce all three builds complete, you should see three secondary indexes plus the primary key:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EIndex Name\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EColumns\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EIs Unique\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EStatus\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESYS_INDEX_ORDERS_PRIMARY\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EORDER_ID\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EY\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EACTIVE\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EIDX_ORDERS_CUSTOMER_ID\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECUSTOMER_ID\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EN\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EACTIVE\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EIDX_ORDERS_STATUS_REGION_TS\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESTATUS, REGION, CREATED_AT\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EN\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EACTIVE\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EIDX_ORDERS_REGION_CREATED\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EREGION, CREATED_AT\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EN\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EACTIVE\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n<!-- ------------------------ -->\n<!-- INTENT: Teach how to diagnose index usage from query profile scan modes -->\n<!-- EXPECTED_BEHAVIOR: Users can identify ROW_BASED vs COLUMN_BASED and take corrective action -->\n<!-- COMMON_MISTAKE: Confusing TableScan ROW_BASED (PK access, good) with TableScan COLUMN_BASED (full scan, bad) -->\n","\u003Ch2\u003EStep 6: Reading Query Profiles for Index Diagnosis\u003C/h2\u003E\n","\u003Cp\u003EThe most important skill for operating a Hybrid Table workload is knowing how to quickly determine whether a query is using an index or performing a full scan.\u003C/p\u003E\n","\u003Ch3\u003EThe Three Scan Patterns\u003C/h3\u003E\n","\u003Cp\u003ERun each of these queries and compare their profiles:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPattern 1: Primary key lookup (fastest)\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Direct PK lookup: TableScan, ROW_BASED, 1 row scanned\nSET SAMPLE_ORDER = (SELECT order_id FROM orders LIMIT 1);\nSELECT * FROM orders WHERE order_id = $SAMPLE_ORDER;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EExpected profile: \u003Ccode\u003ETableScan\u003C/code\u003E operator, Scan Mode = \u003Ccode\u003EROW_BASED\u003C/code\u003E, rows scanned = 1.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPattern 2: Secondary index seek (fast)\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Uses idx_orders_customer_id: IndexScan, ROW_BASED, ~50 rows scanned\nSELECT * FROM orders WHERE customer_id = 4200;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EExpected profile: \u003Ccode\u003EIndexScan\u003C/code\u003E operator, Scan Mode = \u003Ccode\u003EROW_BASED\u003C/code\u003E, index name visible in attributes.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPattern 3: Full table scan (slow)\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- No index on total_amount: TableScan, COLUMN_BASED, 500K rows scanned\nSELECT * FROM orders WHERE total_amount > 1000;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EExpected profile: \u003Ccode\u003ETableScan\u003C/code\u003E operator, Scan Mode = \u003Ccode\u003ECOLUMN_BASED\u003C/code\u003E, rows scanned = ~500,000.\u003C/p\u003E\n","\u003Ch3\u003EQuick Diagnostic Checklist\u003C/h3\u003E\n","\u003Cp\u003EWhen a Hybrid Table query is slow, open the Query Profile and check the bottom-most operator:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhat You See\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhat It Means\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\"\u003E\u003Ccode\u003EIndexScan\u003C/code\u003E, \u003Ccode\u003EROW_BASED\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EIndex in use\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECheck rows scanned -- may need more selective index\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ETableScan\u003C/code\u003E, \u003Ccode\u003EROW_BASED\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPK lookup in use\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENormal for PK filters\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ETableScan\u003C/code\u003E, \u003Ccode\u003ECOLUMN_BASED\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFull scan -- no index\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAdd a secondary index for the WHERE clause column(s)\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EAlso check the \u003Cstrong\u003EProfile Overview\u003C/strong\u003E panel (deselect all operators) for a \u003Cstrong\u003EHybrid Table Requests Throttling\u003C/strong\u003E percentage. A high throttling percentage indicates too many requests are being sent to the row store simultaneously, which is often a sign of non-selective index scans.\u003C/p\u003E\n<!-- ------------------------ -->\n<!-- INTENT: Catalog the patterns that prevent index usage so users can avoid them -->\n<!-- ANTI_PATTERNS: ILIKE, function_on_column, non_leading_column, range_before_equality, low_cardinality_standalone, non_deterministic_function -->\n","\u003Ch2\u003EStep 7: Index Anti-Patterns\u003C/h2\u003E\n","\u003Cp\u003EThe following patterns look like they should use an index but do not. Each causes a \u003Ccode\u003ECOLUMN_BASED\u003C/code\u003E full table scan.\u003C/p\u003E\n","\u003Ch3\u003EAnti-Pattern 1: \u003Ccode\u003EILIKE\u003C/code\u003E (case-insensitive LIKE)\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAvoid:\u003C/strong\u003E \u003Ccode\u003EILIKE\u003C/code\u003E always triggers a full table scan.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * FROM orders WHERE region ILIKE 'us%';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EBetter:\u003C/strong\u003E \u003Ccode\u003ELIKE\u003C/code\u003E with a constant prefix is index-eligible.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * FROM orders WHERE region LIKE 'US%';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EBest:\u003C/strong\u003E Normalize case at write time and use an equality predicate at read time.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * FROM orders WHERE region = 'US-EAST';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EAnti-Pattern 2: Function Applied to an Indexed Column\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAvoid:\u003C/strong\u003E Wrapping an indexed column in a function disqualifies the index.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * FROM orders WHERE LOWER(region) = 'us-east';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDo this instead:\u003C/strong\u003E Store normalized values at write time and query without the function.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * FROM orders WHERE region = 'US-EAST';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EAnti-Pattern 3: Non-Leading Column in Composite Index\u003C/h3\u003E\n","\u003Cp\u003EThe composite index is \u003Ccode\u003E(status, region, created_at)\u003C/code\u003E. Querying on \u003Ccode\u003Eregion\u003C/code\u003E alone skips the leading column \u003Ccode\u003Estatus\u003C/code\u003E, so the index cannot be used.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAvoid:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * FROM orders WHERE region = 'EU';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDo this instead:\u003C/strong\u003E Use the dedicated \u003Ccode\u003Eidx_orders_region_created\u003C/code\u003E index, which has \u003Ccode\u003Eregion\u003C/code\u003E as its leading column.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * FROM orders WHERE region = 'EU' AND created_at > DATEADD(DAY, -30, CURRENT_TIMESTAMP())::TIMESTAMP_NTZ;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EAnti-Pattern 4: Range Predicate on a Non-Final Column\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAvoid:\u003C/strong\u003E Placing a range predicate before equality predicates in the index breaks the seek prefix. Only the columns before the gap can be used for seeking.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE INDEX idx_bad ON orders (region, created_at, status);\n\nSELECT * FROM orders WHERE region LIKE 'US%' AND created_at > '2026-01-01' AND status = 'PENDING';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOnly \u003Ccode\u003Eregion\u003C/code\u003E (via the LIKE prefix) benefits from this index. The predicates on \u003Ccode\u003Ecreated_at\u003C/code\u003E and \u003Ccode\u003Estatus\u003C/code\u003E are applied as post-scan filters.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDo this instead:\u003C/strong\u003E Equality predicates first, range predicate last.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- idx_orders_status_region_ts (status, region, created_at) is the correct design\nSELECT * FROM orders WHERE status = 'PENDING' AND region = 'US-EAST' AND created_at > '2026-01-01'::TIMESTAMP_NTZ;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EAnti-Pattern 5: Low-Cardinality Standalone Index\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAvoid:\u003C/strong\u003E An index on a column with very few distinct values (like \u003Ccode\u003Estatus\u003C/code\u003E with 4 values) still scans a large fraction of the table and provides little benefit on its own.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE INDEX idx_status_only ON orders (status);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDo this instead:\u003C/strong\u003E Combine the low-cardinality column with higher-cardinality columns in a composite index. The index \u003Ccode\u003E(status, region, created_at)\u003C/code\u003E seeks to a much smaller subset.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE INDEX idx_orders_status_region_ts ON orders (status, region, created_at);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EAnti-Pattern 6: Non-Deterministic Session Functions in Predicates\u003C/h3\u003E\n","\u003Cp\u003EFunctions like \u003Ccode\u003ECURRENT_USER()\u003C/code\u003E, \u003Ccode\u003ECURRENT_SESSION()\u003C/code\u003E, \u003Ccode\u003ECURRENT_ACCOUNT()\u003C/code\u003E, and \u003Ccode\u003ECURRENT_ROLE()\u003C/code\u003E are evaluated at runtime and cannot be pushed to an index access predicate. A query filtering on one of these functions directly will produce a \u003Ccode\u003ECOLUMN_BASED\u003C/code\u003E full table scan regardless of whether an index exists on the column.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAvoid:\u003C/strong\u003E Using a session function directly in the predicate.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE INDEX idx_orders_created_by ON orders (created_by);\n\n-- COLUMN_BASED scan even with the index, because CURRENT_USER() cannot\n-- be pushed to an index access predicate at compile time\nSELECT * FROM orders WHERE created_by = CURRENT_USER();\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDo this instead:\u003C/strong\u003E Capture the value in a session variable first, then use the variable as the predicate.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESET active_user = CURRENT_USER();\nSELECT * FROM orders WHERE created_by = $active_user;\n\u003C/code\u003E\u003C/pre\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 schema:\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E"Analyze my Hybrid Table DDL and identify any secondary index anti-patterns. Suggest improvements based on HT index selection rules."\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E"I have a Hybrid Table with these queries: [paste queries]. Recommend the optimal composite index design, with equality columns first and range predicates last."\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E"Generate CREATE INDEX DDL for my Hybrid Table based on these WHERE clause predicates: [paste predicates]. Explain which selection rule applies to each."\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_INDEX_QS_DB;\nDROP WAREHOUSE IF EXISTS HT_INDEX_QS_WH;\nDROP ROLE IF EXISTS HT_INDEX_QS_ROLE;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Cp\u003EYou have completed the Hybrid Tables Secondary Index Design quickstart. You can now:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ECreate secondary indexes\u003C/strong\u003E at table creation time and on live, active tables\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EOrder composite index columns\u003C/strong\u003E correctly: equality predicates first, range predicate last\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EEliminate probe scans\u003C/strong\u003E using \u003Ccode\u003EINCLUDE\u003C/code\u003E columns for high-throughput query paths\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDiagnose index usage\u003C/strong\u003E by reading \u003Ccode\u003EIndexScan\u003C/code\u003E vs \u003Ccode\u003ETableScan\u003C/code\u003E operators and \u003Ccode\u003EROW_BASED\u003C/code\u003E vs \u003Ccode\u003ECOLUMN_BASED\u003C/code\u003E scan modes in query profiles\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAvoid\u003C/strong\u003E the anti-patterns (ILIKE, functions on indexed columns, non-leading column predicates) that force full table scans\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EIndex Design Decision Summary\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 Index\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESingle-column equality lookups\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EINDEX idx (col)\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMulti-column equality + one range\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EINDEX idx (eq_col1, eq_col2, range_col)\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHot read path, avoid probe scan\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EINDEX idx (key_cols) INCLUDE (projected_cols)\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAdding index to live table\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ECREATE INDEX idx ON table(col)\u003C/code\u003E, then monitor with \u003Ccode\u003ESHOW INDEXES\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELow-cardinality column alone\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECombine with high-cardinality column as leading prefix\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\"\u003EHybrid Tables Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-hybrid-index\"\u003EIndexing Hybrid Tables\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-hybrid-read-query-profiles\"\u003EAnalyze Query Profiles for Hybrid Tables\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-hybrid-best-practices\"\u003EBest Practices for Hybrid Tables\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/getting-started-with-hybrid-tables/\"\u003EGetting Started with Hybrid Tables Quickstart\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/developers/guides/hybrid-tables-performance-optimization-primer/\"\u003EHybrid Tables Performance Optimization Primer\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EFAQ and Troubleshooting\u003C/h2\u003E\n","\u003Cp\u003E\u003Cstrong\u003EQ: My query still shows COLUMN_BASED scan even after I created an index. Why?\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003ECheck these common causes in order:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EThe index build may not be complete. Run \u003Ccode\u003ESHOW INDEXES IN TABLE\u003C/code\u003E and verify \u003Ccode\u003Estatus = ACTIVE\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003EThe predicate column may not match the leading column(s) of the index.\u003C/li\u003E\u003Cli\u003EYou may have a data type mismatch (e.g., comparing \u003Ccode\u003ETIMESTAMP_LTZ\u003C/code\u003E against a \u003Ccode\u003ETIMESTAMP_NTZ\u003C/code\u003E indexed column). Add an explicit cast.\u003C/li\u003E\u003Cli\u003EThe predicate may use a disqualifying pattern: \u003Ccode\u003EILIKE\u003C/code\u003E, a function wrapping the column, or a non-deterministic session function.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cstrong\u003EQ: How many secondary indexes can I create on a single Hybrid Table?\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EThere is no hard limit on the number of secondary indexes. However, each index adds write overhead (indexes are maintained synchronously on every INSERT/UPDATE/DELETE) and increases storage consumption. Balance read performance gains against write latency requirements.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EQ: Should I use INCLUDE on every index?\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003ENo. INCLUDE columns duplicate data from the primary store into the index, increasing storage. Use INCLUDE only on hot query paths where the projected columns are stable and the latency savings justify the storage cost.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EQ: Can I create an index on a VARIANT, ARRAY, OBJECT, or GEOGRAPHY column?\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003ENo. Geospatial data types (GEOGRAPHY, GEOMETRY), semi-structured types (ARRAY, OBJECT, VARIANT), and vector types (VECTOR) are not supported in indexed columns. These columns can exist in a Hybrid Table but cannot be part of a PRIMARY KEY or secondary index.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EQ: What is the difference between IndexScan and TableScan with ROW_BASED mode?\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Ccode\u003ETableScan\u003C/code\u003E with \u003Ccode\u003EROW_BASED\u003C/code\u003E indicates a primary key lookup. \u003Ccode\u003EIndexScan\u003C/code\u003E with \u003Ccode\u003EROW_BASED\u003C/code\u003E indicates a secondary index was used. Both are efficient row store operations. \u003Ccode\u003ETableScan\u003C/code\u003E with \u003Ccode\u003ECOLUMN_BASED\u003C/code\u003E indicates a full scan of the object storage copy of the data (equivalent to a full table scan).\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EQ: My index build is stuck at BUILD IN PROGRESS. What should I do?\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EOnly one index build can run at a time per table. If you submitted multiple \u003Ccode\u003ECREATE INDEX\u003C/code\u003E commands, they queue sequentially. Wait for each to complete before expecting the next to start. If a build appears stuck for an extended period, check for long-running transactions that may be blocking the build.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EQ: Do bound variables affect index usage?\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EBound variables (parameterized queries) do not change whether an index is used, but they are critical for performance. When you use bound variables, Snowflake can cache the compiled query plan and reuse it across executions. With string literals, each unique value produces a new plan compilation. For high-throughput OLTP workloads, always use bound variables.\u003C/p\u003E"],"description":"","title":"Secondary Index Design for Hybrid Tables","elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!--\nkeywords: hybrid table, secondary index, CREATE INDEX, INCLUDE columns, covering index, composite index, index design, query profile, IndexScan, TableScan, ROW_BASED, COLUMN_BASED, OLTP, Unistore, point lookup, range scan, probe scan, bound variables, TIMESTAMP_NTZ\nrelated_concepts: primary key, B-tree index, predicate pushdown, plan cache, query optimization, data type matching, cardinality, selectivity\nprerequisite_guides: getting-started-with-hybrid-tables, hybrid-tables-performance-optimization-primer\nskill_level: intermediate\nestimated_time_minutes: 45\nsnowflake_features: hybrid_tables, secondary_indexes, include_columns, create_index, show_indexes, query_profile\n--\u003E\n\n# Secondary Index Design for Hybrid Tables\n\u003C!-- ------------------------ --\u003E\n## Overview\n\n\u003E **Note on Production Workloads:** The SQL examples in this quickstart use string literals and session variables for clarity. Production OLTP workloads should use bound variables (parameterized queries) so Snowflake can cache and reuse query plans, which is critical for high-throughput workloads. See [Hybrid Tables Best Practices](https://docs.snowflake.com/en/user-guide/tables-hybrid-best-practices) and [Performance Testing for Hybrid Tables](https://docs.snowflake.com/en/user-guide/tables-hybrid-test).\n\nA [Hybrid Table](https://docs.snowflake.com/en/user-guide/tables-hybrid) stores its data in a row-oriented store. Queries against a Hybrid Table typically resolve to one of three access patterns: row-based primary-key access, row-based secondary-index access, or column-based scans, depending on predicates, indexes, and query shape.\n\nUnlike standard Snowflake tables, which can use micro-partition pruning, result cache, and clustering, Hybrid Tables depend heavily on well-designed primary and secondary indexes, along with plan cache warm-up and warehouse cache, to achieve low latency.\n\nThis quickstart teaches you how to design secondary indexes correctly, validate them using query profiles, and avoid the most common index anti-patterns that cause full table scans.\n\n### Scenario\n\nYou are building the order management backend for an e-commerce platform. Orders are written by your application in real time and read by customer service agents, fulfillment systems, and operational dashboards. You will model this workload using Hybrid Tables and progressively improve query performance through index design.\n\n### What You Will Learn\n\n- How Hybrid Table indexes differ from standard table clustering\n- How to create single-column and composite secondary indexes\n- The **equality-first, range-last** rule for composite index column ordering\n- How to create **covering indexes** with `INCLUDE` columns to eliminate probe scans\n- How to add indexes to a live, actively-used Hybrid Table without downtime\n- How to read query profiles to confirm index usage\n- The predicates and patterns that **disqualify** a query from index use\n\n### Prerequisites\n\n- A Snowflake paid account in an AWS or Azure commercial region (Hybrid Tables are not available in GCP, government regions, or trial accounts)\n- Familiarity with SQL and Snowflake Snowsight\n- ACCOUNTADMIN is used in this quickstart to set up the role, warehouse, and database. Hybrid Tables can be created on any existing database or schema as long as your role has `CREATE TABLE` privileges on that schema — ACCOUNTADMIN is not required for the Hybrid Table itself. Contact your Snowflake administrator if you need `CREATE TABLE` granted on an existing schema.\n- To add secondary indexes to an existing Hybrid Table, the role must also have `SELECT` on the table\n\n\u003E **Note:** Queries executed in Snowsight carry additional overhead compared to driver-based access. The absolute latency numbers you observe here will be higher than what your application achieves via JDBC/Python/Node.js. The *relative* difference between indexed and unindexed queries is the key signal to observe.\n\n\u003C!-- ------------------------ --\u003E\n## Setup\n\nOpen a new SQL Worksheet in Snowsight and run the following to create an isolated environment for this quickstart.\n\n### Create Role, Warehouse, and Database\n\n```sql\nUSE ROLE ACCOUNTADMIN;\n\nCREATE OR REPLACE ROLE HT_INDEX_QS_ROLE;\nGRANT ROLE HT_INDEX_QS_ROLE TO ROLE ACCOUNTADMIN;\n\nCREATE OR REPLACE WAREHOUSE HT_INDEX_QS_WH\n  WAREHOUSE_SIZE = XSMALL\n  AUTO_SUSPEND = 300\n  AUTO_RESUME = TRUE;\nGRANT OWNERSHIP ON WAREHOUSE HT_INDEX_QS_WH TO ROLE HT_INDEX_QS_ROLE;\n\nCREATE OR REPLACE DATABASE HT_INDEX_QS_DB;\nGRANT OWNERSHIP ON DATABASE HT_INDEX_QS_DB TO ROLE HT_INDEX_QS_ROLE;\n\nCREATE OR REPLACE SCHEMA HT_INDEX_QS_DB.ORDERS;\nGRANT OWNERSHIP ON SCHEMA HT_INDEX_QS_DB.ORDERS TO ROLE HT_INDEX_QS_ROLE;\n\nUSE ROLE HT_INDEX_QS_ROLE;\nUSE WAREHOUSE HT_INDEX_QS_WH;\nUSE DATABASE HT_INDEX_QS_DB;\nUSE SCHEMA ORDERS;\n```\n\n### Create the Orders Table (No Secondary Indexes Yet)\n\nYou will start with a Hybrid Table that has **only a primary key**. The primary key creates an index automatically; all other columns will require full table scans until you add secondary indexes.\n\n\u003E **Note:** In production, best practice is to define secondary indexes at table creation time using the `INDEX` clause in the CREATE HYBRID TABLE statement (e.g., `INDEX idx_customer (customer_id)`). This quickstart adds indexes separately after the table is created so you can observe the before/after effect in query profiles. See [Indexing Hybrid Tables](https://docs.snowflake.com/en/user-guide/tables-hybrid-index) for the inline syntax.\n\n```sql\nCREATE OR REPLACE HYBRID TABLE orders (\n    order_id     NUMBER        NOT NULL AUTOINCREMENT PRIMARY KEY,\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    updated_at   TIMESTAMP_NTZ,\n    total_amount NUMBER(12,2)  NOT NULL,\n    created_by   VARCHAR(100)  NOT NULL\n);\n```\n\n### Load Sample Data\n\nInsert 500,000 orders spread across 10,000 customers, four statuses, and four regions, spanning the past 90 days.\n\n```sql\nINSERT INTO orders (customer_id, status, region, created_at, updated_at, total_amount, created_by)\nSELECT\n    UNIFORM(1, 10000, RANDOM())::NUMBER               AS customer_id,\n    ARRAY_CONSTRUCT('PENDING','SHIPPED','DELIVERED','CANCELLED')\n        [UNIFORM(0, 3, RANDOM())]::VARCHAR            AS status,\n    ARRAY_CONSTRUCT('US-EAST','US-WEST','EU','APAC')\n        [UNIFORM(0, 3, RANDOM())]::VARCHAR            AS region,\n    DATEADD(SECOND,\n        UNIFORM(0, 7776000, RANDOM()),\n        DATEADD(DAY, -90, CURRENT_TIMESTAMP())::TIMESTAMP_NTZ)  AS created_at,\n    NULL                                              AS updated_at,\n    ROUND(UNIFORM(5.00, 2500.00, RANDOM()), 2)        AS total_amount,\n    'user_' || UNIFORM(1, 500, RANDOM())::VARCHAR     AS created_by\nFROM TABLE(GENERATOR(ROWCOUNT =\u003E 500000));\n```\n\nConfirm the row count:\n\n```sql\nSELECT COUNT(*) FROM orders;\n-- Expected: 500000\n```\n\nConfirm there are no secondary indexes yet:\n\n```sql\nSHOW INDEXES IN TABLE orders;\n-- Should show only the PRIMARY KEY index\n```\n\n\u003C!-- ------------------------ --\u003E\n\u003C!-- INTENT: Show that queries without a matching index cause COLUMN_BASED full scans --\u003E\n\u003C!-- EXPECTED_BEHAVIOR: Query profile shows TableScan with Scan Mode = COLUMN_BASED --\u003E\n\u003C!-- COMMON_MISTAKE: Assuming Hybrid Tables use micro-partition pruning like standard tables --\u003E\n## Step 1: The Cost of a Missing Index\n\nBefore adding any secondary indexes, run a query that is typical for a customer service lookup: **find all orders for a specific customer**.\n\n```sql\n-- Pick a real customer_id from the data\nSET SAMPLE_CUSTOMER = (SELECT customer_id FROM orders LIMIT 1);\n\n-- Customer lookup: no secondary index exists\nSELECT order_id, status, created_at, total_amount\nFROM orders\nWHERE customer_id = $SAMPLE_CUSTOMER\nORDER BY created_at DESC;\n```\n\nAfter this query runs, open the **Query Profile** in Snowsight (click the query ID, then select View Query Profile). Select the **TableScan** operator at the bottom of the plan tree.\n\nYou will see:\n\n| Attribute | Value |\n|-----------|-------|\n| **Scan Mode** | `COLUMN_BASED` |\n| **Rows scanned** | ~500,000 (the entire table) |\n\n`COLUMN_BASED` scan mode means Snowflake could not use the row store or any index, so it fell back to scanning the object storage copy of the data. This is the equivalent of a full table scan on a traditional RDBMS.\n\n\u003E **Note:** Any query on a Hybrid Table that does not filter on the primary key or a secondary index column will produce a `COLUMN_BASED` scan. For an OLTP workload with thousands of queries per second, this is the primary cause of high latency (hundreds of milliseconds instead of single-digit milliseconds).\n\n\u003C!-- ------------------------ --\u003E\n\u003C!-- INTENT: Demonstrate CREATE INDEX on a single column and confirm ROW_BASED IndexScan in profile --\u003E\n\u003C!-- EXPECTED_BEHAVIOR: IndexScan operator appears; Scan Mode = ROW_BASED; rows scanned proportional to matching rows only --\u003E\n\u003C!-- COMMON_MISTAKE: Not waiting for index build to complete (status must be ACTIVE before testing) --\u003E\n## Step 2: Single-Column Secondary Index\n\nAdd a secondary index on `customer_id`, the most frequent lookup predicate in this workload.\n\n```sql\nCREATE INDEX idx_orders_customer_id ON orders (customer_id);\n```\n\nThe index is built concurrently, so the table remains available for reads and writes. Check the build status:\n\n```sql\nSHOW INDEXES IN TABLE orders;\n-- Wait until the status column shows ACTIVE for idx_orders_customer_id\n```\n\n\u003E **Note:** Only one index build can run at a time. If you submit multiple `CREATE INDEX` statements, they will queue.\n\nNow re-run the same customer lookup:\n\n```sql\nSELECT order_id, status, created_at, total_amount\nFROM orders\nWHERE customer_id = $SAMPLE_CUSTOMER\nORDER BY created_at DESC;\n```\n\nOpen the Query Profile again. The plan tree now shows an **IndexScan** operator (not TableScan) at the bottom.\n\n| Attribute | Value |\n|-----------|-------|\n| **Scan Mode** | `ROW_BASED` |\n| **Index name** | `IDX_ORDERS_CUSTOMER_ID` |\n| **Access predicates** | `ORDERS.CUSTOMER_ID = (:SFAP_PRE_NR_1)` |\n| **Rows scanned** | ~50 (rows matching that customer_id) |\n\n`ROW_BASED` scan mode with an IndexScan means the query went directly to the relevant rows in the row store, with no full scan. You should see a significant latency improvement; exact numbers vary by data distribution, row count, and warehouse state.\n\n### How It Works\n\nWhen you create `idx_orders_customer_id`, Hybrid Table storage builds a separate B-tree structure in the row store keyed by `customer_id`. When a query filters on `customer_id = \u003Cvalue\u003E`, the query optimizer seeks directly to matching entries in this index B-tree, then fetches only those rows from the primary store. The number of rows scanned is proportional to how many orders that customer has, not the total table size.\n\n\u003C!-- ------------------------ --\u003E\n\u003C!-- INTENT: Teach composite index column ordering — equality columns first, range column last --\u003E\n\u003C!-- EXPECTED_BEHAVIOR: All three predicates pushed as access predicates when order is correct --\u003E\n\u003C!-- COMMON_MISTAKE: Putting range predicate (timestamp) as leading column; putting low-cardinality boolean first --\u003E\n## Step 3: Composite Indexes: Equality First, Range Last\n\nA common operational query is: **find all PENDING orders in a specific region, created in the last 7 days**. This query has:\n- Two equality predicates (`status`, `region`)\n- One range predicate (`created_at \u003E`)\n\nThe column ordering in a composite index matters. The rule is:\n\n\u003E **Rule:** Equality predicates come first. A range predicate comes last. Only one range predicate can benefit from the index.\n\n### Correct Design\n\n```sql\nCREATE INDEX idx_orders_status_region_ts\n    ON orders (status, region, created_at);\n```\n\nThis index can be used when:\n- Querying on `status` alone\n- Querying on `status` + `region`\n- Querying on `status` + `region` + a range on `created_at`\n\nRun the operational query:\n\n```sql\nSELECT order_id, customer_id, created_at, total_amount\nFROM orders\nWHERE status   = 'PENDING'\n  AND region   = 'US-EAST'\n  AND created_at \u003E DATEADD(DAY, -7, CURRENT_TIMESTAMP())::TIMESTAMP_NTZ\nORDER BY created_at DESC;\n```\n\nThe query profile will show an **IndexScan** on `IDX_ORDERS_STATUS_REGION_TS` with all three predicates pushed as access predicates.\n\n\u003E **Note:** Predicate values must match the data type of the indexed column. `CURRENT_TIMESTAMP()` returns `TIMESTAMP_LTZ`. Since `created_at` is declared as `TIMESTAMP_NTZ`, the `::TIMESTAMP_NTZ` cast is required for the predicate to be pushed to the index. Without it, the implicit type conversion causes a full table scan.\n\n### Why Column Order Matters\n\nConsider what would happen if you reversed the columns to `INDEX (created_at, status, region)`:\n\n- The index B-tree is sorted by `created_at` first.\n- A filter on `status = 'PENDING'` **cannot** be pushed to the index seek position because `status` is not a leading key.\n- The query would require scanning the entire time range to find matching statuses.\n\nThe rule is: order composite indexes to match your most common query patterns. Equality predicates go first, and the range predicate goes last.\n\n### Verify Index Selectivity\n\nBefore investing in an index, check how selective your predicate is:\n\n```sql\n-- How many distinct customer_ids? High = good selectivity\nSELECT APPROX_COUNT_DISTINCT(customer_id) AS distinct_customers FROM orders;\n\n-- Distribution of status values? Low cardinality -- index alone on status is weak\nSELECT status, COUNT(*) FROM orders GROUP BY status ORDER BY 2 DESC;\n```\n\n\u003E **Note:** A single-column index on a low-cardinality column like `status` (only 4 values) is a poor standalone index because it still scans a large fraction of the table. It becomes more selective as the **leading** column of a composite index that also constrains `region` and `created_at`.\n\n**Ordering within equality columns:** Within the equality prefix of a composite index, higher-cardinality columns should generally lead. A boolean column like `is_active` (2 distinct values) as the leading key means every seek still touches roughly half the index. A higher-cardinality column like `region` (4 values) or `customer_id` (10,000 values) narrows the seek range considerably.\n\n```sql\n-- Less effective: boolean column leads, large fraction of index scanned on every seek\nCREATE INDEX idx_poor ON orders (is_active, region, created_at);\n\n-- Better: higher-cardinality column leads, much smaller seek range\nCREATE INDEX idx_better ON orders (region, is_active, created_at);\n```\n\n\u003C!-- ------------------------ --\u003E\n\u003C!-- INTENT: Show how INCLUDE columns create a covering index that eliminates the probe scan --\u003E\n\u003C!-- EXPECTED_BEHAVIOR: No probe scan in query profile; IndexScan returns all projected columns directly --\u003E\n\u003C!-- COMMON_MISTAKE: Including too many columns (increases storage and write overhead for marginal benefit) --\u003E\n## Step 4: Covering Indexes with INCLUDE\n\nWhen a query uses a secondary index, Snowflake performs two operations. First, it seeks through the index to find rows matching the WHERE clause. Second, it goes back to the main table to fetch the columns in the SELECT that are not part of the index key. That second step is called a **probe scan**, and it adds a row store round-trip for every row the index returns.\n\n`INCLUDE` lets you store additional columns inside the index itself. When all columns referenced by a query are available in the index, Snowflake can skip the probe scan entirely and return results from the index alone.\n\nThink of it this way: without `INCLUDE`, the index is an address book -- it tells you where each row lives, then you go and fetch it. With `INCLUDE`, the index carries a copy of the data you need, so you never leave the index to get your answer.\n\nThe queries above retrieve `order_id, customer_id, created_at, total_amount`, but the index only stores `status`, `region`, and `created_at`. For each row matched by the index, Hybrid Table storage must perform a **probe scan** back to the primary store to fetch the remaining columns (`customer_id`, `total_amount`).\n\nFor hot query paths with very tight latency requirements, you can eliminate this probe scan by using `INCLUDE` columns.\n\n```sql\n-- Drop the existing index\nDROP INDEX orders.idx_orders_status_region_ts;\n\n-- Recreate with INCLUDE columns for the most common projected columns\nCREATE INDEX idx_orders_status_region_ts\n    ON orders (status, region, created_at)\n    INCLUDE (customer_id, total_amount);\n```\n\nNow re-run the query:\n\n```sql\nSELECT order_id, customer_id, created_at, total_amount\nFROM orders\nWHERE status   = 'PENDING'\n  AND region   = 'US-EAST'\n  AND created_at \u003E DATEADD(DAY, -7, CURRENT_TIMESTAMP())::TIMESTAMP_NTZ\nORDER BY created_at DESC;\n```\n\nIn the Query Profile, the IndexScan will no longer have a **probe scan** step above it -- the index itself provides all the projected columns. This eliminates the round-trip back to the primary store for each matching row.\n\n### When to Use INCLUDE\n\nUse `INCLUDE` when:\n- The query path is very hot (thousands of executions per second)\n- The projected columns are stable and predictable (always the same set)\n- You want to minimize row store round-trips for latency-sensitive reads\n\nBe aware that `INCLUDE` columns increase the storage footprint of the index because those column values are stored twice (in the primary store and in the index). Do not use `INCLUDE` for wide rows or infrequently-queried paths.\n\n\u003C!-- ------------------------ --\u003E\n\u003C!-- INTENT: Show that CREATE INDEX on existing tables is non-blocking (concurrent) --\u003E\n\u003C!-- EXPECTED_BEHAVIOR: SHOW INDEXES shows status transitioning from BUILD IN PROGRESS to ACTIVE --\u003E\n\u003C!-- COMMON_MISTAKE: Submitting multiple CREATE INDEX concurrently (only one build at a time) --\u003E\n## Step 5: Adding Indexes to a Live Table\n\nIn production, you will often need to add an index to a Hybrid Table that is already serving traffic. The `CREATE INDEX` command builds the index concurrently, so reads and writes continue normally during the build.\n\nAdd a final index for the scenario where the fulfillment system needs all orders for a specific region sorted by creation time:\n\n```sql\nCREATE INDEX idx_orders_region_created\n    ON orders (region, created_at);\n```\n\nCheck the build status by running `SHOW INDEXES` periodically. The index is live once the `status` column shows `ACTIVE`.\n\n```sql\nSHOW INDEXES IN TABLE orders;\n```\n\nOnce all three builds complete, you should see three secondary indexes plus the primary key:\n\n| Index Name | Columns | Is Unique | Status |\n|------------|---------|-----------|--------|\n| SYS_INDEX_ORDERS_PRIMARY | ORDER_ID | Y | ACTIVE |\n| IDX_ORDERS_CUSTOMER_ID | CUSTOMER_ID | N | ACTIVE |\n| IDX_ORDERS_STATUS_REGION_TS | STATUS, REGION, CREATED_AT | N | ACTIVE |\n| IDX_ORDERS_REGION_CREATED | REGION, CREATED_AT | N | ACTIVE |\n\n\u003C!-- ------------------------ --\u003E\n\u003C!-- INTENT: Teach how to diagnose index usage from query profile scan modes --\u003E\n\u003C!-- EXPECTED_BEHAVIOR: Users can identify ROW_BASED vs COLUMN_BASED and take corrective action --\u003E\n\u003C!-- COMMON_MISTAKE: Confusing TableScan ROW_BASED (PK access, good) with TableScan COLUMN_BASED (full scan, bad) --\u003E\n## Step 6: Reading Query Profiles for Index Diagnosis\n\nThe most important skill for operating a Hybrid Table workload is knowing how to quickly determine whether a query is using an index or performing a full scan.\n\n### The Three Scan Patterns\n\nRun each of these queries and compare their profiles:\n\n**Pattern 1: Primary key lookup (fastest)**\n```sql\n-- Direct PK lookup: TableScan, ROW_BASED, 1 row scanned\nSET SAMPLE_ORDER = (SELECT order_id FROM orders LIMIT 1);\nSELECT * FROM orders WHERE order_id = $SAMPLE_ORDER;\n```\nExpected profile: `TableScan` operator, Scan Mode = `ROW_BASED`, rows scanned = 1.\n\n**Pattern 2: Secondary index seek (fast)**\n```sql\n-- Uses idx_orders_customer_id: IndexScan, ROW_BASED, ~50 rows scanned\nSELECT * FROM orders WHERE customer_id = 4200;\n```\nExpected profile: `IndexScan` operator, Scan Mode = `ROW_BASED`, index name visible in attributes.\n\n**Pattern 3: Full table scan (slow)**\n```sql\n-- No index on total_amount: TableScan, COLUMN_BASED, 500K rows scanned\nSELECT * FROM orders WHERE total_amount \u003E 1000;\n```\nExpected profile: `TableScan` operator, Scan Mode = `COLUMN_BASED`, rows scanned = ~500,000.\n\n### Quick Diagnostic Checklist\n\nWhen a Hybrid Table query is slow, open the Query Profile and check the bottom-most operator:\n\n| What You See | What It Means | Action |\n|---|---|---|\n| `IndexScan`, `ROW_BASED` | Index in use | Check rows scanned -- may need more selective index |\n| `TableScan`, `ROW_BASED` | PK lookup in use | Normal for PK filters |\n| `TableScan`, `COLUMN_BASED` | Full scan -- no index | Add a secondary index for the WHERE clause column(s) |\n\nAlso check the **Profile Overview** panel (deselect all operators) for a **Hybrid Table Requests Throttling** percentage. A high throttling percentage indicates too many requests are being sent to the row store simultaneously, which is often a sign of non-selective index scans.\n\n\u003C!-- ------------------------ --\u003E\n\u003C!-- INTENT: Catalog the patterns that prevent index usage so users can avoid them --\u003E\n\u003C!-- ANTI_PATTERNS: ILIKE, function_on_column, non_leading_column, range_before_equality, low_cardinality_standalone, non_deterministic_function --\u003E\n## Step 7: Index Anti-Patterns\n\nThe following patterns look like they should use an index but do not. Each causes a `COLUMN_BASED` full table scan.\n\n### Anti-Pattern 1: `ILIKE` (case-insensitive LIKE)\n\n**Avoid:** `ILIKE` always triggers a full table scan.\n\n```sql\nSELECT * FROM orders WHERE region ILIKE 'us%';\n```\n\n**Better:** `LIKE` with a constant prefix is index-eligible.\n\n```sql\nSELECT * FROM orders WHERE region LIKE 'US%';\n```\n\n**Best:** Normalize case at write time and use an equality predicate at read time.\n\n```sql\nSELECT * FROM orders WHERE region = 'US-EAST';\n```\n\n### Anti-Pattern 2: Function Applied to an Indexed Column\n\n**Avoid:** Wrapping an indexed column in a function disqualifies the index.\n\n```sql\nSELECT * FROM orders WHERE LOWER(region) = 'us-east';\n```\n\n**Do this instead:** Store normalized values at write time and query without the function.\n\n```sql\nSELECT * FROM orders WHERE region = 'US-EAST';\n```\n\n### Anti-Pattern 3: Non-Leading Column in Composite Index\n\nThe composite index is `(status, region, created_at)`. Querying on `region` alone skips the leading column `status`, so the index cannot be used.\n\n**Avoid:**\n\n```sql\nSELECT * FROM orders WHERE region = 'EU';\n```\n\n**Do this instead:** Use the dedicated `idx_orders_region_created` index, which has `region` as its leading column.\n\n```sql\nSELECT * FROM orders WHERE region = 'EU' AND created_at \u003E DATEADD(DAY, -30, CURRENT_TIMESTAMP())::TIMESTAMP_NTZ;\n```\n\n### Anti-Pattern 4: Range Predicate on a Non-Final Column\n\n**Avoid:** Placing a range predicate before equality predicates in the index breaks the seek prefix. Only the columns before the gap can be used for seeking.\n\n```sql\nCREATE INDEX idx_bad ON orders (region, created_at, status);\n\nSELECT * FROM orders WHERE region LIKE 'US%' AND created_at \u003E '2026-01-01' AND status = 'PENDING';\n```\n\nOnly `region` (via the LIKE prefix) benefits from this index. The predicates on `created_at` and `status` are applied as post-scan filters.\n\n**Do this instead:** Equality predicates first, range predicate last.\n\n```sql\n-- idx_orders_status_region_ts (status, region, created_at) is the correct design\nSELECT * FROM orders WHERE status = 'PENDING' AND region = 'US-EAST' AND created_at \u003E '2026-01-01'::TIMESTAMP_NTZ;\n```\n\n### Anti-Pattern 5: Low-Cardinality Standalone Index\n\n**Avoid:** An index on a column with very few distinct values (like `status` with 4 values) still scans a large fraction of the table and provides little benefit on its own.\n\n```sql\nCREATE INDEX idx_status_only ON orders (status);\n```\n\n**Do this instead:** Combine the low-cardinality column with higher-cardinality columns in a composite index. The index `(status, region, created_at)` seeks to a much smaller subset.\n\n```sql\nCREATE INDEX idx_orders_status_region_ts ON orders (status, region, created_at);\n```\n\n### Anti-Pattern 6: Non-Deterministic Session Functions in Predicates\n\nFunctions like `CURRENT_USER()`, `CURRENT_SESSION()`, `CURRENT_ACCOUNT()`, and `CURRENT_ROLE()` are evaluated at runtime and cannot be pushed to an index access predicate. A query filtering on one of these functions directly will produce a `COLUMN_BASED` full table scan regardless of whether an index exists on the column.\n\n**Avoid:** Using a session function directly in the predicate.\n\n```sql\nCREATE INDEX idx_orders_created_by ON orders (created_by);\n\n-- COLUMN_BASED scan even with the index, because CURRENT_USER() cannot\n-- be pushed to an index access predicate at compile time\nSELECT * FROM orders WHERE created_by = CURRENT_USER();\n```\n\n**Do this instead:** Capture the value in a session variable first, then use the variable as the predicate.\n\n```sql\nSET active_user = CURRENT_USER();\nSELECT * FROM orders WHERE created_by = $active_user;\n```\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 schema:\n\n\u003E \"Analyze my Hybrid Table DDL and identify any secondary index anti-patterns. Suggest improvements based on HT index selection rules.\"\n\n\u003E \"I have a Hybrid Table with these queries: [paste queries]. Recommend the optimal composite index design, with equality columns first and range predicates last.\"\n\n\u003E \"Generate CREATE INDEX DDL for my Hybrid Table based on these WHERE clause predicates: [paste predicates]. Explain which selection rule applies to each.\"\n\n\u003C!-- ------------------------ --\u003E\n## Cleanup\n\n```sql\nUSE ROLE ACCOUNTADMIN;\nDROP DATABASE IF EXISTS HT_INDEX_QS_DB;\nDROP WAREHOUSE IF EXISTS HT_INDEX_QS_WH;\nDROP ROLE IF EXISTS HT_INDEX_QS_ROLE;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion and Resources\n\nYou have completed the Hybrid Tables Secondary Index Design quickstart. You can now:\n\n- **Create secondary indexes** at table creation time and on live, active tables\n- **Order composite index columns** correctly: equality predicates first, range predicate last\n- **Eliminate probe scans** using `INCLUDE` columns for high-throughput query paths\n- **Diagnose index usage** by reading `IndexScan` vs `TableScan` operators and `ROW_BASED` vs `COLUMN_BASED` scan modes in query profiles\n- **Avoid** the anti-patterns (ILIKE, functions on indexed columns, non-leading column predicates) that force full table scans\n\n### Index Design Decision Summary\n\n| Scenario | Recommended Index |\n|----------|------------------|\n| Single-column equality lookups | `INDEX idx (col)` |\n| Multi-column equality + one range | `INDEX idx (eq_col1, eq_col2, range_col)` |\n| Hot read path, avoid probe scan | `INDEX idx (key_cols) INCLUDE (projected_cols)` |\n| Adding index to live table | `CREATE INDEX idx ON table(col)`, then monitor with `SHOW INDEXES` |\n| Low-cardinality column alone | Combine with high-cardinality column as leading prefix |\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 Documentation](https://docs.snowflake.com/en/user-guide/tables-hybrid)\n- [Indexing Hybrid Tables](https://docs.snowflake.com/en/user-guide/tables-hybrid-index)\n- [Analyze Query Profiles for Hybrid Tables](https://docs.snowflake.com/en/user-guide/tables-hybrid-read-query-profiles)\n- [Best Practices for Hybrid Tables](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- [Getting Started with Hybrid Tables Quickstart](https://www.snowflake.com/en/developers/guides/getting-started-with-hybrid-tables/)\n- [Hybrid Tables Performance Optimization Primer](https://www.snowflake.com/en/developers/guides/hybrid-tables-performance-optimization-primer/)\n\n\u003C!-- ------------------------ --\u003E\n## FAQ and Troubleshooting\n\n**Q: My query still shows COLUMN_BASED scan even after I created an index. Why?**\n\nCheck these common causes in order:\n1. The index build may not be complete. Run `SHOW INDEXES IN TABLE` and verify `status = ACTIVE`.\n2. The predicate column may not match the leading column(s) of the index.\n3. You may have a data type mismatch (e.g., comparing `TIMESTAMP_LTZ` against a `TIMESTAMP_NTZ` indexed column). Add an explicit cast.\n4. The predicate may use a disqualifying pattern: `ILIKE`, a function wrapping the column, or a non-deterministic session function.\n\n**Q: How many secondary indexes can I create on a single Hybrid Table?**\n\nThere is no hard limit on the number of secondary indexes. However, each index adds write overhead (indexes are maintained synchronously on every INSERT/UPDATE/DELETE) and increases storage consumption. Balance read performance gains against write latency requirements.\n\n**Q: Should I use INCLUDE on every index?**\n\nNo. INCLUDE columns duplicate data from the primary store into the index, increasing storage. Use INCLUDE only on hot query paths where the projected columns are stable and the latency savings justify the storage cost.\n\n**Q: Can I create an index on a VARIANT, ARRAY, OBJECT, or GEOGRAPHY column?**\n\nNo. Geospatial data types (GEOGRAPHY, GEOMETRY), semi-structured types (ARRAY, OBJECT, VARIANT), and vector types (VECTOR) are not supported in indexed columns. These columns can exist in a Hybrid Table but cannot be part of a PRIMARY KEY or secondary index.\n\n**Q: What is the difference between IndexScan and TableScan with ROW_BASED mode?**\n\n`TableScan` with `ROW_BASED` indicates a primary key lookup. `IndexScan` with `ROW_BASED` indicates a secondary index was used. Both are efficient row store operations. `TableScan` with `COLUMN_BASED` indicates a full scan of the object storage copy of the data (equivalent to a full table scan).\n\n**Q: My index build is stuck at BUILD IN PROGRESS. What should I do?**\n\nOnly one index build can run at a time per table. If you submitted multiple `CREATE INDEX` commands, they queue sequentially. Wait for each to complete before expecting the next to start. If a build appears stuck for an extended period, check for long-running transactions that may be blocking the build.\n\n**Q: Do bound variables affect index usage?**\n\nBound variables (parameterized queries) do not change whether an index is used, but they are critical for performance. When you use bound variables, Snowflake can cache the compiled query plan and reuse it across executions. With string literals, each unique value produces a new plan compilation. For high-throughput OLTP workloads, always use bound variables.\n",":type":"text/x-markdown","multiValue":false},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image",":type":"text/plain","multiValue":false}},":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"isDeveloperGuidesPage":false,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-6002720097","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-2bbfa14c26",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-573d4aa19b","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-06-23",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-c94a2e8259","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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