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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}","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"},"responsivegrid":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","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"},"columnCount":12,":items":{"quickstart_hero":{"id":"quickstart-hero-8aa0febdc3","quickstartHeroFirstCertifiedTag":{"tagText":"Quickstart","tagColor":"#29B5E8","tagPath":"/content/cq:tags/snowflake-site/taxonomy/solution-center/certification/quickstart","tagIcon":""},"fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/hybrid-tables-performance-optimization-primer",":type":"snowflake-site/components/quickstart/quickstart-hero","isDeveloperGuidesPage":false,"quickstartHeroTitle":{"lines":["Hybrid Tables Performance Optimization Primer"],"type":"heading2",":type":"snowflake-site/components/title-v2"},"quickstartHeroAuthor":"Jon Osborn","quickstartHeroForkRepoLink":{"id":"button-61044ec5fa","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/hybrid-tables-performance-optimization-primer"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Fork Repo"},"quickstartHeroBreadcrumbs":[{"title":"Hybrid Tables Performance Optimization Primer","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers/guides/hybrid-tables-performance-optimization-primer","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-bce661d308","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":{"layout":"SIMPLE","id":"container-48ee1f32fa",":items":{"contentfragment":{"id":"contentfragment-16af5f6a61","description":"Follow this primer to learn about basic hybrid table performance optimization techniques","paragraphs":["<!-- ------------------------ -->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Ch3\u003EHybrid Tables\u003C/h3\u003E\n","\u003Cp\u003EA \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-hybrid\"\u003Ehybrid table\u003C/a\u003E is a Snowflake table type that is optimized for hybrid transactional and operational workloads that require low latency and high throughput on small random point reads and writes. A hybrid table supports unique and referential integrity constraint enforcement that is critical for transactional workloads. You can use a hybrid table along with other Snowflake tables and features to power \u003Ca href=\"/en/data-cloud/workloads/unistore/\"\u003EUnistore workloads\u003C/a\u003E that bring transactional and analytical data together in a single platform.\u003C/p\u003E\n","\u003Cp\u003EUse cases that may benefit from hybrid tables include:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EApplications requiring INSERT/UPDATE/DELETE concurrency matched with SELECT performance\u003C/li\u003E\u003Cli\u003EUsage requiring low-latency, high concurrency INSERT/UPDATE like log tables, metadata tracking, or status information\u003C/li\u003E\u003Cli\u003ELow latency responses for highly selective record retrieval over very large data sets\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EArchitecture\u003C/h3\u003E\n","\u003Cp\u003EHybrid tables are integrated seamlessly into the existing Snowflake architecture. Customers connect to the same Snowflake database service. Queries are compiled and optimized in the cloud services layer and executed in the same query engine in virtual warehouses. This provides several key benefits:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESnowflake platform features, such as data governance, work with hybrid tables out of the box.\u003C/li\u003E\u003Cli\u003EYou can run hybrid workloads mixing operational and analytical queries.\u003C/li\u003E\u003Cli\u003EYou can join hybrid tables with other Snowflake tables and the query executes natively and efficiently in the same query engine. No federation is required.\u003C/li\u003E\u003Cli\u003EYou can execute an atomic transaction across hybrid tables and other Snowflake tables. There is no need to orchestrate your own two-phase commit.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hybrid-tables-performance-optimization-primer/Architecture-Hybrid-Tables.png?v=2d1d5955\" alt=\"Architecture\"\u003E\u003C/p\u003E\n","\u003Cp\u003EHybrid tables leverage a row store as the primary data store to provide excellent operational query performance. When you write to a hybrid table, the data is written directly into the rowstore. Data is asynchronously copied into object storage in order to provide better performance and workload isolation for large scans without impacting your ongoing operational workloads. Some data may also be cached in columnar format on your warehouse in order to provide better performance on analytical queries. You simply execute SQL statements against the logical hybrid table and Snowflake’s query optimizer decides where to read data from in order to provide the best performance. You get one consistent view of your data without needing to worry about the underlying infrastructure.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Will Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to design hybrid tables and access patterns that achieve high performance\u003C/li\u003E\u003Cli\u003EHow to analyze queries using Snowsight\u003C/li\u003E\u003Cli\u003EHow to adjust queries to improve performance\u003C/li\u003E\u003Cli\u003EHow hybrid table unique characteristics like Indexes, primary keys, unique and foreign keys affect performance\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EFamiliarity with the Snowflake's Snowsight interface\u003C/li\u003E\u003Cli\u003EFamiliarity with SQL\u003C/li\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\u003EA database and/or schema within which you have permissions to create tables\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EProcess\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EWe will be creating three tables that represent a data model. Following the trucking example from the \u003Ca href=\"/en/developers/guides/getting-started-with-hybrid-tables/\"\u003EGetting Started with Hybrid Tables\u003C/a\u003E quickstart, we will use TRUCK and ORDER_HEADER tables to represent our data.\u003C/li\u003E\u003Cli\u003ECreate and work with TRUCK table to understand primary keys, secondary indexes, and accompanying query patterns\u003C/li\u003E\u003Cli\u003EBuild on TRUCK with ORDER_HEADER to work with foreign key relationships\u003C/li\u003E\u003Cli\u003EAdd a secondary index to find orders for trucks based on order date\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003ESetup\u003C/h2\u003E\n","\u003Cp\u003EWe will use table structures populated with synthetic data to create scenarios We will initially\ncreate scenarios that do not perform well. Following the guide, you will then adjust the query and/or the\ntable structure to create an optimal query.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENOTE:\u003C/strong\u003E Queries executed from Snowsight support an interactive development experience with rich statistics and metadata,\nsuch as runtime statistics about all query operators in the query profile. Queries executed in Snowsight\nwill not perform with the lowest possible latency. Lowest latency is\nachieved via queries issued from a \u003Cem\u003Ecode-based driver connection\u003C/em\u003E to Snowflake. Please review our \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-hybrid-best-practices\"\u003Ebest practices\u003C/a\u003E\nand \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-hybrid-test\"\u003Eperformance testing\u003C/a\u003E guides for more information.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ECreating an Environment\u003C/h3\u003E\n","\u003Cp\u003ECreate a worksheet and establish a database and schema to do the work. We are doing exploration work here\nso make sure you are using a role that has permissions to create tables and view query profiles.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- CUSTOMIZE AS NEEDED\n-- CREATE OR REPLACE DATABASE HT_PERFORMANCE;\n-- CREATE OR REPLACE SCHEMA HT_PERFORMANCE.PRIMER;\n\n-- CREATE OR REPLACE WAREHOUSE HT_PERFORMANCE_WAREHOUSE WITH WAREHOUSE_SIZE=XSMALL;\n    \n-- THE PRIMER WILL USE THIS SCHEMA FOR THE REMAINDER OF THE LEARNING\n-- CUSTOMIZE AS NEEDED\nUSE SCHEMA HT_PERFORMANCE.PRIMER;\nUSE WAREHOUSE HT_PERFORMANCE_WAREHOUSE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow, let's explore primary keys and secondary indexes\u003C/p\u003E\n","\u003Ch2\u003EExplore the Primary Key\u003C/h2\u003E\n","\u003Cp\u003EPrimary keys are a required configuration for a hybrid table. The key establishes the rules for row uniqueness\nas well as the sort order for how data is written to the row storage. See \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-hybrid-table#required-parameters\"\u003Eprimary key documentation\u003C/a\u003E for more details.\u003C/p\u003E\n","\u003Cp\u003ESecondary Indexes provide fast access to query patterns that may not be able to use the primary key.\u003C/p\u003E\n","\u003Ch3\u003EWhat We Will Cover\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ECreating a hybrid table with test data\u003C/li\u003E\u003Cli\u003EPrimary Key setup and analysis\u003C/li\u003E\u003Cli\u003ESecondary index setup and analysis\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ECreate the \u003Ccode\u003ETRUCKS\u003C/code\u003E Hybrid Table\u003C/h3\u003E\n","\u003Cp\u003ECreate and populate a simple hybrid table. This table is similar to the table used in the getting started with hybrid tables guide but is\nsimplified for brevity. This quickstart will use similar queries for the remainder of the work:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E--- CREATE LOTS OF TRUCKS WITH RANDOM YEARS, MAKES, FRANCHISE AND EMAILS.\nCREATE OR REPLACE HYBRID TABLE TRUCK (\n    TRUCK_ID NUMBER(38,0) NOT NULL,\n    YEAR NUMBER(38,0) NOT NULL,\n    MAKE VARCHAR(250) NOT NULL,\n    FRANCHISE_ID NUMBER(38,0) NOT NULL,\n    TRUCK_EMAIL VARCHAR NOT NULL,\n    PRIMARY KEY (TRUCK_ID) \n)\nAS SELECT\n    SEQ4(),\n    UNIFORM(1970, 2025, RANDOM()),\n    ARRAY_CONSTRUCT( 'Freightliner', 'Peterbilt', 'Kenworth', 'Volvo Trucks', 'Mack', 'International (Navistar)', 'Western Star')[UNIFORM(0,6, RANDOM())],\n    UNIFORM(1, 250, RANDOM()),\n    RANDSTR(8, RANDOM()) || '@sf-quickstart'\nFROM TABLE(GENERATOR(ROWCOUNT => 1000000)) -- WE NEED ENOUGH RECORDS FOR REASONABLE TESTING OTHERWISE EVERYTHING IS IN-MEMORY\n;\n-- SEE WHAT THE RESULTS LOOK LIKE\nSELECT * FROM TRUCK LIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EQuery with the Primary Key\u003C/h3\u003E\n","\u003Cp\u003EPrimary keys are a highly performant way to query hybrid tables. Let's look at a query profile of what querying using the primary key looks like.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- SELECT AND SET A RANDOM TRUCK_ID TO SESSION VARIABLE FOR CONVENIENCE\nSET TRUCK_ID = (SELECT TRUCK_ID FROM TRUCK SAMPLE(1 ROWS));\n\nSELECT *\nFROM TRUCK\nWHERE TRUCK_ID = $TRUCK_ID;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow, click on the resulting query id (QID) on the Snowsight console. A new tab will open, and you will be presented with a screen that looks like this:\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hybrid-tables-performance-optimization-primer/explore_00.png?v=2d1d5955\" alt=\"assets/explore_00.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe screen gives you basic information about the query.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENOTE:\u003C/strong\u003E As this query was executed on Snowsight, compile and execution times will be longer than connected driver based queries.\nReview \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-hybrid-best-practices\"\u003Ehybrid table best practices\u003C/a\u003E to learn more.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EClicking on the "Query Profile" button:\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hybrid-tables-performance-optimization-primer/explore_10.png?v=2d1d5955\" alt=\"assets/explore_10.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003Ewill lead you to a screen showing you the visual representation of how the query planner decided\nto execute the query:\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hybrid-tables-performance-optimization-primer/explore_20.png?v=2d1d5955\" alt=\"assets/explore_20.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThis is the primary screen we will be using for query analysis.\u003C/p\u003E\n","\u003Ch3\u003EAnalyze the Query Profile Output\u003C/h3\u003E\n","\u003Cp\u003ENow that you understand how to get to the query profile screen, let's discover how this query was executed. Once we understand how the query planner decided\nto execute the query, we can understand how to implement any changes in the query that will improve performance.\u003C/p\u003E\n","\u003Cp\u003EClicking on the bottom "TableScan" node will focus our attention on what happened in the data access.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hybrid-tables-performance-optimization-primer/explore_30.png?v=2d1d5955\" alt=\"assets/explore_30.png\"\u003E\nIn this example we can see that:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EThe Access Predicate was indeed the primary key \u003Ccode\u003ETRUCK.TRUCK_ID\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EThe Scan Mode was \u003Ccode\u003EROW_BASED\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EExactly one record was retrieved from storage by the scan, exactly matching the truck ID.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Ccode\u003EROW_BASED\u003C/code\u003E scans means that the query directly accessed the row store and did not use the backing object store to produce the results.\nFor selective predicates against the primary key, \u003Ccode\u003EROW_BASED\u003C/code\u003E access indicates that the primary key index was used to\npush down the predicate and is the fastest and most efficient way to execute the query.\u003C/p\u003E\n","\u003Ch3\u003EQuery with \u003Ccode\u003ECOLUMN_BASED\u003C/code\u003E Access (no key)\u003C/h3\u003E\n","\u003Cp\u003EInstead of querying the table using the primary key, let's query the table using a column that is \u003Cstrong\u003Enot\u003C/strong\u003E indexed and see what\nthe resulting query plan looks like.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESET EMAIL = (SELECT ANY_VALUE(TRUCK_EMAIL) FROM TRUCK);\n    \nSELECT *\nFROM TRUCK\nWHERE TRUE\n    AND TRUCK_EMAIL = $EMAIL;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe can see from the query profile screen that the query plan looks far different. Much time is spent on the table scan and it\nis the most expensive node in the plan:\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hybrid-tables-performance-optimization-primer/explore_40.png?v=2d1d5955\" alt=\"assets/explore_40.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003ELooking at the "TableScan" node, we can clearly see that the scan was \u003Ccode\u003ECOLUMN_BASED\u003C/code\u003E and processed many partitions with lots of data.\nThe optimizer was not able to push down any access predicates against an index.\u003C/p\u003E\n","\u003Cp\u003ENext, we will explore secondary indexes.\u003C/p\u003E\n","\u003Ch2\u003EExplore Secondary Indexes\u003C/h2\u003E\n","\u003Ch3\u003EAdd a Secondary Index\u003C/h3\u003E\n","\u003Cp\u003EClearly, the above query is not optimal. A secondary index can help the query run faster. Let's create a secondary index\nand use it to explore the query improvement:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE HYBRID TABLE TRUCK (\n    TRUCK_ID NUMBER(38,0) NOT NULL,\n    YEAR NUMBER(38,0) NOT NULL,\n    MAKE VARCHAR(250) NOT NULL,\n    FRANCHISE_ID NUMBER(38,0) NOT NULL,\n    TRUCK_EMAIL VARCHAR NOT NULL UNIQUE,\n    PRIMARY KEY (TRUCK_ID),\n    INDEX IDX_TRUCK_EMAIL (TRUCK_EMAIL)\n)\nAS SELECT\n    SEQ4(),\n    UNIFORM(1970, 2025, RANDOM()),\n    ARRAY_CONSTRUCT( 'Freightliner', 'Peterbilt', 'Kenworth', 'Volvo Trucks', 'Mack', 'International (Navistar)', 'Western Star')[UNIFORM(0,6, RANDOM())],\n    UNIFORM(1, 250, RANDOM()),\n    RANDSTR(8, RANDOM()) || '@sf-quickstart'\nFROM TABLE(GENERATOR(ROWCOUNT => 1000000)) -- WE NEED ENOUGH RECORDS FOR REASONABLE TESTING OTHERWISE EVERYTHING IS IN-MEMORY\n;\n-- SEE WHAT THE RESULTS LOOK LIKE\nSELECT * FROM TRUCK LIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOnce the table is created we can re-run the query and show that it is using the newly created index:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESET EMAIL = (SELECT ANY_VALUE(TRUCK_EMAIL) FROM TRUCK);\n\nSELECT *\nFROM TRUCK\nWHERE TRUE\n  AND TRUCK_EMAIL = $EMAIL;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EUsing the index results in a much better query plan that uses the index we created:\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hybrid-tables-performance-optimization-primer/explore_61.png?v=2d1d5955\" alt=\"assets/explore_61.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENext, we will cover foreign keys.\u003C/p\u003E\n","\u003Ch2\u003EExplore Foreign Keys\u003C/h2\u003E\n","\u003Cp\u003ERDBMS data models use foreign keys to establish relationships between tables. All kinds of models take advantage of the relationships to keep\ndata from becoming corrupted or otherwise orphaned from a definition. We call the relationship between the primary key\nof the reference table and the usage of the key in another table a \u003Cstrong\u003Eforeign key\u003C/strong\u003E.\u003C/p\u003E\n","\u003Ch3\u003ESetup\u003C/h3\u003E\n","\u003Cp\u003EWe will use a \u003Ccode\u003ETRUCK\u003C/code\u003E and \u003Ccode\u003EORDER_HEADER\u003C/code\u003E relationship to create a foreign key relationship. Let's create the tables and\nexplore how the query plan looks with and without a foreign key.\u003C/p\u003E\n","\u003Cp\u003ELet's reuse the \u003Ccode\u003ETRUCK\u003C/code\u003E table and create orders for each truck.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E--- CREATE ORDERS FOR EACH TRUCK\nCREATE OR REPLACE HYBRID TABLE ORDER_HEADER (\n\tORDER_ID NUMBER(38,0) NOT NULL,\n\tTRUCK_ID NUMBER(38,0) NOT NULL,\n    ORDER_TIMESTAMP TIMESTAMP_NTZ NOT NULL,\n\tORDER_TOTAL NUMBER(38,2),\n\tORDER_STATUS VARCHAR(200) DEFAULT 'INQUEUE',\n\tPRIMARY KEY (ORDER_ID)\n) AS\nSELECT\n    SEQ4(),\n    T.TRUCK_ID,\n    DATEADD('seconds', UNIFORM(-1*60*60*24*365, -1*60*60, RANDOM()), CURRENT_TIMESTAMP()), -- SPREAD RANDOM ORDER IDS OUT\n    UNIFORM(200.0, 25000.0, RANDOM()),\n    ARRAY_CONSTRUCT('DELIVERED', 'INQUEUE', 'LOADING', 'EN-ROUTE')[UNIFORM(0,3, RANDOM())],\nFROM TABLE(GENERATOR(ROWCOUNT => 5000))\nCROSS JOIN (SELECT TRUCK_ID FROM TRUCK SAMPLE (1000 ROWS)) T    -- ASSIGN ORDERS TO EVERY TRUCK = # TRUCKS X # ORDERS PER TRUCK\n;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EConsider a query that loads all orders for a specific truck. This query will join the \u003Ccode\u003ETRUCK\u003C/code\u003E table with the \u003Ccode\u003EORDER_HEADER\u003C/code\u003E\ntable while filtering for a particular truck.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- SELECT A TRUCK THAT HAS ORDER DETAILS\nSET TRUCK_ID = (SELECT ANY_VALUE(TRUCK_ID) FROM ORDER_HEADER);\n\n-- QUERY FOR ALL ORDERS (5000) FOR THIS PARTICULAR TRUCK \nSELECT *\nFROM TRUCK T\nINNER JOIN ORDER_HEADER O ON O.TRUCK_ID=T.TRUCK_ID\nWHERE TRUE\n  AND T.TRUCK_ID = $TRUCK_ID\n;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENote that the query optimizer does not have information to relate the two tables within the join. Thus,\nthe optimizer chooses a column scan mode when reading data from the \u003Ccode\u003EORDER_HEADER\u003C/code\u003E table. Technically, the optimizer must\ninterpret the join as a many-to-many join.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hybrid-tables-performance-optimization-primer/explore_70.png?v=2d1d5955\" alt=\"assets/explore_70.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIntroducing a \u003Ccode\u003EFOREIGN KEY\u003C/code\u003E will serve two important purposes. First, it establishes a relationship between the tables that\nguarantees a one-to-many join for a given \u003Ccode\u003EPRIMARY KEY\u003C/code\u003E in the parent table. Second, the relationship will enforce a\nrule that requires data to be consistent such that child table \u003Ccode\u003ETRUCK_ID\u003C/code\u003E values must exist in the \u003Ccode\u003ETRUCK\u003C/code\u003E primary key.\nRecreate the table with a \u003Ccode\u003EFOREIGN KEY\u003C/code\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- CREATE ORDER_HEADER WITH A FOREIGN KEY\nCREATE OR REPLACE HYBRID TABLE ORDER_HEADER (\n    ORDER_ID NUMBER(38,0) NOT NULL,\n    TRUCK_ID NUMBER(38,0) NOT NULL,\n    ORDER_TIMESTAMP TIMESTAMP_NTZ NOT NULL,\n    ORDER_TOTAL NUMBER(38,2),\n    ORDER_STATUS VARCHAR(200) DEFAULT 'INQUEUE',\n    PRIMARY KEY (ORDER_ID),\n    CONSTRAINT FK_TRUCK_ID FOREIGN KEY (TRUCK_ID) REFERENCES TRUCK(TRUCK_ID)\n);\n-- USE INSERT INTO METHOD BECAUSE THE FOREIGN KEY MUST EXIST TO BE ENFORCED\nINSERT INTO ORDER_HEADER\nSELECT\n    SEQ4(),\n    T.TRUCK_ID,\n    DATEADD('seconds', UNIFORM(-1*60*60*24*365, -1*60*60, RANDOM()), CURRENT_TIMESTAMP()), -- SPREAD RANDOM ORDER IDS OUT\n    UNIFORM(200.0, 25000.0, RANDOM()),\n    ARRAY_CONSTRUCT('DELIVERED', 'INQUEUE', 'LOADING', 'EN-ROUTE')[UNIFORM(0,3, RANDOM())],\nFROM TABLE(GENERATOR(ROWCOUNT => 5000))\nCROSS JOIN (SELECT TRUCK_ID FROM TRUCK SAMPLE (1000 ROWS)) T    -- ASSIGN ORDERS TO EVERY TRUCK = # TRUCKS X # ORDERS PER TRUCK\n;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENext, find a new \u003Ccode\u003ETRUCK_ID\u003C/code\u003E and execute the query to retrieve the order header information.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESET TRUCK_ID = (SELECT ANY_VALUE(TRUCK_ID) FROM ORDER_HEADER);\n\nSELECT *\nFROM TRUCK T\nINNER JOIN ORDER_HEADER O ON O.TRUCK_ID=T.TRUCK_ID\nWHERE TRUE\n    AND T.TRUCK_ID = $TRUCK_ID\n;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EChecking the query plan, we can see that the query optimizer is now using the foreign key to efficiently select\nthe order detail information.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hybrid-tables-performance-optimization-primer/explore_71.png?v=2d1d5955\" alt=\"assets/explore_71.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENext, consider a query to retrieve a specific order and join in details for the truck for which the order was submitted.\nThis sort of pattern is common when dealing with normalized data models in transactional applications:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- SELECT A SPECIFIC ORDER\nSET ORDER_ID = (SELECT ANY_VALUE(ORDER_ID) FROM ORDER_HEADER);\n\nSELECT *\nFROM ORDER_HEADER O\nINNER JOIN TRUCK T ON O.TRUCK_ID=T.TRUCK_ID\nWHERE TRUE\n  AND O.ORDER_ID = $ORDER_ID\n;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe query plan shows both primary keys in use loading only a single record\nfrom each table by using an Nested Loop Join pattern where we first retrieve the order record\nand then use the truck ID for that order to push down the truck lookup against the primary key of the truck table:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hybrid-tables-performance-optimization-primer/explore_72.png?v=2d1d5955\" alt=\"assets/explore_72.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENext, we will explore how secondary indexes help queries that do not use primary keys.\u003C/p\u003E\n","\u003Ch2\u003EJoining and Secondary Indexes\u003C/h2\u003E\n","\u003Cp\u003EA common query pattern is to join two tables using foreign keys and filter one of them with another column. For example,\nthis query is fetching orders over the last few days for a specific truck:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT *\nFROM ORDER_HEADER O\nINNER JOIN TRUCK T ON T.TRUCK_ID=O.TRUCK_ID\nWHERE TRUE\n    AND O.ORDER_TIMESTAMP > DATEADD('days', -2, CURRENT_TIMESTAMP()) :: TIMESTAMP_NTZ  -- NOTE THE CAST\n    AND T.TRUCK_ID = $TRUCK_ID\n;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe \u003Ccode\u003ETRUCK_ID\u003C/code\u003E will be used as a primary key filter on \u003Ccode\u003ETRUCK\u003C/code\u003E and a foreign key filter on \u003Ccode\u003EORDER_HEADER\u003C/code\u003E. The timestamp filter\nwill be applied after data was fetched from hybrid table object store. From the query profile, we can see that only the\n\u003Ccode\u003ETRUCK_ID\u003C/code\u003E was used as an access predicate. The \u003Ccode\u003EORDER_TIMESTAMP\u003C/code\u003E filtering was completed after data was fetched.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hybrid-tables-performance-optimization-primer/explore_80.png?v=2d1d5955\" alt=\"assets/explore_80.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EA secondary index on (TRUCK_ID, ORDER_TIMESTAMP) will improve performance by allowing us to also push the timestamp\nfilter down to storage:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E--- CREATE ORDERS FOR EACH TRUCK\nCREATE OR REPLACE HYBRID TABLE ORDER_HEADER (\n    ORDER_ID NUMBER(38,0) NOT NULL,\n    TRUCK_ID NUMBER(38,0) NOT NULL,\n    ORDER_TIMESTAMP TIMESTAMP_NTZ NOT NULL,\n    ORDER_TOTAL NUMBER(38,2),\n    ORDER_STATUS VARCHAR(200) DEFAULT 'INQUEUE',  -- USE DEFAULT VALUE\n    PRIMARY KEY (ORDER_ID),\n    FOREIGN KEY (TRUCK_ID) REFERENCES TRUCK(TRUCK_ID),  -- REFER TO THE TRUCK TABLE FOR TRUCK_ID\n    INDEX IDX_TRUCK_TIMESTAMPS (TRUCK_ID, ORDER_TIMESTAMP )  -- ADD THE SECONDARY INDEX FOR ORDER_TIMESTAMP\n);\nINSERT INTO ORDER_HEADER (ORDER_ID, TRUCK_ID, ORDER_TIMESTAMP, ORDER_TOTAL)\nSELECT\n    SEQ4(),\n    T.TRUCK_ID,\n    DATEADD('seconds', UNIFORM(-1*60*60*24*365, -1*60*60, RANDOM()), CURRENT_TIMESTAMP()), -- SPREAD RANDOM ORDER IDS OUT\n    UNIFORM(200.0, 25000.0, RANDOM()),\nFROM TABLE(GENERATOR(ROWCOUNT => 5000))\nCROSS JOIN (SELECT TRUCK_ID FROM TRUCK SAMPLE (1000 ROWS)) T    -- ASSIGN ORDERS TO EVERY TRUCK = # TRUCKS X # ORDERS PER TRUCK\n;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow that the index exists and is \u003Ccode\u003EACTIVE\u003C/code\u003E, we can use it in a query:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESET TRUCKID = (SELECT ANY_VALUE(TRUCK_ID) FROM ORDER_HEADER);\n\nSELECT *\nFROM ORDER_HEADER O\nINNER JOIN TRUCK T ON T.TRUCK_ID=O.TRUCK_ID\nWHERE TRUE\n    AND O.ORDER_TIMESTAMP > DATEADD('days', -1, CURRENT_TIMESTAMP()) :: TIMESTAMP_NTZ\n    AND T.TRUCK_ID = $TRUCKID\n;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow the truck and timestamp predicates are being pushed down against the index in order to reduce the amount of data\nretrieved from the order_header table improving latency and efficiency.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hybrid-tables-performance-optimization-primer/explore_83.png?v=2d1d5955\" alt=\"assets/explore_83.png\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENOTE:\u003C/strong\u003E Looking at the query in detail, you will notice that there is a cast to \u003Ccode\u003ETIMESTAMP_NTZ\u003C/code\u003E for the \u003Ccode\u003EORDER_TIMESTAMP\u003C/code\u003E\npredicate value. All types of timestamps are supported by hybrid tables, however, columns used in primary keys\nor indexes are only supported as \u003Ccode\u003ETIMESTAMP_NTZ\u003C/code\u003E. As an exercise, try the query without the cast and inspect\nthe result.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ENext, we will summarize best practices.\u003C/p\u003E\n","\u003Ch2\u003ESummarize Best Practices\u003C/h2\u003E\n","\u003Cp\u003EHybrid table performance benefits from following best practices that extract the best performance and highest value\nfrom this Snowflake feature.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EUse a \u003Ccode\u003EPRIMARY KEY\u003C/code\u003E, built from business columns, to drive the highest performance.\u003C/li\u003E\u003Cli\u003EImplement \u003Ccode\u003EFOREIGN KEYS\u003C/code\u003E when joining hybrid tables.\u003C/li\u003E\u003Cli\u003EWrite queries that use keys and indexes to keep queries in \u003Ccode\u003EROW_BASED\u003C/code\u003E mode.\u003C/li\u003E\u003Cli\u003EUse \u003Ccode\u003EINCLUDE\u003C/code\u003E columns on secondary indexes to create covering indexes that eliminate probe scans for hot query paths.\u003C/li\u003E\u003Cli\u003EUse \u003Cstrong\u003Ebound variables\u003C/strong\u003E (parameterized queries) in application code so Snowflake can cache and reuse compiled query plans.\u003C/li\u003E\u003Cli\u003EUse Snowsight query plans to verify optimal query plans and troubleshoot performance issues.\u003C/li\u003E\u003Cli\u003EPay close attention to data types, join criteria, and index utilization.\u003C/li\u003E\u003Cli\u003EFollow Snowflake \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-hybrid-best-practices\"\u003Ehybrid table best practices\u003C/a\u003E.\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EThank you for completing this Hybrid Tables Performance Optimization Primer. You now have a solid understanding of how to\nrelate query performance to the query planning tool. You will use this knowledge to help create and optimize your hybrid table solutions.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n","\u003Cp\u003EHaving completed this quickstart you have successfully\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ECreated hybrid tables with primary keys\u003C/li\u003E\u003Cli\u003EWrote and learned how to analyze a query profile\u003C/li\u003E\u003Cli\u003ELearned how to use secondary indexes and constraints\u003C/li\u003E\u003Cli\u003ECreated a child table using foreign keys referencing a parent table\u003C/li\u003E\u003Cli\u003EJoined tables and worked with secondary indexes to maximize performance\u003C/li\u003E\u003Cli\u003EUtilized secondary indexes to speed query performance across non-primary key columns\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources:\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"/en/data-cloud/workloads/unistore/\"\u003ESnowflake Unistore Landing Page\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-hybrid\"\u003ESnowflake Documentation 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=\"/en/developers/guides/hybrid-tables-jmeter-performance-testing/\"\u003EHybrid Table Performance Testing Quickstart\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/hybrid-tables-secondary-index-design/\"\u003ESecondary Index Design for Hybrid Tables\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/hybrid-tables-standard-to-hybrid-migration/\"\u003EConverting Standard Tables to Hybrid Tables\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/blog/simplify-application-development-hybrid-tables\"\u003ESimplify Application Development Hybrid Tables Blog\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"title":"Hybrid Tables Performance Optimization Primer",":items":{},":itemsOrder":[],":type":"snowflake-site/components/contentfragment","isDeveloperGuidesPage":false,"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview \n\n### Hybrid Tables\n\nA [hybrid table](https://docs.snowflake.com/en/user-guide/tables-hybrid) is a Snowflake table type that is optimized for hybrid transactional and operational workloads that require low latency and high throughput on small random point reads and writes. A hybrid table supports unique and referential integrity constraint enforcement that is critical for transactional workloads. You can use a hybrid table along with other Snowflake tables and features to power [Unistore workloads](/en/data-cloud/workloads/unistore/) that bring transactional and analytical data together in a single platform.\n\nUse cases that may benefit from hybrid tables include:\n\n- Applications requiring INSERT/UPDATE/DELETE concurrency matched with SELECT performance\n- Usage requiring low-latency, high concurrency INSERT/UPDATE like log tables, metadata tracking, or status information\n- Low latency responses for highly selective record retrieval over very large data sets\n\n### Architecture\n\nHybrid tables are integrated seamlessly into the existing Snowflake architecture. Customers connect to the same Snowflake database service. Queries are compiled and optimized in the cloud services layer and executed in the same query engine in virtual warehouses. This provides several key benefits:\n\n- Snowflake platform features, such as data governance, work with hybrid tables out of the box.\n- You can run hybrid workloads mixing operational and analytical queries.\n- You can join hybrid tables with other Snowflake tables and the query executes natively and efficiently in the same query engine. No federation is required.\n- You can execute an atomic transaction across hybrid tables and other Snowflake tables. There is no need to orchestrate your own two-phase commit.\n\n![Architecture](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hybrid-tables-performance-optimization-primer/Architecture-Hybrid-Tables.png?v=2d1d5955)\n\nHybrid tables leverage a row store as the primary data store to provide excellent operational query performance. When you write to a hybrid table, the data is written directly into the rowstore. Data is asynchronously copied into object storage in order to provide better performance and workload isolation for large scans without impacting your ongoing operational workloads. Some data may also be cached in columnar format on your warehouse in order to provide better performance on analytical queries. You simply execute SQL statements against the logical hybrid table and Snowflake’s query optimizer decides where to read data from in order to provide the best performance. You get one consistent view of your data without needing to worry about the underlying infrastructure.\n\n\n### What You Will Learn \n\n- How to design hybrid tables and access patterns that achieve high performance\n- How to analyze queries using Snowsight\n- How to adjust queries to improve performance\n- How hybrid table unique characteristics like Indexes, primary keys, unique and foreign keys affect performance\n\n### Prerequisites\n\n- Familiarity with the Snowflake's Snowsight interface\n- Familiarity with SQL\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- A database and/or schema within which you have permissions to create tables\n\n\n### Process\n- We will be creating three tables that represent a data model. Following the trucking example from the [Getting Started with Hybrid Tables](/en/developers/guides/getting-started-with-hybrid-tables/) quickstart, we will use TRUCK and ORDER_HEADER tables to represent our data.\n- Create and work with TRUCK table to understand primary keys, secondary indexes, and accompanying query patterns\n- Build on TRUCK with ORDER_HEADER to work with foreign key relationships\n- Add a secondary index to find orders for trucks based on order date\n\n## Setup\n\n\nWe will use table structures populated with synthetic data to create scenarios We will initially\ncreate scenarios that do not perform well. Following the guide, you will then adjust the query and/or the \ntable structure to create an optimal query.\n\n\u003E \n\u003E **NOTE:** Queries executed from Snowsight support an interactive development experience with rich statistics and metadata, \n\u003E such as runtime statistics about all query operators in the query profile. Queries executed in Snowsight \n\u003E will not perform with the lowest possible latency. Lowest latency is \n\u003E achieved via queries issued from a _code-based driver connection_ to Snowflake. Please review our [best practices](https://docs.snowflake.com/en/user-guide/tables-hybrid-best-practices)\n\u003E and [performance testing](https://docs.snowflake.com/en/user-guide/tables-hybrid-test) guides for more information.\n\u003E\n\n\n### Creating an Environment\n\nCreate a worksheet and establish a database and schema to do the work. We are doing exploration work here\nso make sure you are using a role that has permissions to create tables and view query profiles.\n\n```sql\n-- CUSTOMIZE AS NEEDED\n-- CREATE OR REPLACE DATABASE HT_PERFORMANCE;\n-- CREATE OR REPLACE SCHEMA HT_PERFORMANCE.PRIMER;\n\n-- CREATE OR REPLACE WAREHOUSE HT_PERFORMANCE_WAREHOUSE WITH WAREHOUSE_SIZE=XSMALL;\n    \n-- THE PRIMER WILL USE THIS SCHEMA FOR THE REMAINDER OF THE LEARNING\n-- CUSTOMIZE AS NEEDED\nUSE SCHEMA HT_PERFORMANCE.PRIMER;\nUSE WAREHOUSE HT_PERFORMANCE_WAREHOUSE;\n```\n\nNow, let's explore primary keys and secondary indexes\n\n## Explore the Primary Key\n\n\nPrimary keys are a required configuration for a hybrid table. The key establishes the rules for row uniqueness\nas well as the sort order for how data is written to the row storage. See [primary key documentation](https://docs.snowflake.com/en/sql-reference/sql/create-hybrid-table#required-parameters) for more details.\n\nSecondary Indexes provide fast access to query patterns that may not be able to use the primary key.\n\n### What We Will Cover\n- Creating a hybrid table with test data\n- Primary Key setup and analysis\n- Secondary index setup and analysis\n\n\n### Create the `TRUCKS` Hybrid Table\nCreate and populate a simple hybrid table. This table is similar to the table used in the getting started with hybrid tables guide but is \nsimplified for brevity. This quickstart will use similar queries for the remainder of the work:\n```sql\n--- CREATE LOTS OF TRUCKS WITH RANDOM YEARS, MAKES, FRANCHISE AND EMAILS.\nCREATE OR REPLACE HYBRID TABLE TRUCK (\n    TRUCK_ID NUMBER(38,0) NOT NULL,\n    YEAR NUMBER(38,0) NOT NULL,\n    MAKE VARCHAR(250) NOT NULL,\n    FRANCHISE_ID NUMBER(38,0) NOT NULL,\n    TRUCK_EMAIL VARCHAR NOT NULL,\n    PRIMARY KEY (TRUCK_ID) \n)\nAS SELECT\n    SEQ4(),\n    UNIFORM(1970, 2025, RANDOM()),\n    ARRAY_CONSTRUCT( 'Freightliner', 'Peterbilt', 'Kenworth', 'Volvo Trucks', 'Mack', 'International (Navistar)', 'Western Star')[UNIFORM(0,6, RANDOM())],\n    UNIFORM(1, 250, RANDOM()),\n    RANDSTR(8, RANDOM()) || '@sf-quickstart'\nFROM TABLE(GENERATOR(ROWCOUNT =\u003E 1000000)) -- WE NEED ENOUGH RECORDS FOR REASONABLE TESTING OTHERWISE EVERYTHING IS IN-MEMORY\n;\n-- SEE WHAT THE RESULTS LOOK LIKE\nSELECT * FROM TRUCK LIMIT 10;\n```\n\n### Query with the Primary Key\nPrimary keys are a highly performant way to query hybrid tables. Let's look at a query profile of what querying using the primary key looks like.\n```sql\n-- SELECT AND SET A RANDOM TRUCK_ID TO SESSION VARIABLE FOR CONVENIENCE\nSET TRUCK_ID = (SELECT TRUCK_ID FROM TRUCK SAMPLE(1 ROWS));\n\nSELECT *\nFROM TRUCK\nWHERE TRUCK_ID = $TRUCK_ID;\n```\nNow, click on the resulting query id (QID) on the Snowsight console. A new tab will open, and you will be presented with a screen that looks like this:\n![assets/explore_00.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hybrid-tables-performance-optimization-primer/explore_00.png?v=2d1d5955)\n\nThe screen gives you basic information about the query.\n\n\u003E \n\u003E **NOTE:** As this query was executed on Snowsight, compile and execution times will be longer than connected driver based queries.\n\u003E Review [hybrid table best practices](https://docs.snowflake.com/en/user-guide/tables-hybrid-best-practices) to learn more.\n\u003E\n\nClicking on the \"Query Profile\" button:\n![assets/explore_10.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hybrid-tables-performance-optimization-primer/explore_10.png?v=2d1d5955)\n\nwill lead you to a screen showing you the visual representation of how the query planner decided\nto execute the query:\n![assets/explore_20.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hybrid-tables-performance-optimization-primer/explore_20.png?v=2d1d5955)\n\nThis is the primary screen we will be using for query analysis.\n\n### Analyze the Query Profile Output\nNow that you understand how to get to the query profile screen, let's discover how this query was executed. Once we understand how the query planner decided\nto execute the query, we can understand how to implement any changes in the query that will improve performance.\n\nClicking on the bottom \"TableScan\" node will focus our attention on what happened in the data access.\n![assets/explore_30.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hybrid-tables-performance-optimization-primer/explore_30.png?v=2d1d5955)\nIn this example we can see that:\n- The Access Predicate was indeed the primary key `TRUCK.TRUCK_ID`\n- The Scan Mode was `ROW_BASED`\n- Exactly one record was retrieved from storage by the scan, exactly matching the truck ID.\n\n`ROW_BASED` scans means that the query directly accessed the row store and did not use the backing object store to produce the results. \nFor selective predicates against the primary key, `ROW_BASED` access indicates that the primary key index was used to \npush down the predicate and is the fastest and most efficient way to execute the query.\n\n\n### Query with `COLUMN_BASED` Access (no key)\nInstead of querying the table using the primary key, let's query the table using a column that is **not** indexed and see what\nthe resulting query plan looks like.\n```sql\nSET EMAIL = (SELECT ANY_VALUE(TRUCK_EMAIL) FROM TRUCK);\n    \nSELECT *\nFROM TRUCK\nWHERE TRUE\n    AND TRUCK_EMAIL = $EMAIL;\n```\nWe can see from the query profile screen that the query plan looks far different. Much time is spent on the table scan and it\nis the most expensive node in the plan:\n![assets/explore_40.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hybrid-tables-performance-optimization-primer/explore_40.png?v=2d1d5955)\n\nLooking at the \"TableScan\" node, we can clearly see that the scan was `COLUMN_BASED` and processed many partitions with lots of data. \nThe optimizer was not able to push down any access predicates against an index.\n\nNext, we will explore secondary indexes.\n\n## Explore Secondary Indexes\n\n\n### Add a Secondary Index\nClearly, the above query is not optimal. A secondary index can help the query run faster. Let's create a secondary index\nand use it to explore the query improvement:\n```sql\nCREATE OR REPLACE HYBRID TABLE TRUCK (\n    TRUCK_ID NUMBER(38,0) NOT NULL,\n    YEAR NUMBER(38,0) NOT NULL,\n    MAKE VARCHAR(250) NOT NULL,\n    FRANCHISE_ID NUMBER(38,0) NOT NULL,\n    TRUCK_EMAIL VARCHAR NOT NULL UNIQUE,\n    PRIMARY KEY (TRUCK_ID),\n    INDEX IDX_TRUCK_EMAIL (TRUCK_EMAIL)\n)\nAS SELECT\n    SEQ4(),\n    UNIFORM(1970, 2025, RANDOM()),\n    ARRAY_CONSTRUCT( 'Freightliner', 'Peterbilt', 'Kenworth', 'Volvo Trucks', 'Mack', 'International (Navistar)', 'Western Star')[UNIFORM(0,6, RANDOM())],\n    UNIFORM(1, 250, RANDOM()),\n    RANDSTR(8, RANDOM()) || '@sf-quickstart'\nFROM TABLE(GENERATOR(ROWCOUNT =\u003E 1000000)) -- WE NEED ENOUGH RECORDS FOR REASONABLE TESTING OTHERWISE EVERYTHING IS IN-MEMORY\n;\n-- SEE WHAT THE RESULTS LOOK LIKE\nSELECT * FROM TRUCK LIMIT 10;\n```\nOnce the table is created we can re-run the query and show that it is using the newly created index:\n```sql\nSET EMAIL = (SELECT ANY_VALUE(TRUCK_EMAIL) FROM TRUCK);\n\nSELECT *\nFROM TRUCK\nWHERE TRUE\n  AND TRUCK_EMAIL = $EMAIL;\n```\n\nUsing the index results in a much better query plan that uses the index we created:\n![assets/explore_61.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hybrid-tables-performance-optimization-primer/explore_61.png?v=2d1d5955)\n\nNext, we will cover foreign keys.\n\n## Explore Foreign Keys\n\n\nRDBMS data models use foreign keys to establish relationships between tables. All kinds of models take advantage of the relationships to keep\ndata from becoming corrupted or otherwise orphaned from a definition. We call the relationship between the primary key\nof the reference table and the usage of the key in another table a **foreign key**.\n\n### Setup\nWe will use a `TRUCK` and `ORDER_HEADER` relationship to create a foreign key relationship. Let's create the tables and\nexplore how the query plan looks with and without a foreign key.\n\nLet's reuse the `TRUCK` table and create orders for each truck.\n\n```sql\n--- CREATE ORDERS FOR EACH TRUCK\nCREATE OR REPLACE HYBRID TABLE ORDER_HEADER (\n\tORDER_ID NUMBER(38,0) NOT NULL,\n\tTRUCK_ID NUMBER(38,0) NOT NULL,\n    ORDER_TIMESTAMP TIMESTAMP_NTZ NOT NULL,\n\tORDER_TOTAL NUMBER(38,2),\n\tORDER_STATUS VARCHAR(200) DEFAULT 'INQUEUE',\n\tPRIMARY KEY (ORDER_ID)\n) AS\nSELECT\n    SEQ4(),\n    T.TRUCK_ID,\n    DATEADD('seconds', UNIFORM(-1*60*60*24*365, -1*60*60, RANDOM()), CURRENT_TIMESTAMP()), -- SPREAD RANDOM ORDER IDS OUT\n    UNIFORM(200.0, 25000.0, RANDOM()),\n    ARRAY_CONSTRUCT('DELIVERED', 'INQUEUE', 'LOADING', 'EN-ROUTE')[UNIFORM(0,3, RANDOM())],\nFROM TABLE(GENERATOR(ROWCOUNT =\u003E 5000))\nCROSS JOIN (SELECT TRUCK_ID FROM TRUCK SAMPLE (1000 ROWS)) T    -- ASSIGN ORDERS TO EVERY TRUCK = # TRUCKS X # ORDERS PER TRUCK\n;\n```\nConsider a query that loads all orders for a specific truck. This query will join the `TRUCK` table with the `ORDER_HEADER`\ntable while filtering for a particular truck.\n\n```sql\n-- SELECT A TRUCK THAT HAS ORDER DETAILS\nSET TRUCK_ID = (SELECT ANY_VALUE(TRUCK_ID) FROM ORDER_HEADER);\n\n-- QUERY FOR ALL ORDERS (5000) FOR THIS PARTICULAR TRUCK \nSELECT *\nFROM TRUCK T\nINNER JOIN ORDER_HEADER O ON O.TRUCK_ID=T.TRUCK_ID\nWHERE TRUE\n  AND T.TRUCK_ID = $TRUCK_ID\n;\n```\nNote that the query optimizer does not have information to relate the two tables within the join. Thus,\nthe optimizer chooses a column scan mode when reading data from the `ORDER_HEADER` table. Technically, the optimizer must\ninterpret the join as a many-to-many join.\n\n![assets/explore_70.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hybrid-tables-performance-optimization-primer/explore_70.png?v=2d1d5955)\n\nIntroducing a `FOREIGN KEY` will serve two important purposes. First, it establishes a relationship between the tables that\nguarantees a one-to-many join for a given `PRIMARY KEY` in the parent table. Second, the relationship will enforce a\nrule that requires data to be consistent such that child table `TRUCK_ID` values must exist in the `TRUCK` primary key.\nRecreate the table with a `FOREIGN KEY`:\n\n```sql\n-- CREATE ORDER_HEADER WITH A FOREIGN KEY\nCREATE OR REPLACE HYBRID TABLE ORDER_HEADER (\n    ORDER_ID NUMBER(38,0) NOT NULL,\n    TRUCK_ID NUMBER(38,0) NOT NULL,\n    ORDER_TIMESTAMP TIMESTAMP_NTZ NOT NULL,\n    ORDER_TOTAL NUMBER(38,2),\n    ORDER_STATUS VARCHAR(200) DEFAULT 'INQUEUE',\n    PRIMARY KEY (ORDER_ID),\n    CONSTRAINT FK_TRUCK_ID FOREIGN KEY (TRUCK_ID) REFERENCES TRUCK(TRUCK_ID)\n);\n-- USE INSERT INTO METHOD BECAUSE THE FOREIGN KEY MUST EXIST TO BE ENFORCED\nINSERT INTO ORDER_HEADER\nSELECT\n    SEQ4(),\n    T.TRUCK_ID,\n    DATEADD('seconds', UNIFORM(-1*60*60*24*365, -1*60*60, RANDOM()), CURRENT_TIMESTAMP()), -- SPREAD RANDOM ORDER IDS OUT\n    UNIFORM(200.0, 25000.0, RANDOM()),\n    ARRAY_CONSTRUCT('DELIVERED', 'INQUEUE', 'LOADING', 'EN-ROUTE')[UNIFORM(0,3, RANDOM())],\nFROM TABLE(GENERATOR(ROWCOUNT =\u003E 5000))\nCROSS JOIN (SELECT TRUCK_ID FROM TRUCK SAMPLE (1000 ROWS)) T    -- ASSIGN ORDERS TO EVERY TRUCK = # TRUCKS X # ORDERS PER TRUCK\n;\n```\n\nNext, find a new `TRUCK_ID` and execute the query to retrieve the order header information.\n\n```sql\nSET TRUCK_ID = (SELECT ANY_VALUE(TRUCK_ID) FROM ORDER_HEADER);\n\nSELECT *\nFROM TRUCK T\nINNER JOIN ORDER_HEADER O ON O.TRUCK_ID=T.TRUCK_ID\nWHERE TRUE\n    AND T.TRUCK_ID = $TRUCK_ID\n;\n```\n\nChecking the query plan, we can see that the query optimizer is now using the foreign key to efficiently select\nthe order detail information.\n\n![assets/explore_71.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hybrid-tables-performance-optimization-primer/explore_71.png?v=2d1d5955)\n\n\nNext, consider a query to retrieve a specific order and join in details for the truck for which the order was submitted. \nThis sort of pattern is common when dealing with normalized data models in transactional applications:\n\n```sql\n-- SELECT A SPECIFIC ORDER\nSET ORDER_ID = (SELECT ANY_VALUE(ORDER_ID) FROM ORDER_HEADER);\n\nSELECT *\nFROM ORDER_HEADER O\nINNER JOIN TRUCK T ON O.TRUCK_ID=T.TRUCK_ID\nWHERE TRUE\n  AND O.ORDER_ID = $ORDER_ID\n;\n```\n\nThe query plan shows both primary keys in use loading only a single record\nfrom each table by using an Nested Loop Join pattern where we first retrieve the order record \nand then use the truck ID for that order to push down the truck lookup against the primary key of the truck table:\n\n![assets/explore_72.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hybrid-tables-performance-optimization-primer/explore_72.png?v=2d1d5955)\n\nNext, we will explore how secondary indexes help queries that do not use primary keys.\n\n## Joining and Secondary Indexes\n\n\nA common query pattern is to join two tables using foreign keys and filter one of them with another column. For example,\nthis query is fetching orders over the last few days for a specific truck:\n\n```sql\nSELECT *\nFROM ORDER_HEADER O\nINNER JOIN TRUCK T ON T.TRUCK_ID=O.TRUCK_ID\nWHERE TRUE\n    AND O.ORDER_TIMESTAMP \u003E DATEADD('days', -2, CURRENT_TIMESTAMP()) :: TIMESTAMP_NTZ  -- NOTE THE CAST\n    AND T.TRUCK_ID = $TRUCK_ID\n;\n```\nThe `TRUCK_ID` will be used as a primary key filter on `TRUCK` and a foreign key filter on `ORDER_HEADER`. The timestamp filter\nwill be applied after data was fetched from hybrid table object store. From the query profile, we can see that only the\n`TRUCK_ID` was used as an access predicate. The `ORDER_TIMESTAMP` filtering was completed after data was fetched.\n\n![assets/explore_80.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hybrid-tables-performance-optimization-primer/explore_80.png?v=2d1d5955)\n\nA secondary index on (TRUCK_ID, ORDER_TIMESTAMP) will improve performance by allowing us to also push the timestamp \nfilter down to storage:\n\n```sql\n--- CREATE ORDERS FOR EACH TRUCK\nCREATE OR REPLACE HYBRID TABLE ORDER_HEADER (\n    ORDER_ID NUMBER(38,0) NOT NULL,\n    TRUCK_ID NUMBER(38,0) NOT NULL,\n    ORDER_TIMESTAMP TIMESTAMP_NTZ NOT NULL,\n    ORDER_TOTAL NUMBER(38,2),\n    ORDER_STATUS VARCHAR(200) DEFAULT 'INQUEUE',  -- USE DEFAULT VALUE\n    PRIMARY KEY (ORDER_ID),\n    FOREIGN KEY (TRUCK_ID) REFERENCES TRUCK(TRUCK_ID),  -- REFER TO THE TRUCK TABLE FOR TRUCK_ID\n    INDEX IDX_TRUCK_TIMESTAMPS (TRUCK_ID, ORDER_TIMESTAMP )  -- ADD THE SECONDARY INDEX FOR ORDER_TIMESTAMP\n);\nINSERT INTO ORDER_HEADER (ORDER_ID, TRUCK_ID, ORDER_TIMESTAMP, ORDER_TOTAL)\nSELECT\n    SEQ4(),\n    T.TRUCK_ID,\n    DATEADD('seconds', UNIFORM(-1*60*60*24*365, -1*60*60, RANDOM()), CURRENT_TIMESTAMP()), -- SPREAD RANDOM ORDER IDS OUT\n    UNIFORM(200.0, 25000.0, RANDOM()),\nFROM TABLE(GENERATOR(ROWCOUNT =\u003E 5000))\nCROSS JOIN (SELECT TRUCK_ID FROM TRUCK SAMPLE (1000 ROWS)) T    -- ASSIGN ORDERS TO EVERY TRUCK = # TRUCKS X # ORDERS PER TRUCK\n;\n```\n\nNow that the index exists and is `ACTIVE`, we can use it in a query:\n\n```sql\nSET TRUCKID = (SELECT ANY_VALUE(TRUCK_ID) FROM ORDER_HEADER);\n\nSELECT *\nFROM ORDER_HEADER O\nINNER JOIN TRUCK T ON T.TRUCK_ID=O.TRUCK_ID\nWHERE TRUE\n    AND O.ORDER_TIMESTAMP \u003E DATEADD('days', -1, CURRENT_TIMESTAMP()) :: TIMESTAMP_NTZ\n    AND T.TRUCK_ID = $TRUCKID\n;\n```\nNow the truck and timestamp predicates are being pushed down against the index in order to reduce the amount of data \nretrieved from the order_header table improving latency and efficiency.\n\n![assets/explore_83.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/hybrid-tables-performance-optimization-primer/explore_83.png?v=2d1d5955)\n\n\u003E \n\u003E **NOTE:** Looking at the query in detail, you will notice that there is a cast to `TIMESTAMP_NTZ` for the `ORDER_TIMESTAMP`\n\u003E predicate value. All types of timestamps are supported by hybrid tables, however, columns used in primary keys\n\u003E or indexes are only supported as `TIMESTAMP_NTZ`. As an exercise, try the query without the cast and inspect\n\u003E the result.\n\u003E \n\nNext, we will summarize best practices.\n\n## Summarize Best Practices\n\n\nHybrid table performance benefits from following best practices that extract the best performance and highest value\nfrom this Snowflake feature.\n1. Use a `PRIMARY KEY`, built from business columns, to drive the highest performance.\n1. Implement `FOREIGN KEYS` when joining hybrid tables.\n1. Write queries that use keys and indexes to keep queries in `ROW_BASED` mode.\n1. Use `INCLUDE` columns on secondary indexes to create covering indexes that eliminate probe scans for hot query paths.\n1. Use **bound variables** (parameterized queries) in application code so Snowflake can cache and reuse compiled query plans.\n1. Use Snowsight query plans to verify optimal query plans and troubleshoot performance issues.\n1. Pay close attention to data types, join criteria, and index utilization.\n1. Follow Snowflake [hybrid table best practices](https://docs.snowflake.com/en/user-guide/tables-hybrid-best-practices).\n\n\n## Conclusion And Resources\n\n\n### Overview\nThank you for completing this Hybrid Tables Performance Optimization Primer. You now have a solid understanding of how to \nrelate query performance to the query planning tool. You will use this knowledge to help create and optimize your hybrid table solutions.\n\n\n### What You Learned\n\nHaving completed this quickstart you have successfully\n- Created hybrid tables with primary keys\n- Wrote and learned how to analyze a query profile\n- Learned how to use secondary indexes and constraints\n- Created a child table using foreign keys referencing a parent table\n- Joined tables and worked with secondary indexes to maximize performance \n- Utilized secondary indexes to speed query performance across non-primary key columns\n\n\n### Related Resources:\n- [Snowflake Unistore Landing Page](/en/data-cloud/workloads/unistore/)\n- [Snowflake Documentation for Hybrid Tables](https://docs.snowflake.com/en/user-guide/tables-hybrid)\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- [Hybrid Table Performance Testing Quickstart](/en/developers/guides/hybrid-tables-jmeter-performance-testing/)\n- [Secondary Index Design for Hybrid Tables](/en/developers/guides/hybrid-tables-secondary-index-design/)\n- [Converting Standard Tables to Hybrid Tables](/en/developers/guides/hybrid-tables-standard-to-hybrid-migration/)\n- [Simplify Application Development Hybrid Tables Blog](/blog/simplify-application-development-hybrid-tables)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-5c740f700b","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-c5fd4a1ce7",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-67ebe2dbed","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-06-16",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-e040c15c58","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. It may be out of date with current Snowflake instances\u003C/span\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"}},":itemsOrder":["quickstart_last_modi","text"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-9749d7eca8",":items":{},":itemsOrder":[],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"}},":itemsOrder":["contentfragment","flexible_column_cont"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-6593ee794d",":items":{"quickstart_table_of_":{"layout":"SIMPLE","id":"container-c162569522","isDeveloperGuidesPage":false,":items":{"quickstart_table_of_":{"id":"quickstart-table-of-content-637bbe813d","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/hybrid-tables-performance-optimization-primer",":type":"snowflake-site/components/quickstart/quickstart-table-of-content","headings":["\u003Ch2\u003EOverview\u003C/h2\u003E","\u003Ch2\u003ESetup\u003C/h2\u003E","\u003Ch2\u003EExplore 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