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this tutorial to learn the basics of PostgreSQL for transactional workloads","title":"Getting Started with Snowflake Postgres Tables","paragraphs":["<!-- ------------------------ -->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Ch3\u003EPostgres for Transactional Workloads\u003C/h3\u003E\n","\u003Cp\u003E\u003Ca href=\"https://www.postgresql.org/\"\u003EPostgreSQL\u003C/a\u003E is a powerful, open-source object-relational database system that is optimized for transactional workloads requiring low latency and high throughput on small random point reads/writes. Pronounced \u003Ccode\u003EPost-gres-Q-L\u003C/code\u003E or just \u003Ccode\u003EPostgres\u003C/code\u003E for short.  Postgres supports robust referential integrity constraint enforcement, ACID compliance, and advanced indexing that is critical for transactional workloads. You can use Postgres tables with various features like partitioning, foreign data wrappers, logical replication, and a vast set of extensions to power transactional workloads with various needs.\u003C/p\u003E\n","\u003Cp\u003EUse cases that benefit from Postgres include:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EApplication requires Postgres compatibility\u003C/li\u003E\u003Cli\u003ELow-latency data servicing and ingestion\u003C/li\u003E\u003Cli\u003EImplement full ACID-compliant transactional systems\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EArchitecture\u003C/h3\u003E\n","\u003Cp\u003EPostgres uses a proven architecture with over 35 years of active development. The system consists of:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EClient connections\u003C/strong\u003E via standard Postgres protocol (libpq)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EQuery parser and planner\u003C/strong\u003E that optimizes SQL queries for best performance\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EStorage engine\u003C/strong\u003E with MVCC (Multi-Version Concurrency Control) for concurrent access\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EWAL (Write-Ahead Logging)\u003C/strong\u003E for durability and crash recovery\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EBackground processes\u003C/strong\u003E for vacuuming, checkpointing, and statistics\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EKey benefits of this architecture:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EACID compliance ensures data consistency and reliability\u003C/li\u003E\u003Cli\u003ESupport for concurrent reads and writes without blocking\u003C/li\u003E\u003Cli\u003EAdvanced indexing (B-tree, Hash, GiST, GIN, BRIN) for optimal query performance\u003C/li\u003E\u003Cli\u003ENative support for JSON, full-text search, and geospatial data\u003C/li\u003E\u003Cli\u003EExtensible with custom functions, data types, and extensions\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EPostgres stores data in a row-oriented format by default, which provides excellent performance for transactional workloads.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Will Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EThe basics of Postgres tables and constraints\u003C/li\u003E\u003Cli\u003EHow to create and use Postgres tables with indexes\u003C/li\u003E\u003Cli\u003EThe advantages of proper constraint and index usage\u003C/li\u003E\u003Cli\u003EPostgres characteristics like indexes, primary keys, unique and foreign keys\u003C/li\u003E\u003Cli\u003ERow-level locking and transaction isolation\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EFamiliarity with SQL\u003C/li\u003E\u003Cli\u003ESnowflake Postgres cluster\u003C/li\u003E\u003Cli\u003EPostgres client (psql or your preferred client)\u003C/li\u003E\u003Cli\u003EDatabase superuser or sufficient privileges to create databases and roles\u003C/li\u003E\u003Cli\u003EBasic understanding of relational database concepts\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ENote:  Several of the commands used in this guide are specific to \u003Ccode\u003Epsql\u003C/code\u003E.  If another tool is used, some commands may need to be skipped.\u003C/p\u003E\n","\u003Ch2\u003ESetup\u003C/h2\u003E\n","\u003Cp\u003EIn this section, we will set up our Postgres environment, create a database, role, schema, and tables that we will use throughout this tutorial.  If you need to install \u003Ccode\u003Epsql\u003C/code\u003E on MacOS, use \u003Ccode\u003Ebrew install postgresql@18\u003C/code\u003E.\u003C/p\u003E\n","\u003Ch3\u003EStep 1 Create Snowflake Postgres Cluster\u003C/h3\u003E\n","\u003Cp\u003EFollow the \u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowflake-postgres-getting-started-with-tables/assets/en/developers/guides/getting-started-with-snowflake-postgres/?v=32b758de\" alt=\"Getting Started with Snowflake Postgres\"\u003E to create a Postgres instance and configure network policies and rules.\u003C/p\u003E\n","\u003Ch3\u003EStep 2 Connect to Postgres\u003C/h3\u003E\n","\u003Cp\u003ETo connect to Snowflake Postgres, you will need to get the login credentials for the \u003Ccode\u003Esnowflake_admin\u003C/code\u003E account.  These credentials are generated during the creation of the Postgres cluster or can be regenerated using \u003Ccode\u003EALTER POSTGRES INSTANCE RESET ACCESS FOR 'snowflake_admin';\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003ESave the credentials as environment variables to simplify the connection process.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Eexport PGHOST="<Snowflake host name>"\nexport PGDATABASE="postgres"\nexport PGUSER="snowflake_admin"\nexport PGPASSWORD="<password>"\nexport PGSSLMODE=require\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EConnect to your Postgres instance using psql or your preferred client:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E# Connect to Postgres as an admin user\npsql -U snowflake_admin\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 3 Setup\u003C/h3\u003E\n","\u003Ch4\u003ECreate Database Objects\u003C/h4\u003E\n","\u003Cp\u003ECreate a role, database, and schema for our quickstart:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Create role quickstart_user\nCREATE ROLE quickstart_user WITH LOGIN PASSWORD 'secure_password_here';\n\n-- Grant database creation privilege\nALTER ROLE quickstart_user CREATEDB;\n\n-- Create database\nCREATE DATABASE quickstart_db;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWith the newly created \u003Ccode\u003Equickstart_db\u003C/code\u003E, change the active (connected) database to be \u003Ccode\u003Equickstart_db\u003C/code\u003E and create the \u003Ccode\u003Equickstart\u003C/code\u003E schema.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Connect to the new database\n\\c quickstart_db\n\n-- Create schema\nCREATE SCHEMA IF NOT EXISTS quickstart;\n\n-- Grant necessary privileges\nGRANT ALL PRIVILEGES ON SCHEMA quickstart TO quickstart_user;\nGRANT ALL PRIVILEGES ON DATABASE quickstart_db TO quickstart_user;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003ECreate Tables and Load Data\u003C/h4\u003E\n","\u003Cp\u003EIn this quickstart, we will use Tasty Bytes fictional food truck business data to simulate a typical transactional use case.\u003C/p\u003E\n","\u003Cp\u003EWe will create three tables:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EORDER_HEADER\u003C/strong\u003E - Stores order metadata such as TRUCK_ID, CUSTOMER_ID, ORDER_AMOUNT, etc.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETRUCK\u003C/strong\u003E - Stores truck metadata such as TRUCK_ID, FRANCHISE_ID, MENU_TYPE_ID, etc.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETRUCK_HISTORY\u003C/strong\u003E - Stores historical TRUCK information, enabling you to track changes over time\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EFirst, let's create the TRUCK table with appropriate constraints:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Create TRUCK table\nCREATE TABLE quickstart.truck (\n    truck_id INTEGER NOT NULL,\n    menu_type_id INTEGER,\n    primary_city VARCHAR(255),\n    region VARCHAR(255),\n    iso_region VARCHAR(255),\n    country VARCHAR(255),\n    iso_country_code VARCHAR(10),\n    franchise_flag INTEGER,\n    year INTEGER,\n    make VARCHAR(255),\n    model VARCHAR(255),\n    ev_flag INTEGER,\n    franchise_id INTEGER,\n    truck_opening_date DATE,\n    truck_email VARCHAR(255) NOT NULL UNIQUE,\n    record_start_time TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,\n    PRIMARY KEY (truck_id)\n);\n\n-- Create index on commonly queried columns\nCREATE INDEX idx_truck_franchise ON quickstart.truck(franchise_id);\nCREATE INDEX idx_truck_city ON quickstart.truck(primary_city);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECreate the TRUCK_HISTORY table for tracking historical changes:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Create TRUCK_HISTORY table\nCREATE TABLE quickstart.truck_history (\n    truck_id INTEGER NOT NULL,\n    menu_type_id INTEGER,\n    primary_city VARCHAR(255),\n    region VARCHAR(255),\n    iso_region VARCHAR(255),\n    country VARCHAR(255),\n    iso_country_code VARCHAR(10),\n    franchise_flag INTEGER,\n    year INTEGER,\n    make VARCHAR(255),\n    model VARCHAR(255),\n    ev_flag INTEGER,\n    franchise_id INTEGER,\n    truck_opening_date DATE,\n    truck_email VARCHAR(255) NOT NULL,\n    record_start_time TIMESTAMP WITH TIME ZONE,\n    record_end_time TIMESTAMP WITH TIME ZONE,\n    PRIMARY KEY (truck_id, record_start_time)\n);\n\n-- Create partial index for querying current records\nCREATE INDEX idx_truck_history_current ON quickstart.truck_history(truck_id) \nWHERE record_end_time IS NULL;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECreate the ORDER_HEADER table with foreign key relationships:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Create ORDER_HEADER table with constraints\nCREATE TABLE quickstart.order_header (\n    order_id BIGINT NOT NULL,\n    truck_id INTEGER,\n    location_id BIGINT,\n    customer_id INTEGER,\n    discount_id FLOAT,\n    shift_id INTEGER,\n    shift_start_time TIME,\n    shift_end_time TIME,\n    order_channel VARCHAR(255),\n    order_ts TIMESTAMP,\n    served_ts VARCHAR(255),\n    order_currency VARCHAR(3),\n    order_amount NUMERIC(10,4),\n    order_tax_amount VARCHAR(255),\n    order_discount_amount VARCHAR(255),\n    order_total NUMERIC(10,4),\n    order_status VARCHAR(50) DEFAULT 'INQUEUE',\n    PRIMARY KEY (order_id),\n    FOREIGN KEY (truck_id) REFERENCES quickstart.truck(truck_id)\n);\n\n-- Create indexes for performance\nCREATE INDEX idx_order_header_truck ON quickstart.order_header(truck_id);\nCREATE INDEX idx_order_header_ts ON quickstart.order_header(order_ts);\nCREATE INDEX idx_order_header_status ON quickstart.order_header(order_status);\nCREATE INDEX idx_order_header_customer ON quickstart.order_header(customer_id);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003ELoad Sample Data\u003C/h4\u003E\n","\u003Cp\u003ENow let's load sample data into our tables. We'll generate sample data using Postgres's \u003Ccode\u003Egenerate_series()\u003C/code\u003E function. You can adjust the row counts to control the volume of test data.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 1: Clear existing data\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EFirst, clear any existing data from the tables. We truncate \u003Ccode\u003Etruck_history\u003C/code\u003E first, then \u003Ccode\u003Etruck\u003C/code\u003E with CASCADE to handle the foreign key relationship with \u003Ccode\u003Eorder_header\u003C/code\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ETRUNCATE TABLE quickstart.truck_history;\nTRUNCATE TABLE quickstart.truck CASCADE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 2: Load the truck table (1,000 rows)\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EGenerate truck records with varied cities, makes, models, and other attributes:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EINSERT INTO quickstart.truck (\n    truck_id, menu_type_id, primary_city, region, iso_region, country, \n    iso_country_code, franchise_flag, year, make, model, ev_flag, franchise_id, \n    truck_opening_date, truck_email, record_start_time\n)\nSELECT \n    gs.id AS truck_id,\n    (gs.id % 5) + 1 AS menu_type_id,\n    (ARRAY['New York', 'Los Angeles', 'Chicago', 'Houston', 'Phoenix', \n           'Philadelphia', 'San Antonio', 'San Diego', 'Dallas', 'San Jose',\n           'Austin', 'Jacksonville', 'Fort Worth', 'Columbus', 'Charlotte',\n           'San Francisco', 'Indianapolis', 'Seattle', 'Denver', 'Boston'])[(gs.id % 20) + 1] AS primary_city,\n    (ARRAY['Northeast', 'Southeast', 'Midwest', 'Southwest', 'West'])[(gs.id % 5) + 1] AS region,\n    (ARRAY['NY', 'CA', 'IL', 'TX', 'AZ', 'PA', 'FL', 'OH', 'NC', 'WA'])[(gs.id % 10) + 1] AS iso_region,\n    'United States' AS country,\n    'US' AS iso_country_code,\n    (gs.id % 2)::INTEGER AS franchise_flag,\n    2015 + (gs.id % 10) AS year,\n    (ARRAY['Ford', 'Mercedes', 'Freightliner', 'Chevrolet', 'GMC', 'RAM'])[(gs.id % 6) + 1] AS make,\n    (ARRAY['Transit', 'Sprinter', 'MT45', 'Express', 'Savana', 'ProMaster'])[(gs.id % 6) + 1] AS model,\n    (gs.id % 4 = 0)::INTEGER AS ev_flag,\n    100 + (gs.id % 500) AS franchise_id,\n    CURRENT_DATE - ((gs.id % 1825) || ' days')::INTERVAL AS truck_opening_date,\n    gs.id || '_truck@email.com' AS truck_email,\n    CURRENT_TIMESTAMP - ((gs.id % 365) || ' days')::INTERVAL AS record_start_time\nFROM generate_series(1, 1000) AS gs(id);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 3: Load the truck_history table\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003ECopy all truck records into the history table. This simulates having a historical record for each truck:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EINSERT INTO quickstart.truck_history \nSELECT *, NULL AS record_end_time \nFROM quickstart.truck;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 4: Load the order_header table (1,000,000 rows)\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EGenerate order records that reference existing trucks. This query uses a weighted distribution where 80% of orders go to the top 20% of trucks (simulating popular food trucks):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EINSERT INTO quickstart.order_header (\n    order_id, truck_id, location_id, customer_id, discount_id, \n    shift_id, shift_start_time, shift_end_time, order_channel, order_ts, \n    served_ts, order_currency, order_amount, order_tax_amount, \n    order_discount_amount, order_total, order_status\n)\nSELECT \n    1000 + gs.id AS order_id,\n    -- Weighted truck selection: 80% of orders go to top 20% of trucks\n    CASE \n        WHEN (gs.id * 17) % 100 < 80 THEN ((gs.id * 31) % 200) + 1  -- Top 200 trucks\n        ELSE ((gs.id * 37) % 1000) + 1                              -- All trucks\n    END AS truck_id,\n    5000 + (gs.id % 1000) AS location_id,\n    2000 + (gs.id % 5000) AS customer_id,\n    CASE WHEN (gs.id * 7) % 100 < 10 THEN ((gs.id * 11) % 20)::FLOAT ELSE 0 END AS discount_id,\n    (gs.id % 3) + 1 AS shift_id,\n    CASE (gs.id % 3)\n        WHEN 0 THEN '08:00:00'::TIME\n        WHEN 1 THEN '16:00:00'::TIME\n        ELSE '00:00:00'::TIME\n    END AS shift_start_time,\n    CASE (gs.id % 3)\n        WHEN 0 THEN '16:00:00'::TIME\n        WHEN 1 THEN '23:00:00'::TIME\n        ELSE '08:00:00'::TIME\n    END AS shift_end_time,\n    (ARRAY['mobile', 'web', 'phone', 'kiosk'])[(gs.id % 4) + 1] AS order_channel,\n    CURRENT_TIMESTAMP - ((1000000 - gs.id) % 180 || ' days')::INTERVAL \n        + ((gs.id % 86400) || ' seconds')::INTERVAL AS order_ts,\n    CASE \n        WHEN (gs.id * 13) % 100 < 80 THEN \n            (CURRENT_TIMESTAMP - ((1000000 - gs.id) % 180 || ' days')::INTERVAL \n                + ((gs.id % 86400 + 300) || ' seconds')::INTERVAL)::TEXT\n        ELSE ''\n    END AS served_ts,\n    'USD' AS order_currency,\n    (10 + ((gs.id * 19) % 90))::NUMERIC(10,4) AS order_amount,\n    ((10 + ((gs.id * 19) % 90)) * 0.08)::NUMERIC(10,4)::TEXT AS order_tax_amount,\n    CASE \n        WHEN (gs.id * 23) % 100 < 10 THEN (((gs.id * 29) % 20) * 0.1)::NUMERIC(10,4)::TEXT\n        ELSE '0'\n    END AS order_discount_amount,\n    ((10 + ((gs.id * 19) % 90)) * 1.08)::NUMERIC(10,4) AS order_total,\n    (ARRAY['COMPLETED', 'COMPLETED', 'COMPLETED', 'INQUEUE', 'PROCESSING'])[(gs.id % 5) + 1] AS order_status\nFROM generate_series(1, 1000000) AS gs(id);\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote\u003C/strong\u003E: This insert may take 30-60 seconds depending on your environment.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 5: Verify data relationships\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EConfirm the foreign key relationships are intact by checking that all orders reference valid trucks:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT \n    (SELECT COUNT(*) FROM quickstart.truck) AS trucks,\n    (SELECT COUNT(*) FROM quickstart.truck_history) AS truck_history,\n    (SELECT COUNT(*) FROM quickstart.order_header) AS orders,\n    (SELECT COUNT(DISTINCT truck_id) FROM quickstart.order_header) AS trucks_with_orders;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAlthough autovacuum will run and perform the necessary background statistics gathering, etc. the below commands will manually perform a vacuum and analyze on the newly populated tables.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EVACUUM ANALYZE quickstart.truck;\nVACUUM ANALYZE quickstart.truck_history;\nVACUUM ANALYZE quickstart.order_header;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EExplore Data\u003C/h2\u003E\n","\u003Cp\u003EIn the previous Setup step, we created our database, schema, and tables. Let's explore them now.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Set context\n\\c quickstart_db\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe created and loaded data into the TRUCK and ORDER_HEADER tables. Now we can run queries to get familiar with them.\u003C/p\u003E\n","\u003Cp\u003EView table information:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- List all tables in the data schema and their size\n\\dt+ quickstart.*\n\n-- View detailed table information\n\\d+ quickstart.truck\n\\d+ quickstart.order_header\n\\d+ quickstart.truck_history\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EDisplay information about constraints and indexes:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- View all constraints on TRUCK table\nSELECT con.conname AS constraint_name, con.contype AS constraint_type,\n    CASE con.contype\n        WHEN 'p' THEN 'PRIMARY KEY'\n        WHEN 'u' THEN 'UNIQUE'\n        WHEN 'f' THEN 'FOREIGN KEY'\n        WHEN 'c' THEN 'CHECK'\n        ELSE 'OTHER'\n    END AS constraint_description\nFROM pg_constraint con\n     JOIN pg_class rel ON rel.oid = con.conrelid\n     JOIN pg_namespace nsp ON nsp.oid = rel.relnamespace\nWHERE nsp.nspname = 'quickstart' AND rel.relname = 'truck';\n\n-- View all indexes\nSELECT tablename, indexname, indexdef\nFROM pg_indexes\nWHERE schemaname = 'quickstart'\nORDER BY tablename, indexname;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ELook at sample data from the tables:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Query 10 rows from TRUCK table\nSELECT * FROM quickstart.truck LIMIT 10;\n\n-- Query 10 rows from TRUCK_HISTORY table\nSELECT * FROM quickstart.truck_history LIMIT 10;\n\n-- Query 10 rows from ORDER_HEADER table\nSELECT * FROM quickstart.order_header LIMIT 10;\n\n-- View table row counts\nSELECT 'truck' as table_name, COUNT(*) as row_count FROM quickstart.truck\nUNION ALL\nSELECT 'order_header', COUNT(*) FROM quickstart.order_header\nUNION ALL\nSELECT 'truck_history', COUNT(*) FROM quickstart.truck_history;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EUnique and Foreign Key Constraints\u003C/h2\u003E\n","\u003Cp\u003EIn this section, we will test Unique and Foreign Key constraints.\u003C/p\u003E\n","\u003Ch3\u003EUnique Constraints\u003C/h3\u003E\n","\u003Cp\u003EPostgres's unique constraint ensures that all values in a column are different.\u003C/p\u003E\n","\u003Cp\u003EIn the TRUCK table, we defined the TRUCK_EMAIL column as NOT NULL and UNIQUE.  A key difference between the uniqueness enforced by a PRIMARY KEY constraint and a UNIQUE constraint is the PRIMARY KEY does not allow NULL values where the UNIQUE constraint does.  In the case of the TRUCK_EMAIL we specified NOT NULL to require values for this field.\u003C/p\u003E\n","\u003Cp\u003EDisplay constraint information:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Set context\n\\c quickstart_db\n\n-- View constraints on TRUCK table\n\\d quickstart.truck\n\n-- Or get detailed constraint information\nSELECT \n    con.conname AS constraint_name,\n    pg_get_constraintdef(con.oid) AS constraint_definition\nFROM pg_constraint con\nJOIN pg_class rel ON rel.oid = con.conrelid\nWHERE rel.relname = 'truck' AND con.contype IN ('u', 'p');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EDue to the unique constraint, attempting to insert two records with the same email address will fail:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Attempt insert with duplicate email (will fail)\nINSERT INTO quickstart.truck VALUES (\n    (SELECT MAX(truck_id) + 1 FROM quickstart.truck),\n    2, 'Stockholm', 'Stockholm län', 'Stockholm', 'Sweden', 'SE', \n    1, 2001, 'Freightliner', 'MT45 Utilimaster', 0, 276, '2020-10-01',\n    (SELECT truck_email FROM quickstart.truck LIMIT 1),\n    CURRENT_TIMESTAMP\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should receive an error message similar to:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EERROR: duplicate key value violates unique constraint "truck_truck_email_key"\nDETAIL: Key (truck_email)=(1_truck@email.com) already exists.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow let's insert a new record with a unique email address:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Create a new unique email and insert successfully using CTE\nWITH new_truck AS (\n    SELECT MAX(truck_id) + 1 as new_truck_id,\n           (MAX(truck_id) + 1)::TEXT || '_truck@email.com' as new_email\n    FROM quickstart.truck\n)\nINSERT INTO quickstart.truck (\n    truck_id, menu_type_id, primary_city, region, iso_region, country,\n    iso_country_code, franchise_flag, year, make, model, ev_flag, \n    franchise_id, truck_opening_date, truck_email, record_start_time\n)\nSELECT new_truck_id, 2, 'Stockholm', 'Stockholm län', 'Stockholm', 'Sweden',\n    'SE', 1, 2001, 'Freightliner', 'MT45 Utilimaster', 0, \n    276, '2020-10-01', new_email, CURRENT_TIMESTAMP\nFROM new_truck\nRETURNING truck_id, truck_email;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EStatement should run successfully.\u003C/p\u003E\n","\u003Ch3\u003EForeign Key Constraints\u003C/h3\u003E\n","\u003Cp\u003EForeign key constraints ensure referential integrity between tables.\u003C/p\u003E\n","\u003Cp\u003EFirst, let's examine the foreign key constraints on the ORDER_HEADER table:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- View foreign key constraints\nSELECT con.conname AS constraint_name, pg_get_constraintdef(con.oid) AS constraint_definition\nFROM pg_constraint con\nJOIN pg_class rel ON rel.oid = con.conrelid\nWHERE rel.relname = 'order_header' AND con.contype = 'f';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow let's try to insert a record with a non-existent truck_id (this will fail):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Attempt to insert order with non-existent truck\nINSERT INTO quickstart.order_header VALUES (\n    (SELECT MAX(order_id) + 1 FROM quickstart.order_header),\n    -1,  -- Non-existent truck_id\n    6090, 0, 0, 0, '16:00:00', '23:00:00', 'web', \n    '2024-02-18 21:38:46', '', 'USD', 17.0000, '', '', 17.0000, 'PENDING'\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe statement should fail with an error:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EERROR: insert or update on table "order_header" violates foreign key constraint "order_header_truck_id_fkey"\nDETAIL: Key (truck_id)=(-1) is not present in table "truck".\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow let's insert with a valid truck_id:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Insert with valid truck_id\nINSERT INTO quickstart.order_header VALUES (\n    (SELECT MAX(order_id) + 1 FROM quickstart.order_header),\n    (SELECT truck_id FROM quickstart.truck LIMIT 1),\n    6090, 0, 0, 0, '16:00:00', '23:00:00', 'web', \n    '2024-02-18 21:38:46', '', 'USD', 17.0000, '', '', 17.0000, 'PENDING'\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EStatement should run successfully.\u003C/p\u003E\n","\u003Ch3\u003EForeign Key and TRUNCATE\u003C/h3\u003E\n","\u003Cp\u003ETables referenced by foreign key constraints have special behavior with TRUNCATE:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Attempt to truncate the parent table (this will fail)\nTRUNCATE TABLE quickstart.truck;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe statement should fail with an error:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EERROR: cannot truncate a table referenced in a foreign key constraint\nDETAIL: Table "order_header" references "truck".\nHINT: Truncate table "order_header" at the same time, or use TRUNCATE ... CASCADE.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EForeign Key and DELETE\u003C/h3\u003E\n","\u003Cp\u003ERecords referenced by foreign keys cannot be deleted unless the referencing records are removed first:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Try to delete a truck that has orders (will fail)\nDELETE FROM quickstart.truck WHERE truck_id = 1;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe statement should fail with:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EERROR: update or delete on table "truck" violates foreign key constraint "order_header_truck_id_fkey" on table "order_header"\nDETAIL: Key (truck_id)=(1) is still referenced from table "order_header".\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETo successfully delete a truck, first delete its orders:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Delete order header\nDELETE FROM quickstart.order_header\nWHERE truck_id = 1;\n\n-- Delete truck\nDELETE FROM quickstart.truck\nWHERE truck_id = 1;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EBoth operations complete successfully.  As an alternative, the foreign key constraint could have been created with the CASCADE clause that would automatically delete all child records when the parent was deleted.\u003C/p\u003E\n","\u003Ch2\u003ERow Level Locking\u003C/h2\u003E\n","\u003Cp\u003EPostgres uses Multi-Version Concurrency Control (MVCC) with row-level locking for update operations. This allows for concurrent updates on independent records without blocking reads.\u003C/p\u003E\n","\u003Cp\u003EIn this step, we will test concurrent updates to different records.\u003C/p\u003E\n","\u003Ch3\u003ESetup for Testing\u003C/h3\u003E\n","\u003Cp\u003EOpen two \u003Ccode\u003Epsql\u003C/code\u003E sessions or two connections using the client tool of your choice. We'll call them Session 1 and Session 2.  Remember to set the environment variables with the connection information in each session if using \u003Ccode\u003Epsql\u003C/code\u003E.  Optionally, open a third terminal/session to monitor locks.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESession 1:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epsql -U snowflake_admin -d quickstart_db\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESession 2:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epsql -U snowflake_admin -d quickstart_db\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ERunning Concurrent Updates\u003C/h3\u003E\n","\u003Cp\u003EIn \u003Cstrong\u003ESession 1\u003C/strong\u003E, start a transaction and update a record:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Session 1\n\n-- Begin transaction\nBEGIN;\n\n-- Update the record with highest order_id\nUPDATE quickstart.order_header\nSET order_status = 'COMPLETED'\nWHERE order_id = (SELECT MAX(order_id) FROM quickstart.order_header);\n\n-- DO NOT COMMIT YET!\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENote that we haven't committed the transaction. There's now an open lock on this record.\u003C/p\u003E\n","\u003Cp\u003EIn \u003Cstrong\u003ESession 2\u003C/strong\u003E, update a different record:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Session 2\n\n-- Check active transactions\nSELECT pid, usename, state, query_start, query \nFROM pg_stat_activity \nWHERE datname = 'quickstart_db' AND state = 'idle in transaction';\n\n-- Update a different record (this will succeed immediately)\nUPDATE quickstart.order_header\nSET order_status = 'COMPLETED'\nWHERE order_id = (SELECT MIN(order_id) FROM quickstart.order_header);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESince Postgres uses row-level locking, the second update succeeds immediately without waiting. The locked row in Session 1 doesn't block updates to other rows.\u003C/p\u003E\n","\u003Cp\u003EBack in \u003Cstrong\u003ESession 1\u003C/strong\u003E, commit the transaction:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Session 1\n-- Commit the transaction\nCOMMIT;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ETesting Lock Contention\u003C/h3\u003E\n","\u003Cp\u003ENow let's see what happens when two sessions try to update the same row.\u003C/p\u003E\n","\u003Cp\u003EIn \u003Cstrong\u003ESession 1\u003C/strong\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Session 1\nBEGIN;\n\nUPDATE quickstart.order_header\nSET order_status = 'PROCESSING'\nWHERE order_id = (SELECT MIN(order_id) FROM quickstart.order_header);\n-- Don't commit\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIn \u003Cstrong\u003ESession 2\u003C/strong\u003E, try to update the same row:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Session 2\n-- This will wait for Session 1 to commit or rollback\nUPDATE quickstart.order_header\nSET order_status = 'SHIPPED'\nWHERE order_id = (SELECT MIN(order_id) FROM quickstart.order_header);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESession 2 will wait for Session 1's lock to be released.  This is known as a blocking lock.\u003C/p\u003E\n","\u003Cp\u003EIn a third terminal, you can view the locks that have not been granted (blocked):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-shell\"\u003Epsql -U snowflake_admin -d quickstart_db\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Session 3\nSELECT l.locktype, l.relation::regclass, l.mode,\n       l.granted, a.usename, a.query, a.state\nFROM pg_locks l\nJOIN pg_stat_activity a ON l.pid = a.pid\nWHERE a.datname = 'quickstart_db' and l.granted = false\nORDER BY l.granted;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EBack in \u003Cstrong\u003ESession 1\u003C/strong\u003E, commit to release the lock:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Session 1\nCOMMIT;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESession 2's update will now complete immediately.\u003C/p\u003E\n","\u003Ch2\u003EConsistency and Transactions\u003C/h2\u003E\n","\u003Cp\u003EPostgres provides ACID compliance, allowing you to run multi-statement operations atomically. In this step, we'll demonstrate updating a truck's information while maintaining historical records.\u003C/p\u003E\n","\u003Ch3\u003EMulti-Statement Transaction\u003C/h3\u003E\n","\u003Cp\u003EWe'll simulate a truck owner acquiring a new truck of the same model. We'll:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EUpdate the YEAR column in the TRUCK table\u003C/li\u003E\u003Cli\u003EUpdate TRUCK_HISTORY to mark the old record's end time\u003C/li\u003E\u003Cli\u003EInsert a new record in TRUCK_HISTORY\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Begin transaction\nBEGIN;\n\n-- Update YEAR in TRUCK table\nUPDATE quickstart.truck \nSET year = 2024, \n    record_start_time = current_timestamp\nWHERE truck_id = 2;\n    \n-- Update TRUCK_HISTORY to mark the end of the old record\nUPDATE quickstart.truck_history \nSET record_end_time = current_timestamp\nWHERE truck_id = 2 AND record_end_time IS NULL;\n    \n-- Insert new record in TRUCK_HISTORY\nINSERT INTO quickstart.truck_history \nSELECT *, NULL as record_end_time \nFROM quickstart.truck \nWHERE truck_id = 2;\n\n-- Commit the transaction\nCOMMIT;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAll three operations execute as a single atomic unit. If any operation fails, all changes are rolled back.\u003C/p\u003E\n","\u003Ch3\u003EVerify Changes Data\u003C/h3\u003E\n","\u003Cp\u003ENow let's verify the changes:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Should return two records (one closed, one current)\nSELECT truck_id, year, record_start_time, record_end_time,\n    CASE \n        WHEN record_end_time IS NULL THEN 'CURRENT'\n        ELSE 'HISTORICAL'\n    END as record_status\nFROM quickstart.truck_history \nWHERE truck_id = 2\nORDER BY record_start_time;\n\n-- Should return the updated record\nSELECT truck_id, year, record_start_time \nFROM quickstart.truck \nWHERE truck_id = 2;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EJoining Tables\u003C/h2\u003E\n","\u003Cp\u003EPostgres excels at joining tables efficiently using various join algorithms (nested loop, hash join, merge join).\u003C/p\u003E\n","\u003Ch3\u003EExplore Data\u003C/h3\u003E\n","\u003Cp\u003ELet's review our tables:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- List all tables\n\\dt quickstart.*\n\n-- View sample data\nSELECT * FROM quickstart.truck_history LIMIT 10;\nSELECT * FROM quickstart.order_header LIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EJoin Tables\u003C/h3\u003E\n","\u003Cp\u003EJoin ORDER_HEADER with TRUCK_HISTORY to get order details with truck information:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Simple join\nSELECT oh.order_id, oh.order_ts, oh.order_total, oh.order_status,\n       th.truck_id, th.make, th.model, th.primary_city\nFROM quickstart.order_header oh\n     JOIN quickstart.truck_history th ON oh.truck_id = th.truck_id\nWHERE th.record_end_time IS NULL\nLIMIT 10;\n\n-- More complex analytical query\nSELECT th.primary_city, th.make, th.model,\n    COUNT(oh.order_id) as total_orders,\n    SUM(oh.order_total) as total_revenue,\n    AVG(oh.order_total) as avg_order_value,\n    MAX(oh.order_ts) as last_order_date\nFROM quickstart.order_header oh\n     JOIN quickstart.truck_history th ON oh.truck_id = th.truck_id\nWHERE th.record_end_time IS NULL\n      AND oh.order_status = 'COMPLETED'\nGROUP BY th.primary_city, th.make, th.model\nORDER BY total_revenue DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EQuery Performance\u003C/h3\u003E\n","\u003Cp\u003EView the query execution plan:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Analyze query performance\nEXPLAIN (ANALYZE, BUFFERS, MEMORY)\n    SELECT oh.order_id, oh.customer_id, oh.order_ts, t.primary_city, t.make, t.model\n    FROM quickstart.order_header oh\n         JOIN quickstart.truck t ON oh.truck_id = t.truck_id\n    WHERE oh.order_status = 'COMPLETED';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe \u003Ccode\u003EEXPLAIN ANALYZE\u003C/code\u003E output shows:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EJoin method used (Hash Join, Nested Loop, Merge Join)\u003C/li\u003E\u003Cli\u003EIndex usage\u003C/li\u003E\u003Cli\u003EEstimated vs actual rows\u003C/li\u003E\u003Cli\u003EExecution time\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EFor a deeper dive into using EXPLAIN and understanding execution plans, see the \u003Ca href=\"/en/developers/guides/snowflake-postgres-execution-plan/\"\u003EUnderstanding Execution Plans\u003C/a\u003E guide.\u003C/p\u003E\n","\u003Ch2\u003ESecurity & Governance\u003C/h2\u003E\n","\u003Cp\u003EPostgres provides robust security features including role-based access control, row-level security, and data masking capabilities.\u003C/p\u003E\n","\u003Ch3\u003EAccess Control and User Management\u003C/h3\u003E\n","\u003Cp\u003EPostgres uses role-based access control (RBAC) similar to SQL standards.\u003C/p\u003E\n","\u003Cp\u003ECreate a new role with limited privileges:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Create BI User\nCREATE ROLE quickstart_bi_user WITH LOGIN PASSWORD 'bi_user_password';\n\n-- Create a new role for BI users\nCREATE ROLE quickstart_bi_role;\n\n-- Grant connection to database\nGRANT CONNECT ON DATABASE quickstart_db TO quickstart_bi_role;\n\n-- Grant schema usage\nGRANT USAGE ON SCHEMA quickstart TO quickstart_bi_role;\n\n-- Grant role to BI user\nGRANT quickstart_bi_role TO quickstart_bi_user;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIn a new session, connect as the BI user.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epsql -U quickstart_bi_user -d quickstart_db -W\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETest access without SELECT privileges:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Try to select data (this will fail)\nSELECT * FROM quickstart.order_header LIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should receive an error:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EERROR: permission denied for table order_header\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EGrant SELECT privileges from the \u003Ccode\u003Esnowflake_admin\u003C/code\u003E session:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Back in the admin session\nGRANT SELECT ON ALL TABLES IN SCHEMA quickstart TO quickstart_bi_role;\n\n-- Grant SELECT on future tables too\nALTER DEFAULT PRIVILEGES IN SCHEMA quickstart \nGRANT SELECT ON TABLES TO quickstart_bi_role;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow try again as the BI user:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- As quickstart_bi_user\nSELECT * FROM quickstart.order_header LIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis time it works!\u003C/p\u003E\n","\u003Ch3\u003EColumn-Level Security\u003C/h3\u003E\n","\u003Cp\u003EWhile Postgres doesn't have built-in dynamic data masking like some commercial databases, we can implement it using views and functions.\u003C/p\u003E\n","\u003Cp\u003ECreate a masking function and view:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Create a function to mask email addresses\nCREATE OR REPLACE FUNCTION quickstart.mask_email(email VARCHAR, user_role NAME)\nRETURNS VARCHAR AS $$\nBEGIN\n    -- Show full email to admin roles only\n    IF user_role IN ('snowflake_admin', 'quickstart_user') THEN\n        RETURN email;\n    ELSE\n        RETURN '***MASKED***';\n    END IF;\nEND;\n$$ LANGUAGE plpgsql SECURITY DEFINER;\n\n-- Create a view with masked data\nCREATE OR REPLACE VIEW quickstart.truck_masked AS\nSELECT \n    truck_id,\n    menu_type_id,\n    primary_city,\n    region,\n    iso_region,\n    country,\n    iso_country_code,\n    franchise_flag,\n    year,\n    make,\n    model,\n    ev_flag,\n    franchise_id,\n    truck_opening_date,\n    quickstart.mask_email(truck_email, current_user) as truck_email,\n    record_start_time\nFROM quickstart.truck;\n\n-- Grant access to the view\nGRANT SELECT ON quickstart.truck_masked TO quickstart_bi_role;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETest as admin (emails visible):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- As snowflake_admin or quickstart_user\nSELECT truck_id, truck_email FROM quickstart.truck_masked LIMIT 5;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETest as BI user (emails masked):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- As quickstart_bi_user\nSELECT truck_id, truck_email FROM quickstart.truck_masked LIMIT 5;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ERow Level Security (RLS)\u003C/h3\u003E\n","\u003Cp\u003EPostgres supports row-level security to restrict which rows users can see:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Create a policy: BI user can only see completed orders\nCREATE POLICY bi_user_completed_orders ON quickstart.order_header\n    FOR SELECT\n    TO quickstart_bi_user\n    USING (order_status = 'COMPLETED');\n\n-- Admin users can see all orders\nCREATE POLICY admin_all_orders ON quickstart.order_header\n    FOR ALL\n    TO quickstart_user, snowflake_admin\n    USING (true);\n\n-- Enable RLS on order_header\nALTER TABLE quickstart.order_header ENABLE ROW LEVEL SECURITY;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETest as BI user (quickstart_bi_user) - only sees completed orders\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT order_status, count(1) cnt FROM quickstart.order_header GROUP BY order_status;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETest as admin (quickstart_user or snowflake_admin) - sees all orders\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT order_status, count(1) cnt FROM quickstart.order_header GROUP BY order_status;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003ECleanup\u003C/h2\u003E\n","\u003Cp\u003EIf you wish to keep the Postgres cluster for other testing, skip the following SQL and perform the Postgres Clean section.\u003C/p\u003E\n","\u003Ch3\u003EOption 1: Drop Postgres Cluster\u003C/h3\u003E\n","\u003Cp\u003EFrom in Snowsight, select the cluster Postgres instance lists.  Under the menu (three dots) beside the Manage menu, select Drop and follow the confirmation prompts to drop the Postgres Instance.\u003C/p\u003E\n","\u003Ch3\u003EOption 2: Postgres Cleanup\u003C/h3\u003E\n","\u003Cp\u003EOption 2 is to clean-up Postgres if the goal is to keep the cluster.\u003C/p\u003E\n","\u003Cp\u003ETo clean up your Postgres environment:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Connect as superuser\n\\c postgres\n\n-- Terminate active connections to the database\nSELECT pg_terminate_backend(pid)\nFROM pg_stat_activity\nWHERE datname = 'quickstart_db' AND pid <> pg_backend_pid();\n\n-- Drop database and roles\nDROP DATABASE IF EXISTS quickstart_db;\nDROP ROLE IF EXISTS quickstart_user;\nDROP ROLE IF EXISTS quickstart_bi_user_role;\nDROP ROLE IF EXISTS quickstart_bi_user;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\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 a Snowflake Postgres cluster\u003C/li\u003E\u003Cli\u003ECreated Postgres tables and loaded data\u003C/li\u003E\u003Cli\u003EExplored table structures, constraints, and indexes\u003C/li\u003E\u003Cli\u003ELearned about unique and foreign key constraints\u003C/li\u003E\u003Cli\u003EUnderstood row-level locking and MVCC\u003C/li\u003E\u003Cli\u003EImplemented multi-statement transactions for data consistency\u003C/li\u003E\u003Cli\u003EPerformed efficient joins between tables\u003C/li\u003E\u003Cli\u003EImplemented security and access control policies\u003C/li\u003E\u003Cli\u003ECreated data masking and row-level security policies\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources:\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://www.postgresql.org/docs/\"\u003EPostgreSQL Official Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.crunchydata.com/developers/tutorials\"\u003EPostgreSQL Tutorial\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://wiki.postgresql.org/wiki/Performance_Optimization\"\u003EPostgreSQL Performance Tuning\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.postgresql.org/docs/current/user-manag.html\"\u003EPostgreSQL Security Best Practices\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.postgresql.org/docs/current/explicit-locking.html\"\u003EUnderstanding PostgreSQL Locks\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.postgresql.org/docs/current/mvcc.html\"\u003EPostgreSQL MVCC in Detail\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/getting-started-with-hybrid-tables/\"\u003EGetting Started with Hybrid Tables\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/snowflake-postgres-execution-plan/\"\u003EUnderstanding Execution Plans\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ENext Steps:\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EExplore \u003Ca href=\"https://www.postgresql.org/docs/current/ddl-partitioning.html\"\u003Epartitioning\u003C/a\u003E for large tables\u003C/li\u003E\u003Cli\u003ELearn about \u003Ca href=\"https://www.postgresql.org/docs/current/logical-replication.html\"\u003Elogical replication\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EImplement \u003Ca href=\"https://www.postgresql.org/docs/current/textsearch.html\"\u003Efull-text search\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EOptimize queries with \u003Ca href=\"https://www.postgresql.org/docs/current/indexes.html\"\u003Eadvanced indexing strategies\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"elements":{"quickstartArticleBody":{"value":"\u003C!-- ------------------------ --\u003E\n## Overview \n\n### Postgres for Transactional Workloads\n\n[PostgreSQL](https://www.postgresql.org/) is a powerful, open-source object-relational database system that is optimized for transactional workloads requiring low latency and high throughput on small random point reads/writes. Pronounced `Post-gres-Q-L` or just `Postgres` for short.  Postgres supports robust referential integrity constraint enforcement, ACID compliance, and advanced indexing that is critical for transactional workloads. You can use Postgres tables with various features like partitioning, foreign data wrappers, logical replication, and a vast set of extensions to power transactional workloads with various needs.\n\nUse cases that benefit from Postgres include:\n\n- Application requires Postgres compatibility\n- Low-latency data servicing and ingestion\n- Implement full ACID-compliant transactional systems\n\n### Architecture\n\nPostgres uses a proven architecture with over 35 years of active development. The system consists of:\n\n- **Client connections** via standard Postgres protocol (libpq)\n- **Query parser and planner** that optimizes SQL queries for best performance\n- **Storage engine** with MVCC (Multi-Version Concurrency Control) for concurrent access\n- **WAL (Write-Ahead Logging)** for durability and crash recovery\n- **Background processes** for vacuuming, checkpointing, and statistics\n\nKey benefits of this architecture:\n\n- ACID compliance ensures data consistency and reliability\n- Support for concurrent reads and writes without blocking\n- Advanced indexing (B-tree, Hash, GiST, GIN, BRIN) for optimal query performance\n- Native support for JSON, full-text search, and geospatial data\n- Extensible with custom functions, data types, and extensions\n\nPostgres stores data in a row-oriented format by default, which provides excellent performance for transactional workloads. \n\n### What You Will Learn \n\n- The basics of Postgres tables and constraints\n- How to create and use Postgres tables with indexes\n- The advantages of proper constraint and index usage\n- Postgres characteristics like indexes, primary keys, unique and foreign keys\n- Row-level locking and transaction isolation\n\n### Prerequisites\n\n- Familiarity with SQL\n- Snowflake Postgres cluster\n- Postgres client (psql or your preferred client)\n- Database superuser or sufficient privileges to create databases and roles\n- Basic understanding of relational database concepts\n\nNote:  Several of the commands used in this guide are specific to `psql`.  If another tool is used, some commands may need to be skipped.\n\n## Setup\n\nIn this section, we will set up our Postgres environment, create a database, role, schema, and tables that we will use throughout this tutorial.  If you need to install `psql` on MacOS, use `brew install postgresql@18`.\n\n### Step 1 Create Snowflake Postgres Cluster\n\nFollow the ![Getting Started with Snowflake Postgres](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowflake-postgres-getting-started-with-tables/assets/en/developers/guides/getting-started-with-snowflake-postgres/?v=32b758de) to create a Postgres instance and configure network policies and rules.\n\n### Step 2 Connect to Postgres\n\nTo connect to Snowflake Postgres, you will need to get the login credentials for the `snowflake_admin` account.  These credentials are generated during the creation of the Postgres cluster or can be regenerated using `ALTER POSTGRES INSTANCE RESET ACCESS FOR 'snowflake_admin';`.\n\nSave the credentials as environment variables to simplify the connection process.\n\n```bash\nexport PGHOST=\"\u003CSnowflake host name\u003E\"\nexport PGDATABASE=\"postgres\"\nexport PGUSER=\"snowflake_admin\"\nexport PGPASSWORD=\"\u003Cpassword\u003E\"\nexport PGSSLMODE=require\n``` \n\nConnect to your Postgres instance using psql or your preferred client:\n\n```bash\n# Connect to Postgres as an admin user\npsql -U snowflake_admin\n```\n\n### Step 3 Setup\n\n#### Create Database Objects\n\nCreate a role, database, and schema for our quickstart:\n\n```sql\n-- Create role quickstart_user\nCREATE ROLE quickstart_user WITH LOGIN PASSWORD 'secure_password_here';\n\n-- Grant database creation privilege\nALTER ROLE quickstart_user CREATEDB;\n\n-- Create database\nCREATE DATABASE quickstart_db;\n```\n\nWith the newly created `quickstart_db`, change the active (connected) database to be `quickstart_db` and create the `quickstart` schema.\n\n```sql\n-- Connect to the new database\n\\c quickstart_db\n\n-- Create schema\nCREATE SCHEMA IF NOT EXISTS quickstart;\n\n-- Grant necessary privileges\nGRANT ALL PRIVILEGES ON SCHEMA quickstart TO quickstart_user;\nGRANT ALL PRIVILEGES ON DATABASE quickstart_db TO quickstart_user;\n```\n\n#### Create Tables and Load Data\n\nIn this quickstart, we will use Tasty Bytes fictional food truck business data to simulate a typical transactional use case.\n\nWe will create three tables:\n- **ORDER_HEADER** - Stores order metadata such as TRUCK_ID, CUSTOMER_ID, ORDER_AMOUNT, etc.\n- **TRUCK** - Stores truck metadata such as TRUCK_ID, FRANCHISE_ID, MENU_TYPE_ID, etc.\n- **TRUCK_HISTORY** - Stores historical TRUCK information, enabling you to track changes over time\n\nFirst, let's create the TRUCK table with appropriate constraints:\n\n```sql\n-- Create TRUCK table\nCREATE TABLE quickstart.truck (\n    truck_id INTEGER NOT NULL,\n    menu_type_id INTEGER,\n    primary_city VARCHAR(255),\n    region VARCHAR(255),\n    iso_region VARCHAR(255),\n    country VARCHAR(255),\n    iso_country_code VARCHAR(10),\n    franchise_flag INTEGER,\n    year INTEGER,\n    make VARCHAR(255),\n    model VARCHAR(255),\n    ev_flag INTEGER,\n    franchise_id INTEGER,\n    truck_opening_date DATE,\n    truck_email VARCHAR(255) NOT NULL UNIQUE,\n    record_start_time TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,\n    PRIMARY KEY (truck_id)\n);\n\n-- Create index on commonly queried columns\nCREATE INDEX idx_truck_franchise ON quickstart.truck(franchise_id);\nCREATE INDEX idx_truck_city ON quickstart.truck(primary_city);\n```\n\nCreate the TRUCK_HISTORY table for tracking historical changes:\n\n```sql\n-- Create TRUCK_HISTORY table\nCREATE TABLE quickstart.truck_history (\n    truck_id INTEGER NOT NULL,\n    menu_type_id INTEGER,\n    primary_city VARCHAR(255),\n    region VARCHAR(255),\n    iso_region VARCHAR(255),\n    country VARCHAR(255),\n    iso_country_code VARCHAR(10),\n    franchise_flag INTEGER,\n    year INTEGER,\n    make VARCHAR(255),\n    model VARCHAR(255),\n    ev_flag INTEGER,\n    franchise_id INTEGER,\n    truck_opening_date DATE,\n    truck_email VARCHAR(255) NOT NULL,\n    record_start_time TIMESTAMP WITH TIME ZONE,\n    record_end_time TIMESTAMP WITH TIME ZONE,\n    PRIMARY KEY (truck_id, record_start_time)\n);\n\n-- Create partial index for querying current records\nCREATE INDEX idx_truck_history_current ON quickstart.truck_history(truck_id) \nWHERE record_end_time IS NULL;\n```\n\nCreate the ORDER_HEADER table with foreign key relationships:\n\n```sql\n-- Create ORDER_HEADER table with constraints\nCREATE TABLE quickstart.order_header (\n    order_id BIGINT NOT NULL,\n    truck_id INTEGER,\n    location_id BIGINT,\n    customer_id INTEGER,\n    discount_id FLOAT,\n    shift_id INTEGER,\n    shift_start_time TIME,\n    shift_end_time TIME,\n    order_channel VARCHAR(255),\n    order_ts TIMESTAMP,\n    served_ts VARCHAR(255),\n    order_currency VARCHAR(3),\n    order_amount NUMERIC(10,4),\n    order_tax_amount VARCHAR(255),\n    order_discount_amount VARCHAR(255),\n    order_total NUMERIC(10,4),\n    order_status VARCHAR(50) DEFAULT 'INQUEUE',\n    PRIMARY KEY (order_id),\n    FOREIGN KEY (truck_id) REFERENCES quickstart.truck(truck_id)\n);\n\n-- Create indexes for performance\nCREATE INDEX idx_order_header_truck ON quickstart.order_header(truck_id);\nCREATE INDEX idx_order_header_ts ON quickstart.order_header(order_ts);\nCREATE INDEX idx_order_header_status ON quickstart.order_header(order_status);\nCREATE INDEX idx_order_header_customer ON quickstart.order_header(customer_id);\n```\n\n#### Load Sample Data\n\nNow let's load sample data into our tables. We'll generate sample data using Postgres's `generate_series()` function. You can adjust the row counts to control the volume of test data.\n\n**Step 1: Clear existing data**\n\nFirst, clear any existing data from the tables. We truncate `truck_history` first, then `truck` with CASCADE to handle the foreign key relationship with `order_header`:\n\n```sql\nTRUNCATE TABLE quickstart.truck_history;\nTRUNCATE TABLE quickstart.truck CASCADE;\n```\n\n**Step 2: Load the truck table (1,000 rows)**\n\nGenerate truck records with varied cities, makes, models, and other attributes:\n\n```sql\nINSERT INTO quickstart.truck (\n    truck_id, menu_type_id, primary_city, region, iso_region, country, \n    iso_country_code, franchise_flag, year, make, model, ev_flag, franchise_id, \n    truck_opening_date, truck_email, record_start_time\n)\nSELECT \n    gs.id AS truck_id,\n    (gs.id % 5) + 1 AS menu_type_id,\n    (ARRAY['New York', 'Los Angeles', 'Chicago', 'Houston', 'Phoenix', \n           'Philadelphia', 'San Antonio', 'San Diego', 'Dallas', 'San Jose',\n           'Austin', 'Jacksonville', 'Fort Worth', 'Columbus', 'Charlotte',\n           'San Francisco', 'Indianapolis', 'Seattle', 'Denver', 'Boston'])[(gs.id % 20) + 1] AS primary_city,\n    (ARRAY['Northeast', 'Southeast', 'Midwest', 'Southwest', 'West'])[(gs.id % 5) + 1] AS region,\n    (ARRAY['NY', 'CA', 'IL', 'TX', 'AZ', 'PA', 'FL', 'OH', 'NC', 'WA'])[(gs.id % 10) + 1] AS iso_region,\n    'United States' AS country,\n    'US' AS iso_country_code,\n    (gs.id % 2)::INTEGER AS franchise_flag,\n    2015 + (gs.id % 10) AS year,\n    (ARRAY['Ford', 'Mercedes', 'Freightliner', 'Chevrolet', 'GMC', 'RAM'])[(gs.id % 6) + 1] AS make,\n    (ARRAY['Transit', 'Sprinter', 'MT45', 'Express', 'Savana', 'ProMaster'])[(gs.id % 6) + 1] AS model,\n    (gs.id % 4 = 0)::INTEGER AS ev_flag,\n    100 + (gs.id % 500) AS franchise_id,\n    CURRENT_DATE - ((gs.id % 1825) || ' days')::INTERVAL AS truck_opening_date,\n    gs.id || '_truck@email.com' AS truck_email,\n    CURRENT_TIMESTAMP - ((gs.id % 365) || ' days')::INTERVAL AS record_start_time\nFROM generate_series(1, 1000) AS gs(id);\n```\n\n**Step 3: Load the truck_history table**\n\nCopy all truck records into the history table. This simulates having a historical record for each truck:\n\n```sql\nINSERT INTO quickstart.truck_history \nSELECT *, NULL AS record_end_time \nFROM quickstart.truck;\n```\n\n**Step 4: Load the order_header table (1,000,000 rows)**\n\nGenerate order records that reference existing trucks. This query uses a weighted distribution where 80% of orders go to the top 20% of trucks (simulating popular food trucks):\n\n```sql\nINSERT INTO quickstart.order_header (\n    order_id, truck_id, location_id, customer_id, discount_id, \n    shift_id, shift_start_time, shift_end_time, order_channel, order_ts, \n    served_ts, order_currency, order_amount, order_tax_amount, \n    order_discount_amount, order_total, order_status\n)\nSELECT \n    1000 + gs.id AS order_id,\n    -- Weighted truck selection: 80% of orders go to top 20% of trucks\n    CASE \n        WHEN (gs.id * 17) % 100 \u003C 80 THEN ((gs.id * 31) % 200) + 1  -- Top 200 trucks\n        ELSE ((gs.id * 37) % 1000) + 1                              -- All trucks\n    END AS truck_id,\n    5000 + (gs.id % 1000) AS location_id,\n    2000 + (gs.id % 5000) AS customer_id,\n    CASE WHEN (gs.id * 7) % 100 \u003C 10 THEN ((gs.id * 11) % 20)::FLOAT ELSE 0 END AS discount_id,\n    (gs.id % 3) + 1 AS shift_id,\n    CASE (gs.id % 3)\n        WHEN 0 THEN '08:00:00'::TIME\n        WHEN 1 THEN '16:00:00'::TIME\n        ELSE '00:00:00'::TIME\n    END AS shift_start_time,\n    CASE (gs.id % 3)\n        WHEN 0 THEN '16:00:00'::TIME\n        WHEN 1 THEN '23:00:00'::TIME\n        ELSE '08:00:00'::TIME\n    END AS shift_end_time,\n    (ARRAY['mobile', 'web', 'phone', 'kiosk'])[(gs.id % 4) + 1] AS order_channel,\n    CURRENT_TIMESTAMP - ((1000000 - gs.id) % 180 || ' days')::INTERVAL \n        + ((gs.id % 86400) || ' seconds')::INTERVAL AS order_ts,\n    CASE \n        WHEN (gs.id * 13) % 100 \u003C 80 THEN \n            (CURRENT_TIMESTAMP - ((1000000 - gs.id) % 180 || ' days')::INTERVAL \n                + ((gs.id % 86400 + 300) || ' seconds')::INTERVAL)::TEXT\n        ELSE ''\n    END AS served_ts,\n    'USD' AS order_currency,\n    (10 + ((gs.id * 19) % 90))::NUMERIC(10,4) AS order_amount,\n    ((10 + ((gs.id * 19) % 90)) * 0.08)::NUMERIC(10,4)::TEXT AS order_tax_amount,\n    CASE \n        WHEN (gs.id * 23) % 100 \u003C 10 THEN (((gs.id * 29) % 20) * 0.1)::NUMERIC(10,4)::TEXT\n        ELSE '0'\n    END AS order_discount_amount,\n    ((10 + ((gs.id * 19) % 90)) * 1.08)::NUMERIC(10,4) AS order_total,\n    (ARRAY['COMPLETED', 'COMPLETED', 'COMPLETED', 'INQUEUE', 'PROCESSING'])[(gs.id % 5) + 1] AS order_status\nFROM generate_series(1, 1000000) AS gs(id);\n```\n\n\u003E **Note**: This insert may take 30-60 seconds depending on your environment.\n\n**Step 5: Verify data relationships**\n\nConfirm the foreign key relationships are intact by checking that all orders reference valid trucks:\n\n```sql\nSELECT \n    (SELECT COUNT(*) FROM quickstart.truck) AS trucks,\n    (SELECT COUNT(*) FROM quickstart.truck_history) AS truck_history,\n    (SELECT COUNT(*) FROM quickstart.order_header) AS orders,\n    (SELECT COUNT(DISTINCT truck_id) FROM quickstart.order_header) AS trucks_with_orders;\n```\n\nAlthough autovacuum will run and perform the necessary background statistics gathering, etc. the below commands will manually perform a vacuum and analyze on the newly populated tables.\n\n```sql\nVACUUM ANALYZE quickstart.truck;\nVACUUM ANALYZE quickstart.truck_history;\nVACUUM ANALYZE quickstart.order_header;\n```\n\n## Explore Data\n\nIn the previous Setup step, we created our database, schema, and tables. Let's explore them now.\n\n```sql\n-- Set context\n\\c quickstart_db\n```\n\nWe created and loaded data into the TRUCK and ORDER_HEADER tables. Now we can run queries to get familiar with them.\n\nView table information:\n\n```sql\n-- List all tables in the data schema and their size\n\\dt+ quickstart.*\n\n-- View detailed table information\n\\d+ quickstart.truck\n\\d+ quickstart.order_header\n\\d+ quickstart.truck_history\n```\n\nDisplay information about constraints and indexes:\n\n```sql\n-- View all constraints on TRUCK table\nSELECT con.conname AS constraint_name, con.contype AS constraint_type,\n    CASE con.contype\n        WHEN 'p' THEN 'PRIMARY KEY'\n        WHEN 'u' THEN 'UNIQUE'\n        WHEN 'f' THEN 'FOREIGN KEY'\n        WHEN 'c' THEN 'CHECK'\n        ELSE 'OTHER'\n    END AS constraint_description\nFROM pg_constraint con\n     JOIN pg_class rel ON rel.oid = con.conrelid\n     JOIN pg_namespace nsp ON nsp.oid = rel.relnamespace\nWHERE nsp.nspname = 'quickstart' AND rel.relname = 'truck';\n\n-- View all indexes\nSELECT tablename, indexname, indexdef\nFROM pg_indexes\nWHERE schemaname = 'quickstart'\nORDER BY tablename, indexname;\n```\n\nLook at sample data from the tables:\n\n```sql\n-- Query 10 rows from TRUCK table\nSELECT * FROM quickstart.truck LIMIT 10;\n\n-- Query 10 rows from TRUCK_HISTORY table\nSELECT * FROM quickstart.truck_history LIMIT 10;\n\n-- Query 10 rows from ORDER_HEADER table\nSELECT * FROM quickstart.order_header LIMIT 10;\n\n-- View table row counts\nSELECT 'truck' as table_name, COUNT(*) as row_count FROM quickstart.truck\nUNION ALL\nSELECT 'order_header', COUNT(*) FROM quickstart.order_header\nUNION ALL\nSELECT 'truck_history', COUNT(*) FROM quickstart.truck_history;\n```\n\n## Unique and Foreign Key Constraints\n\nIn this section, we will test Unique and Foreign Key constraints.\n\n### Unique Constraints\n\nPostgres's unique constraint ensures that all values in a column are different.\n\nIn the TRUCK table, we defined the TRUCK_EMAIL column as NOT NULL and UNIQUE.  A key difference between the uniqueness enforced by a PRIMARY KEY constraint and a UNIQUE constraint is the PRIMARY KEY does not allow NULL values where the UNIQUE constraint does.  In the case of the TRUCK_EMAIL we specified NOT NULL to require values for this field.\n\nDisplay constraint information:\n\n```sql\n-- Set context\n\\c quickstart_db\n\n-- View constraints on TRUCK table\n\\d quickstart.truck\n\n-- Or get detailed constraint information\nSELECT \n    con.conname AS constraint_name,\n    pg_get_constraintdef(con.oid) AS constraint_definition\nFROM pg_constraint con\nJOIN pg_class rel ON rel.oid = con.conrelid\nWHERE rel.relname = 'truck' AND con.contype IN ('u', 'p');\n```\n\nDue to the unique constraint, attempting to insert two records with the same email address will fail:\n\n```sql\n-- Attempt insert with duplicate email (will fail)\nINSERT INTO quickstart.truck VALUES (\n    (SELECT MAX(truck_id) + 1 FROM quickstart.truck),\n    2, 'Stockholm', 'Stockholm län', 'Stockholm', 'Sweden', 'SE', \n    1, 2001, 'Freightliner', 'MT45 Utilimaster', 0, 276, '2020-10-01',\n    (SELECT truck_email FROM quickstart.truck LIMIT 1),\n    CURRENT_TIMESTAMP\n);\n```\n\nYou should receive an error message similar to:\n```\nERROR: duplicate key value violates unique constraint \"truck_truck_email_key\"\nDETAIL: Key (truck_email)=(1_truck@email.com) already exists.\n```\n\nNow let's insert a new record with a unique email address:\n\n```sql\n-- Create a new unique email and insert successfully using CTE\nWITH new_truck AS (\n    SELECT MAX(truck_id) + 1 as new_truck_id,\n           (MAX(truck_id) + 1)::TEXT || '_truck@email.com' as new_email\n    FROM quickstart.truck\n)\nINSERT INTO quickstart.truck (\n    truck_id, menu_type_id, primary_city, region, iso_region, country,\n    iso_country_code, franchise_flag, year, make, model, ev_flag, \n    franchise_id, truck_opening_date, truck_email, record_start_time\n)\nSELECT new_truck_id, 2, 'Stockholm', 'Stockholm län', 'Stockholm', 'Sweden',\n    'SE', 1, 2001, 'Freightliner', 'MT45 Utilimaster', 0, \n    276, '2020-10-01', new_email, CURRENT_TIMESTAMP\nFROM new_truck\nRETURNING truck_id, truck_email;\n```\n\nStatement should run successfully.\n\n### Foreign Key Constraints\n\nForeign key constraints ensure referential integrity between tables.\n\nFirst, let's examine the foreign key constraints on the ORDER_HEADER table:\n\n```sql\n-- View foreign key constraints\nSELECT con.conname AS constraint_name, pg_get_constraintdef(con.oid) AS constraint_definition\nFROM pg_constraint con\nJOIN pg_class rel ON rel.oid = con.conrelid\nWHERE rel.relname = 'order_header' AND con.contype = 'f';\n```\n\nNow let's try to insert a record with a non-existent truck_id (this will fail):\n\n```sql\n-- Attempt to insert order with non-existent truck\nINSERT INTO quickstart.order_header VALUES (\n    (SELECT MAX(order_id) + 1 FROM quickstart.order_header),\n    -1,  -- Non-existent truck_id\n    6090, 0, 0, 0, '16:00:00', '23:00:00', 'web', \n    '2024-02-18 21:38:46', '', 'USD', 17.0000, '', '', 17.0000, 'PENDING'\n);\n```\n\nThe statement should fail with an error:\n```\nERROR: insert or update on table \"order_header\" violates foreign key constraint \"order_header_truck_id_fkey\"\nDETAIL: Key (truck_id)=(-1) is not present in table \"truck\".\n```\n\nNow let's insert with a valid truck_id:\n\n```sql\n-- Insert with valid truck_id\nINSERT INTO quickstart.order_header VALUES (\n    (SELECT MAX(order_id) + 1 FROM quickstart.order_header),\n    (SELECT truck_id FROM quickstart.truck LIMIT 1),\n    6090, 0, 0, 0, '16:00:00', '23:00:00', 'web', \n    '2024-02-18 21:38:46', '', 'USD', 17.0000, '', '', 17.0000, 'PENDING'\n);\n```\n\nStatement should run successfully.\n\n### Foreign Key and TRUNCATE\n\nTables referenced by foreign key constraints have special behavior with TRUNCATE:\n\n```sql\n-- Attempt to truncate the parent table (this will fail)\nTRUNCATE TABLE quickstart.truck;\n```\n\nThe statement should fail with an error:\n```\nERROR: cannot truncate a table referenced in a foreign key constraint\nDETAIL: Table \"order_header\" references \"truck\".\nHINT: Truncate table \"order_header\" at the same time, or use TRUNCATE ... CASCADE.\n```\n\n### Foreign Key and DELETE\n\nRecords referenced by foreign keys cannot be deleted unless the referencing records are removed first:\n\n```sql\n-- Try to delete a truck that has orders (will fail)\nDELETE FROM quickstart.truck WHERE truck_id = 1;\n```\n\nThe statement should fail with:\n```\nERROR: update or delete on table \"truck\" violates foreign key constraint \"order_header_truck_id_fkey\" on table \"order_header\"\nDETAIL: Key (truck_id)=(1) is still referenced from table \"order_header\".\n```\n\nTo successfully delete a truck, first delete its orders:\n\n```sql\n-- Delete order header\nDELETE FROM quickstart.order_header\nWHERE truck_id = 1;\n\n-- Delete truck\nDELETE FROM quickstart.truck\nWHERE truck_id = 1;\n```\n\nBoth operations complete successfully.  As an alternative, the foreign key constraint could have been created with the CASCADE clause that would automatically delete all child records when the parent was deleted.\n\n## Row Level Locking\n\nPostgres uses Multi-Version Concurrency Control (MVCC) with row-level locking for update operations. This allows for concurrent updates on independent records without blocking reads.\n\nIn this step, we will test concurrent updates to different records.\n\n### Setup for Testing\n\nOpen two `psql` sessions or two connections using the client tool of your choice. We'll call them Session 1 and Session 2.  Remember to set the environment variables with the connection information in each session if using `psql`.  Optionally, open a third terminal/session to monitor locks.\n\n**Session 1:**\n```bash\npsql -U snowflake_admin -d quickstart_db\n```\n\n**Session 2:**\n```bash\npsql -U snowflake_admin -d quickstart_db\n```\n\n### Running Concurrent Updates\n\nIn **Session 1**, start a transaction and update a record:\n\n```sql\n-- Session 1\n\n-- Begin transaction\nBEGIN;\n\n-- Update the record with highest order_id\nUPDATE quickstart.order_header\nSET order_status = 'COMPLETED'\nWHERE order_id = (SELECT MAX(order_id) FROM quickstart.order_header);\n\n-- DO NOT COMMIT YET!\n```\n\nNote that we haven't committed the transaction. There's now an open lock on this record.\n\nIn **Session 2**, update a different record:\n\n```sql\n-- Session 2\n\n-- Check active transactions\nSELECT pid, usename, state, query_start, query \nFROM pg_stat_activity \nWHERE datname = 'quickstart_db' AND state = 'idle in transaction';\n\n-- Update a different record (this will succeed immediately)\nUPDATE quickstart.order_header\nSET order_status = 'COMPLETED'\nWHERE order_id = (SELECT MIN(order_id) FROM quickstart.order_header);\n```\n\nSince Postgres uses row-level locking, the second update succeeds immediately without waiting. The locked row in Session 1 doesn't block updates to other rows.\n\nBack in **Session 1**, commit the transaction:\n\n```sql\n-- Session 1\n-- Commit the transaction\nCOMMIT;\n```\n\n### Testing Lock Contention\n\nNow let's see what happens when two sessions try to update the same row.\n\nIn **Session 1**:\n```sql\n-- Session 1\nBEGIN;\n\nUPDATE quickstart.order_header\nSET order_status = 'PROCESSING'\nWHERE order_id = (SELECT MIN(order_id) FROM quickstart.order_header);\n-- Don't commit\n```\n\nIn **Session 2**, try to update the same row:\n```sql\n-- Session 2\n-- This will wait for Session 1 to commit or rollback\nUPDATE quickstart.order_header\nSET order_status = 'SHIPPED'\nWHERE order_id = (SELECT MIN(order_id) FROM quickstart.order_header);\n```\n\nSession 2 will wait for Session 1's lock to be released.  This is known as a blocking lock.\n\nIn a third terminal, you can view the locks that have not been granted (blocked):\n\n```shell\npsql -U snowflake_admin -d quickstart_db\n```\n\n```sql\n-- Session 3\nSELECT l.locktype, l.relation::regclass, l.mode,\n       l.granted, a.usename, a.query, a.state\nFROM pg_locks l\nJOIN pg_stat_activity a ON l.pid = a.pid\nWHERE a.datname = 'quickstart_db' and l.granted = false\nORDER BY l.granted;\n```\n\nBack in **Session 1**, commit to release the lock:\n```sql\n-- Session 1\nCOMMIT;\n```\n\nSession 2's update will now complete immediately.\n\n## Consistency and Transactions\n\nPostgres provides ACID compliance, allowing you to run multi-statement operations atomically. In this step, we'll demonstrate updating a truck's information while maintaining historical records.\n\n### Multi-Statement Transaction\n\nWe'll simulate a truck owner acquiring a new truck of the same model. We'll:\n1. Update the YEAR column in the TRUCK table\n2. Update TRUCK_HISTORY to mark the old record's end time\n3. Insert a new record in TRUCK_HISTORY\n\n```sql\n-- Begin transaction\nBEGIN;\n\n-- Update YEAR in TRUCK table\nUPDATE quickstart.truck \nSET year = 2024, \n    record_start_time = current_timestamp\nWHERE truck_id = 2;\n    \n-- Update TRUCK_HISTORY to mark the end of the old record\nUPDATE quickstart.truck_history \nSET record_end_time = current_timestamp\nWHERE truck_id = 2 AND record_end_time IS NULL;\n    \n-- Insert new record in TRUCK_HISTORY\nINSERT INTO quickstart.truck_history \nSELECT *, NULL as record_end_time \nFROM quickstart.truck \nWHERE truck_id = 2;\n\n-- Commit the transaction\nCOMMIT;\n```\n\nAll three operations execute as a single atomic unit. If any operation fails, all changes are rolled back.\n\n### Verify Changes Data \n\nNow let's verify the changes:\n\n```sql\n-- Should return two records (one closed, one current)\nSELECT truck_id, year, record_start_time, record_end_time,\n    CASE \n        WHEN record_end_time IS NULL THEN 'CURRENT'\n        ELSE 'HISTORICAL'\n    END as record_status\nFROM quickstart.truck_history \nWHERE truck_id = 2\nORDER BY record_start_time;\n\n-- Should return the updated record\nSELECT truck_id, year, record_start_time \nFROM quickstart.truck \nWHERE truck_id = 2;\n```\n\n## Joining Tables\n\nPostgres excels at joining tables efficiently using various join algorithms (nested loop, hash join, merge join).\n\n### Explore Data \n\nLet's review our tables:\n\n```sql\n-- List all tables\n\\dt quickstart.*\n\n-- View sample data\nSELECT * FROM quickstart.truck_history LIMIT 10;\nSELECT * FROM quickstart.order_header LIMIT 10;\n```\n\n### Join Tables\n\nJoin ORDER_HEADER with TRUCK_HISTORY to get order details with truck information:\n\n```sql\n-- Simple join\nSELECT oh.order_id, oh.order_ts, oh.order_total, oh.order_status,\n       th.truck_id, th.make, th.model, th.primary_city\nFROM quickstart.order_header oh\n     JOIN quickstart.truck_history th ON oh.truck_id = th.truck_id\nWHERE th.record_end_time IS NULL\nLIMIT 10;\n\n-- More complex analytical query\nSELECT th.primary_city, th.make, th.model,\n    COUNT(oh.order_id) as total_orders,\n    SUM(oh.order_total) as total_revenue,\n    AVG(oh.order_total) as avg_order_value,\n    MAX(oh.order_ts) as last_order_date\nFROM quickstart.order_header oh\n     JOIN quickstart.truck_history th ON oh.truck_id = th.truck_id\nWHERE th.record_end_time IS NULL\n      AND oh.order_status = 'COMPLETED'\nGROUP BY th.primary_city, th.make, th.model\nORDER BY total_revenue DESC;\n```\n\n### Query Performance\n\nView the query execution plan:\n\n```sql\n-- Analyze query performance\nEXPLAIN (ANALYZE, BUFFERS, MEMORY)\n    SELECT oh.order_id, oh.customer_id, oh.order_ts, t.primary_city, t.make, t.model\n    FROM quickstart.order_header oh\n         JOIN quickstart.truck t ON oh.truck_id = t.truck_id\n    WHERE oh.order_status = 'COMPLETED';\n```\n\nThe `EXPLAIN ANALYZE` output shows:\n- Join method used (Hash Join, Nested Loop, Merge Join)\n- Index usage\n- Estimated vs actual rows\n- Execution time\n\nFor a deeper dive into using EXPLAIN and understanding execution plans, see the [Understanding Execution Plans](/en/developers/guides/snowflake-postgres-execution-plan/) guide.\n\n## Security & Governance\n\nPostgres provides robust security features including role-based access control, row-level security, and data masking capabilities.\n\n### Access Control and User Management\n\nPostgres uses role-based access control (RBAC) similar to SQL standards.\n\nCreate a new role with limited privileges:\n\n```sql\n-- Create BI User\nCREATE ROLE quickstart_bi_user WITH LOGIN PASSWORD 'bi_user_password';\n\n-- Create a new role for BI users\nCREATE ROLE quickstart_bi_role;\n\n-- Grant connection to database\nGRANT CONNECT ON DATABASE quickstart_db TO quickstart_bi_role;\n\n-- Grant schema usage\nGRANT USAGE ON SCHEMA quickstart TO quickstart_bi_role;\n\n-- Grant role to BI user\nGRANT quickstart_bi_role TO quickstart_bi_user;\n```\n\nIn a new session, connect as the BI user.\n\n```bash\npsql -U quickstart_bi_user -d quickstart_db -W\n```\n\nTest access without SELECT privileges:\n\n```sql\n-- Try to select data (this will fail)\nSELECT * FROM quickstart.order_header LIMIT 10;\n```\n\nYou should receive an error:\n```\nERROR: permission denied for table order_header\n```\n\nGrant SELECT privileges from the `snowflake_admin` session:\n\n```sql\n-- Back in the admin session\nGRANT SELECT ON ALL TABLES IN SCHEMA quickstart TO quickstart_bi_role;\n\n-- Grant SELECT on future tables too\nALTER DEFAULT PRIVILEGES IN SCHEMA quickstart \nGRANT SELECT ON TABLES TO quickstart_bi_role;\n```\n\nNow try again as the BI user:\n\n```sql\n-- As quickstart_bi_user\nSELECT * FROM quickstart.order_header LIMIT 10;\n```\n\nThis time it works!\n\n### Column-Level Security\n\nWhile Postgres doesn't have built-in dynamic data masking like some commercial databases, we can implement it using views and functions.\n\nCreate a masking function and view:\n\n```sql\n-- Create a function to mask email addresses\nCREATE OR REPLACE FUNCTION quickstart.mask_email(email VARCHAR, user_role NAME)\nRETURNS VARCHAR AS $$\nBEGIN\n    -- Show full email to admin roles only\n    IF user_role IN ('snowflake_admin', 'quickstart_user') THEN\n        RETURN email;\n    ELSE\n        RETURN '***MASKED***';\n    END IF;\nEND;\n$$ LANGUAGE plpgsql SECURITY DEFINER;\n\n-- Create a view with masked data\nCREATE OR REPLACE VIEW quickstart.truck_masked AS\nSELECT \n    truck_id,\n    menu_type_id,\n    primary_city,\n    region,\n    iso_region,\n    country,\n    iso_country_code,\n    franchise_flag,\n    year,\n    make,\n    model,\n    ev_flag,\n    franchise_id,\n    truck_opening_date,\n    quickstart.mask_email(truck_email, current_user) as truck_email,\n    record_start_time\nFROM quickstart.truck;\n\n-- Grant access to the view\nGRANT SELECT ON quickstart.truck_masked TO quickstart_bi_role;\n```\n\nTest as admin (emails visible):\n\n```sql\n-- As snowflake_admin or quickstart_user\nSELECT truck_id, truck_email FROM quickstart.truck_masked LIMIT 5;\n```\n\nTest as BI user (emails masked):\n\n```sql\n-- As quickstart_bi_user\nSELECT truck_id, truck_email FROM quickstart.truck_masked LIMIT 5;\n```\n\n### Row Level Security (RLS)\n\nPostgres supports row-level security to restrict which rows users can see:\n\n```sql\n-- Create a policy: BI user can only see completed orders\nCREATE POLICY bi_user_completed_orders ON quickstart.order_header\n    FOR SELECT\n    TO quickstart_bi_user\n    USING (order_status = 'COMPLETED');\n\n-- Admin users can see all orders\nCREATE POLICY admin_all_orders ON quickstart.order_header\n    FOR ALL\n    TO quickstart_user, snowflake_admin\n    USING (true);\n\n-- Enable RLS on order_header\nALTER TABLE quickstart.order_header ENABLE ROW LEVEL SECURITY;\n```\n\nTest as BI user (quickstart_bi_user) - only sees completed orders\n\n```sql\nSELECT order_status, count(1) cnt FROM quickstart.order_header GROUP BY order_status;\n```\n\nTest as admin (quickstart_user or snowflake_admin) - sees all orders\n\n```sql\nSELECT order_status, count(1) cnt FROM quickstart.order_header GROUP BY order_status;\n```\n\n## Cleanup\n\nIf you wish to keep the Postgres cluster for other testing, skip the following SQL and perform the Postgres Clean section.  \n\n### Option 1: Drop Postgres Cluster\n\nFrom in Snowsight, select the cluster Postgres instance lists.  Under the menu (three dots) beside the Manage menu, select Drop and follow the confirmation prompts to drop the Postgres Instance.\n\n### Option 2: Postgres Cleanup\n\nOption 2 is to clean-up Postgres if the goal is to keep the cluster.\n\nTo clean up your Postgres environment:\n\n```sql\n-- Connect as superuser\n\\c postgres\n\n-- Terminate active connections to the database\nSELECT pg_terminate_backend(pid)\nFROM pg_stat_activity\nWHERE datname = 'quickstart_db' AND pid \u003C\u003E pg_backend_pid();\n\n-- Drop database and roles\nDROP DATABASE IF EXISTS quickstart_db;\nDROP ROLE IF EXISTS quickstart_user;\nDROP ROLE IF EXISTS quickstart_bi_user_role;\nDROP ROLE IF EXISTS quickstart_bi_user;\n```\n\n\n## Conclusion and Resources\n\n### What You Learned\n\nHaving completed this quickstart you have successfully:\n- Created a Snowflake Postgres cluster\n- Created Postgres tables and loaded data\n- Explored table structures, constraints, and indexes\n- Learned about unique and foreign key constraints\n- Understood row-level locking and MVCC\n- Implemented multi-statement transactions for data consistency\n- Performed efficient joins between tables\n- Implemented security and access control policies\n- Created data masking and row-level security policies\n\n### Related Resources:\n- [PostgreSQL Official Documentation](https://www.postgresql.org/docs/)\n- [PostgreSQL Tutorial](https://www.crunchydata.com/developers/tutorials)\n- [PostgreSQL Performance Tuning](https://wiki.postgresql.org/wiki/Performance_Optimization)\n- [PostgreSQL Security Best Practices](https://www.postgresql.org/docs/current/user-manag.html)\n- [Understanding PostgreSQL Locks](https://www.postgresql.org/docs/current/explicit-locking.html)\n- [PostgreSQL MVCC in Detail](https://www.postgresql.org/docs/current/mvcc.html)\n- [Getting Started with Hybrid Tables](/en/developers/guides/getting-started-with-hybrid-tables/)\n- [Understanding Execution Plans](/en/developers/guides/snowflake-postgres-execution-plan/)\n\n### Next Steps:\n- Explore [partitioning](https://www.postgresql.org/docs/current/ddl-partitioning.html) for large tables\n- Learn about [logical replication](https://www.postgresql.org/docs/current/logical-replication.html)\n- Implement [full-text search](https://www.postgresql.org/docs/current/textsearch.html)\n- Optimize queries with [advanced indexing strategies](https://www.postgresql.org/docs/current/indexes.html)\n","title":"Quickstart Article Body","dataType":"string","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"title":"Quickstart Article Logo Image","dataType":"string","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":type":"snowflake-site/components/contentfragment","isDeveloperGuidesPage":false,":items":{},":itemsOrder":[],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-872f979111","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":{"id":"container-cfb8d39561","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-0d6a2ff763","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-05-19",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-dc04ad246a","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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