Join us on YugabyteDB Community Slack
Star us on
Get Started
Slack
GitHub
Get Started
v2.13 (preview) v2.12 (stable) v2.8 (earlier version) v2.6 (earlier version) v2.4 (earlier version) Unsupported versions
  • YUGABYTEDB CORE
    • Quick start
      • 1. Install YugabyteDB
      • 2. Create a local cluster
      • 3. Explore distributed SQL
      • 4. Build an application
        • Java
        • Node.js
        • Go
        • Python
        • Ruby
        • C#
        • PHP
        • C++
        • C
        • Scala
        • Rust
    • Explore
      • SQL features
        • SQL Feature Support
        • PostgreSQL Compatibility
        • Foreign Data Wrappers
        • Schemas and Tables
        • Data Types
        • Data Manipulation
        • Queries and Joins
        • Expressions and Operators
        • Stored Procedures
        • Triggers
        • Advanced features
          • Cursors
          • Table Partitioning
          • Views
          • Savepoints
          • Collations
        • Going beyond SQL
          • Follower reads
          • Tablespaces
        • PostgreSQL extensions
      • Fault tolerance
      • Horizontal scalability
        • Scaling Transactions
        • Sharding Data
      • Transactions
        • Distributed Transactions
        • Isolation Levels
        • Explicit Locking
      • Indexes and Constraints
        • Primary keys
        • Foreign keys
        • Secondary indexes
        • Unique indexes
        • Partial indexes
        • Expression indexes
        • Covering indexes
        • GIN indexes
        • Other constraints
      • JSON support
      • Multi-region deployments
        • Sync replication (3+ regions)
        • Async Replication (2+ regions)
        • Row-Level Geo-Partitioning
        • Read replicas
      • Query tuning
        • Introduction
        • Get query statistics using pg_stat_statements
        • Viewing live queries with pg_stat_activity
        • Analyzing queries with EXPLAIN
        • Optimizing YSQL queries using pg_hint_plan
      • Cluster management
        • Point-in-time recovery
      • Change data capture (CDC)
        • Debezium connector
        • Java CDC console
      • Security
      • Observability
        • Prometheus Integration
        • Grafana Dashboard
    • Drivers and ORMs
      • Java
        • JDBC drivers
        • Hibernate ORM
        • Supported versions
      • Go
        • Go drivers
        • Go ORMs
        • Supported versions
      • C#
        • C# drivers
        • C# ORMs
        • Supported versions
      • NodeJS
        • NodeJS drivers
        • NodeJS ORMs
        • Supported Versions
      • Python
        • Python drivers
        • Python ORMs
        • Supported versions
      • Rust
        • Diesel ORM
    • Develop
      • Learn app development
        • 1. SQL vs NoSQL
        • 2. Data modeling
        • 3. Data types
        • 4. ACID transactions
        • 5. Aggregations
        • 6. Batch operations
        • 7. Date and time
        • 8. Strings and text
        • 9. TTL for data expiration
      • Real-world examples
        • E-Commerce app
        • IoT fleet management
      • Explore sample apps
      • Best practices
      • Cloud-native development
        • Codespaces
        • Gitpod
    • Migrate
      • Migration process overview
      • Migrate from PostgreSQL
        • Convert a PostgreSQL schema
        • Migrate a PostgreSQL application
        • Export PostgreSQL data
        • Prepare a cluster
        • Import PostgreSQL data
        • Verify Migration
    • Deploy
      • Deployment checklist
      • Manual deployment
        • 1. System configuration
        • 2. Install software
        • 3. Start YB-Masters
        • 4. Start YB-TServers
        • 5. Verify deployment
      • Kubernetes
        • Single-zone
          • Open Source
          • Amazon EKS
          • Google Kubernetes Engine
          • Azure Kubernetes Service
        • Multi-zone
          • Amazon EKS
          • Google Kubernetes Engine
        • Multi-cluster
          • Google Kubernetes Engine
        • Best practices
        • Connect Clients
      • Docker
      • Public clouds
        • Amazon Web Services
        • Google Cloud Platform
        • Microsoft Azure
      • Multi-DC deployments
        • Three+ data center (3DC)
        • Asynchronous Replication
        • Read replica clusters
    • Benchmark
      • TPC-C
      • sysbench
      • YCSB
      • Key-value workload
      • Large datasets
      • Scalability
        • Scaling queries
      • Resilience
        • Jepsen testing
      • Performance Troubleshooting
    • Secure
      • Security checklist
      • Enable authentication
        • Enable users
        • Configure client authentication
      • Authentication methods
        • Password authentication
        • LDAP authentication
        • Host-based authentication
        • Trust authentication
      • Role-based access control
        • Overview
        • Manage users and roles
        • Grant privileges
        • Row-level security
        • Column-level security
      • Encryption in transit
        • Create server certificates
        • Enable server-to-server encryption
        • Enable client-to-server encryption
        • Connect to clusters
        • TLS and authentication
      • Encryption at rest
      • Column-level encryption
      • Audit logging
        • Configure audit logging
        • Session-Level Audit Logging
        • Object-Level Audit Logging
      • Vulnerability disclosure policy
    • Manage
      • Back up and restore
        • Export and import data
        • Snapshot and restore data
        • Point-in-time recovery
      • Migrate data
        • Bulk import
        • Bulk export
      • Change cluster configuration
      • Diagnostics reporting
      • Upgrade a deployment
      • Grow cluster
    • Troubleshoot
      • Troubleshooting
      • Cluster level issues
        • YCQL connection issues
        • YEDIS connection Issues
        • Recover tserver/master
        • Replace a failed YB-TServer
        • Replace a failed YB-Master
        • Manual remote bootstrap when a majority of peers fail
      • Node level issues
        • Check servers
        • Inspect logs
        • System statistics
        • Disk failure
        • Common error messages
    • Contribute
      • Core database
        • Contribution checklist
        • Build the source
        • Configure a CLion project
        • Run the tests
        • Coding style
  • YUGABYTEDB ANYWHERE
    • Overview
      • Install
      • Configure
    • Install
      • Prerequisites
      • Prepare the environment
      • Install software
      • Prepare nodes
      • Uninstall software
    • Configure
      • Create admin user
      • Configure cloud providers
      • Configure backup target
      • Configure alerts
    • Create deployments
      • Multi-zone universe
      • Multi-region universe
      • Multi-cloud universe
      • Read replica cluster
      • Asynchronous replication
    • Manage deployments
      • Start and stop processes
      • Eliminate an unresponsive node
      • Recover a node
      • Enable high availability
      • Edit configuration flags
      • Edit a universe
      • Delete a universe
      • Configure instance tags
      • Upgrade YugabyteDB
      • Migrate to Helm 3
    • Back up universes
      • Configure backup storage
      • Back up universe data
      • Restore universe data
      • Schedule data backups
    • Security
      • Security checklist
      • Configure ports
      • LDAP authentication
      • Authorization
      • Create a KMS configuration
      • Enable encryption at rest
      • Enable encryption in transit
      • Network security
    • Alerts and monitoring
      • Alerts
      • Live Queries dashboard
      • Slow Queries dashboard
    • Troubleshoot
      • Install and upgrade issues
      • Universe issues
    • Administer
      • Back up YugabyteDB Anywhere
      • Authenticate with LDAP
    • Upgrade
      • Upgrade Kubernetes installation
      • Upgrade using Replicated
  • YUGABYTEDB MANAGED
    • Overview
    • Quick start
      • Create a free cluster
      • Connect to the cluster
      • Explore distributed SQL
      • Build an application
        • Before you begin
        • Java
        • Go
        • Python
        • Node.js
        • C
        • C++
        • C#
        • Ruby
        • Rust
        • PHP
    • Deploy clusters
      • Planning a cluster
      • Create a free cluster
      • Create a standard cluster
      • VPC network
        • Overview
        • VPCs
        • Peering connections
        • Create a VPC Network
    • Secure clusters
      • IP allow lists
      • Database authorization
      • Add database users
      • Encryption in transit
      • Audit account activity
    • Connect to clusters
      • Cloud Shell
      • Client shell
      • Connect applications
    • Alerts and monitoring
      • Alerts
      • Performance metrics
      • Live queries
      • Slow YSQL queries
      • Cluster activity
    • Manage clusters
      • Scale and configure clusters
      • Backup and restore
      • Maintenance windows
      • Create extensions
    • Administration and billing
      • Manage account access
      • Manage billing
      • Cluster costs
    • Example applications
      • Connect a Spring application
      • Connect a YCQL Java application
      • Hasura Cloud
      • Deploy a GraphQL application
    • Security architecture
      • Security architecture
      • Shared responsibility model
    • Troubleshoot
    • YugabyteDB Managed FAQ
    • What's new
  • INTEGRATIONS
    • Apache Kafka
    • Apache Spark
    • Debezium
    • Django REST framework
    • Entity Framework
    • Flyway
    • GORM
    • Hasura
      • Application Development
      • Benchmarking
    • JanusGraph
    • KairosDB
    • Liquibase
    • Metabase
    • Presto
    • Prisma
    • Sequelize
    • Spring Framework
      • Spring Data YugabyteDB
      • Spring Data JPA
      • Spring Data Cassandra
    • SQLAlchemy
    • WSO2 Identity Server
    • YSQL Loader
    • YugabyteDB JDBC driver
  • REFERENCE
    • Architecture
      • Design goals
      • Key concepts
        • Universe
        • YB-TServer Service
        • YB-Master Service
      • Core functions
        • Universe creation
        • Table creation
        • Write IO path
        • Read IO path
        • High availability
      • Layered architecture
      • Query layer
        • Overview
      • DocDB transactions layer
        • Transactions overview
        • Transaction isolation levels
        • Explicit locking
        • Read Committed
        • Single-row transactions
        • Distributed transactions
        • Transactional IO path
      • DocDB sharding layer
        • Hash & range sharding
        • Tablet splitting
        • Colocated tables
      • DocDB replication layer
        • Replication
        • xCluster replication
        • Read replicas
        • Change data capture (CDC)
      • DocDB storage layer
        • Persistence
        • Performance
    • APIs
      • YSQL
        • The SQL language
          • SQL statements
            • ABORT
            • ALTER DATABASE
            • ALTER DEFAULT PRIVILEGES
            • ALTER DOMAIN
            • ALTER FOREIGN DATA WRAPPER
            • ALTER FOREIGN TABLE
            • ALTER GROUP
            • ALTER POLICY
            • ALTER ROLE
            • ALTER SEQUENCE
            • ALTER SERVER
            • ALTER TABLE
            • ALTER USER
            • ANALYZE
            • BEGIN
            • CALL
            • COMMENT
            • COMMIT
            • COPY
            • CREATE AGGREGATE
            • CREATE CAST
            • CREATE DATABASE
            • CREATE DOMAIN
            • CREATE EXTENSION
            • CREATE FOREIGN DATA WRAPPER
            • CREATE FOREIGN TABLE
            • CREATE FUNCTION
            • CREATE GROUP
            • CREATE INDEX
            • CREATE MATERIALIZED VIEW
            • CREATE OPERATOR
            • CREATE OPERATOR CLASS
            • CREATE POLICY
            • CREATE PROCEDURE
            • CREATE ROLE
            • CREATE RULE
            • CREATE SCHEMA
            • CREATE SEQUENCE
            • CREATE SERVER
            • CREATE TABLE
            • CREATE TABLE AS
            • CREATE TRIGGER
            • CREATE TYPE
            • CREATE USER
            • CREATE USER MAPPING
            • CREATE VIEW
            • DEALLOCATE
            • DELETE
            • DO
            • DROP AGGREGATE
            • DROP CAST
            • DROP DATABASE
            • DROP DOMAIN
            • DROP EXTENSION
            • DROP FOREIGN DATA WRAPPER
            • DROP FOREIGN TABLE
            • DROP FUNCTION
            • DROP GROUP
            • DROP MATERIALIZED VIEW
            • DROP OPERATOR
            • DROP OPERATOR CLASS
            • DROP OWNED
            • DROP POLICY
            • DROP PROCEDURE
            • DROP ROLE
            • DROP RULE
            • DROP SEQUENCE
            • DROP SERVER
            • DROP TABLE
            • DROP TRIGGER
            • DROP TYPE
            • DROP USER
            • END
            • EXECUTE
            • EXPLAIN
            • GRANT
            • IMPORT FOREIGN SCHEMA
            • INSERT
            • LOCK
            • PREPARE
            • REASSIGN OWNED
            • REFRESH MATERIALIZED VIEW
            • RELEASE SAVEPOINT
            • RESET
            • REVOKE
            • ROLLBACK
            • ROLLBACK TO SAVEPOINT
            • SAVEPOINT
            • SELECT
            • SET
            • SET CONSTRAINTS
            • SET ROLE
            • SET SESSION AUTHORIZATION
            • SET TRANSACTION
            • SHOW
            • SHOW TRANSACTION
            • TRUNCATE
            • UPDATE
            • VALUES
          • WITH clause
            • WITH clause—SQL syntax and semantics
            • recursive CTE
            • case study—traversing an employee hierarchy
            • traversing general graphs
              • graph representation
              • common code
              • undirected cyclic graph
              • directed cyclic graph
              • directed acyclic graph
              • rooted tree
              • Unique containing paths
              • Stress testing find_paths()
            • case study—Bacon Numbers from IMDb
              • Bacon numbers for synthetic data
              • Bacon numbers for IMDb data
        • Data types
          • Array
            • array[] constructor
            • Literals
              • Text typecasting and literals
              • Array of primitive values
              • Row
              • Array of rows
            • FOREACH loop (PL/pgSQL)
            • array of DOMAINs
            • Functions and operators
              • ANY and ALL
              • Array comparison
              • Array slice operator
              • Array concatenation
              • Array properties
              • array_agg(), unnest(), generate_subscripts()
              • array_fill()
              • array_position(), array_positions()
              • array_remove()
              • array_replace() / set value
              • array_to_string()
              • string_to_array()
          • Binary
          • Boolean
          • Character
          • Date and time
            • Conceptual background
            • Timezones and UTC offsets
              • Catalog views
              • Extended_timezone_names
                • Unrestricted full projection
                • Real timezones with DST
                • Real timezones no DST
                • Synthetic timezones no DST
              • Offset/timezone-sensitive operations
                • Timestamptz to/from timestamp conversion
                • Pure 'day' interval arithmetic
              • Four ways to specify offset
                • Name-resolution rules
                  • 1 case-insensitive resolution
                  • 2 ~names.abbrev never searched
                  • 3 'set timezone' string not resolved in ~abbrevs.abbrev
                  • 4 ~abbrevs.abbrev before ~names.name
                  • Helper functions
              • Syntax contexts for offset
              • Recommended practice
            • Typecasting between date-time and text-values
            • Semantics of the date-time data types
              • Date data type
              • Time data type
              • Plain timestamp and timestamptz
              • Interval data type
                • Interval representation
                  • Ad hoc examples
                  • Representation model
                • Interval value limits
                • Declaring intervals
                • Justify() and extract(epoch...)
                • Interval arithmetic
                  • Interval-interval comparison
                  • Interval-interval addition and subtraction
                  • Interval-number multiplication
                  • Moment-moment overloads of "-"
                  • Moment-interval overloads of "+" and "-"
                • Custom interval domains
                • Interval utility functions
            • Typecasting between date-time datatypes
            • Operators
              • Test comparison overloads
              • Test addition overloads
              • Test subtraction overloads
              • Test multiplication overloads
              • Test division overloads
            • General-purpose functions
              • Creating date-time values
              • Manipulating date-time values
              • Current date-time moment
              • Delaying execution
              • Miscellaneous
                • Function age()
                • Function extract() | date_part()
                • Implementations that model the overlaps operator
            • Formatting functions
            • Case study—SQL stopwatch
            • Download & install the date-time utilities
            • ToC
          • JSON
            • JSON literals
            • Primitive and compound data types
            • Code example conventions
            • Indexes and check constraints
            • Functions & operators
              • ::jsonb, ::json, ::text (typecast)
              • ->, ->>, #>, #>> (JSON subvalues)
              • - and #- (remove)
              • || (concatenation)
              • = (equality)
              • @> and <@ (containment)
              • ? and ?| and ?& (key or value existence)
              • array_to_json()
              • jsonb_agg()
              • jsonb_array_elements()
              • jsonb_array_elements_text()
              • jsonb_array_length()
              • jsonb_build_object()
              • jsonb_build_array()
              • jsonb_each()
              • jsonb_each_text()
              • jsonb_extract_path()
              • jsonb_extract_path_text() and json_extract_path_text()
              • jsonb_object()
              • jsonb_object_agg()
              • jsonb_object_keys()
              • jsonb_populate_record()
              • jsonb_populate_recordset()
              • jsonb_pretty()
              • jsonb_set() and jsonb_insert()
              • jsonb_strip_nulls()
              • jsonb_to_record()
              • jsonb_to_recordset()
              • jsonb_typeof()
              • row_to_json()
              • to_jsonb()
          • Money
          • Numeric
          • Range
          • Serial
          • UUID
        • Functions and operators
          • Aggregate functions
            • Informal functionality overview
            • Invocation syntax and semantics
            • grouping sets, rollup, cube
            • Per function signature and purpose
              • avg(), count(), max(), min(), sum()
              • array_agg(), string_agg(), jsonb_agg(), jsonb_object_agg()
              • bit_and(), bit_or(), bool_and(), bool_or()
              • variance(), var_pop(), var_samp(), stddev(), stddev_pop(), stddev_samp()
              • linear regression
                • covar_pop(), covar_samp(), corr()
                • regr_%()
              • mode(), percentile_disc(), percentile_cont()
              • rank(), dense_rank(), percent_rank(), cume_dist()
            • case study—percentile_cont() and the "68–95–99.7" rule
            • case study—linear regression on COVID data
              • Download the COVIDcast data
              • Ingest the COVIDcast data
                • Inspect the COVIDcast data
                • Copy the .csv files to staging tables
                • Check staged data conforms to the rules
                • Join the staged data into a single table
                • SQL scripts
                  • Create cr_staging_tables()
                  • Create cr_copy_from_scripts()
                  • Create assert_assumptions_ok()
                  • Create xform_to_covidcast_fb_survey_results()
                  • ingest-the-data.sql
              • Analyze the COVIDcast data
                • symptoms vs mask-wearing by day
                • Data for scatter-plot for 21-Oct-2020
                • Scatter-plot for 21-Oct-2020
                • SQL scripts
                  • analysis-queries.sql
                  • synthetic-data.sql
          • currval()
          • lastval()
          • nextval()
          • Window functions
            • Informal functionality overview
            • Invocation syntax and semantics
            • Per function signature and purpose
              • row_number(), rank() and dense_rank()
              • percent_rank(), cume_dist() and ntile()
              • first_value(), nth_value(), last_value()
              • lag(), lead()
              • Tables for the code examples
                • table t1
                • table t2
                • table t3
                • table t4
            • case study—analyzing a normal distribution
              • Bucket allocation scheme
              • do_clean_start.sql
              • cr_show_t4.sql
              • cr_dp_views.sql
              • cr_int_views.sql
              • cr_pr_cd_equality_report.sql
              • cr_bucket_using_width_bucket.sql
              • cr_bucket_dedicated_code.sql
              • do_assert_bucket_ok
              • cr_histogram.sql
              • cr_do_ntile.sql
              • cr_do_percent_rank.sql
              • cr_do_cume_dist.sql
              • do_populate_results.sql
              • do_report_results.sql
              • do_compare_dp_results.sql
              • do_demo.sql
              • Reports
                • Histogram report
                • dp-results
                • compare-dp-results
                • int-results
          • yb_hash_code()
        • Keywords
        • Reserved names
      • YCQL
        • ALTER KEYSPACE
        • ALTER ROLE
        • ALTER TABLE
        • CREATE INDEX
        • CREATE KEYSPACE
        • CREATE ROLE
        • CREATE TABLE
        • CREATE TYPE
        • DROP INDEX
        • DROP KEYSPACE
        • DROP ROLE
        • DROP TABLE
        • DROP TYPE
        • GRANT PERMISSION
        • GRANT ROLE
        • REVOKE PERMISSION
        • REVOKE ROLE
        • USE
        • INSERT
        • SELECT
        • EXPLAIN
        • UPDATE
        • DELETE
        • TRANSACTION
        • TRUNCATE
        • Simple expressions
        • Subscripted expressions
        • Function call
        • Operators
        • BLOB
        • BOOLEAN
        • Collection
        • FROZEN
        • INET
        • Integer and counter
        • Non-integer
        • TEXT
        • DATE, TIME, and TIMESTAMP
        • UUID and TIMEUUID
        • JSONB
        • Date and time
        • BATCH
    • CLIs
      • yb-ctl
      • yb-docker-ctl
      • ysqlsh
      • ycqlsh
      • yb-admin
      • yb-ts-cli
      • ysql_dump
      • ysql_dumpall
    • Configuration
      • yb-tserver
      • yb-master
      • yugabyted
      • Default ports
    • Drivers and ORMs
      • JDBC Drivers
      • C# Drivers
      • Go Drivers
      • Python Drivers
      • Client drivers for YSQL
      • Client drivers for YCQL
    • Connectors
      • Kafka Connect YugabyteDB
    • Third party tools
      • pgAdmin
      • Apache Superset
      • Arctype
      • DBeaver
      • TablePlus
      • DbSchema
      • SQL Workbench/J
      • Cassandra Workbench
    • Sample datasets
      • Chinook
      • Northwind
      • PgExercises
      • SportsDB
      • Retail Analytics
  • RELEASES
    • Releases overview
      • v2.13 series (preview)
      • v2.12 series (stable)
      • v2.11 series
      • v2.9 series
      • v2.8 series
      • v2.7 series
      • v2.6 series
      • v2.5 series
      • v2.4 series
      • v2.3 series
      • v2.2 series
      • v2.1 series
      • v2.0 series
      • v1.3 series
      • v1.2 series
    • Release versioning
  • FAQ
    • Comparisons
      • Amazon Aurora
      • Google Cloud Spanner
      • CockroachDB
      • TiDB
      • Vitess
      • MongoDB
      • FoundationDB
      • Amazon DynamoDB
      • Azure Cosmos DB
      • Apache Cassandra
      • PostgreSQL
      • Redis in-memory store
      • Apache HBase
    • General FAQ
    • Operations FAQ
    • API compatibility FAQ
    • YugabyteDB Anywhere FAQ
  • MISC
    • YEDIS
      • Quick start
      • Develop
        • Build an application
        • C#
        • C++
        • Go
        • Java
        • NodeJS
        • Python
      • API reference
        • APPEND
        • AUTH
        • CONFIG
        • CREATEDB
        • DELETEDB
        • LISTDB
        • SELECT
        • DEL
        • ECHO
        • EXISTS
        • EXPIRE
        • EXPIREAT
        • FLUSHALL
        • FLUSHDB
        • GET
        • GETRANGE
        • GETSET
        • HDEL
        • HEXISTS
        • HGET
        • HGETALL
        • HINCRBY
        • HKEYS
        • HLEN
        • HMGET
        • HMSET
        • HSET
        • HSTRLEN
        • HVALS
        • INCR
        • INCRBY
        • KEYS
        • MONITOR
        • PEXPIRE
        • PEXPIREAT
        • PTTL
        • ROLE
        • SADD
        • SCARD
        • RENAME
        • SET
        • SETEX
        • PSETEX
        • SETRANGE
        • SISMEMBER
        • SMEMBERS
        • SREM
        • STRLEN
        • ZRANGE
        • TSADD
        • TSCARD
        • TSGET
        • TSLASTN
        • TSRANGEBYTIME
        • TSREM
        • TSREVRANGEBYTIME
        • TTL
        • ZADD
        • ZCARD
        • ZRANGEBYSCORE
        • ZREM
        • ZREVRANGE
        • ZSCORE
        • PUBSUB
        • PUBLISH
        • SUBSCRIBE
        • UNSUBSCRIBE
        • PSUBSCRIBE
        • PUNSUBSCRIBE
    • Legal
      • Third party software
The internal representation of an interval value [YSQL]
> APIs > YSQL > Data types > Date and time > Semantics of the date-time data types > Interval data type >

How does YSQL represent an interval value?

Report a doc issue Suggest new content

Download and install the date-time utilities code.

The code on this page and on its child, Modeling the internal representation and comparing the model with the actual implementation, depends on the code presented in the section User-defined interval utility functions. This is included in the larger code kit that includes all of the reusable code that the overall date-time section describes and uses.

The PostgreSQL documentation, under the table Interval Input, says this:

Internally, interval values are stored as months, days, and seconds. This is done because the number of days in a month varies, and a day can have 23 or 25 hours if a Daylight Savings Time adjustment is involved. The months and days fields are integers while the seconds field can store fractions. Because intervals are usually created from constant strings or timestamp subtraction, this storage method works well in most cases, but can cause unexpected results.

Inspection of the C code of the implementation shows that the mm and dd fields of the [mm, dd, ss] internal implementation tuple are four-byte integers. The ss field is an eight-byte integer that records the value in microseconds.

The reference to Daylight Savings Time is a nod to the critical distinction between clock-time-semantics and calendar-time-semantics. Notice the use of "unexpected". It is better to say that your ability confidently to predict the outcome of interval arithmetic rests on a relatively elaborate mental model. This model has two complementary parts:

  • How the values of the three fields of the [mm, dd, ss] representation of an interval value are computed when an interval value is created. The present "How does YSQL represent an interval value?" section addresses this.

  • The different semantics of these three fields when an interval value is added or subtracted to/from a timestamptz value, a timestamp value, or a time value or when an interval value is created by subtracting one moment (typically a plain timestamp value or a timestamptz_value) from another. This is addressed in the section Interval arithmetic.

As long as you have a robust mental model, then your results will not be unexpected. This section explains the mental model for interval value creation. It enables you to predict what values for months, days, and seconds will be represented internally when you specify an interval value using values for years, months, days, hours, minutes, and seconds. And it enables you to predict what values for years, months, days, hours, minutes, and seconds you will read back from an interval value whose months, days, and seconds values you have managed to predict.

The value recorded by each of the three fields of the representation can be arbitrarily large with respect to the conventions that say, for example, that 25 hours is 1 day and 1 hour. For example, this tuple is allowed: 99 months 700 days 926351.522816 seconds. (Of course, the physical internal representation does impose some limits. See the section interval value limits.)

Note: The internal sixteen-byte format of the internal [mm, dd, ss] representation of an interval value determines the theoretical upper limits on the values of each of the three fields. Other factors determine the actual limits. This is explained in the section interval value limits.

Ad hoc examples

There are no built-in functions or operators that let you display the months, days, and seconds "as is" from the internal representation. Rather, you can display only canonically derived values for years, months, days, hours, minutes, and seconds. The rule for extracting these values from the internal representation is simple and intuitive. It is presented as executable PL/pgSQL in the implementation of the function parameterization (interval_mm_dd_ss_t) in the section User-defined interval utility functions. Briefly, the internal integral months value is displayed as integral years and integral months by taking one year to be 12 months; the internal integral days value is displayed "as is"; and the real number internal seconds is displayed as integral hours, integral minutes, and real number seconds by taking one hour to be 60 minutes and one minute to be 60 seconds.

The section Ad hoc examples of defining interval values provides six examples that give a flavor of the complexity of the rules.

Modeling the internal representation and comparing the model with the actual implementation

The best way to express a statement of the rules that are consistent with the outcomes of the six Ad hoc examples of defining interval values, and any number of other examples that you might try, is to implement an executable simulation and to compare its outputs with the outputs that the actual PostgreSQL, and therefore YSQL, implementations produce.

Note: If you follow the recommendations made below, you can simply skip attempting to understand these tricky rules without sacrificing any useful functionality.

The section Modeling the internal representation and comparing the model with the actual implementation presents this. Here is the algorithm, copied from the body of function interval_mm_dd_ss (interval_parameterization_t):

-- The input values are "p.yy", "p.mm", "p.dd", "p.hh", "p.mi", and "p.ss" — i.e. the
-- conventional parameterization of an "interval" value used by the "::interval" typecast
-- and the "make_interval()" approaches.

-- The output values are "mm_out", "dd_out", and "ss_out" — i.e. the fields of the internal
-- representation tuple.

-- "mm_per_yy", "dd_per_mm", "ss_per_dd", "ss_per_hh", and "ss_per_mi" are constants
-- with the meanings that the mnemonics suggest: the number of months in a year,
-- and so on.
mm_trunc                constant int              not null := trunc(p.mm);
mm_remainder            constant double precision not null := p.mm - mm_trunc::double precision;

-- This is a quirk.
mm_out                  constant int              not null := trunc(p.yy*mm_per_yy) + mm_trunc;

dd_real_from_mm         constant double precision not null := mm_remainder*dd_per_mm;

dd_int_from_mm          constant int              not null := trunc(dd_real_from_mm);
dd_remainder_from_mm    constant double precision not null := dd_real_from_mm - dd_int_from_mm::double precision;

dd_int_from_user        constant int              not null := trunc(p.dd);
dd_remainder_from_user  constant double precision not null := p.dd - dd_int_from_user::double precision;

dd_out                  constant int              not null := dd_int_from_mm + dd_int_from_user;

d_remainder             constant double precision not null := dd_remainder_from_mm + dd_remainder_from_user;

ss_out                  constant double precision not null := d_remainder*ss_per_dd +
                                                              p.hh*ss_per_hh +
                                                              p.mi*ss_per_mi +
                                                              p.ss;

The algorithm is too hard to remember and produces unhelpful outcomes.

Yugabyte staff members have carefully considered the rules that this algorithm expresses. They have the property that when non-integral values are used in the ::interval typecast approach, even a literal that specifies, for example, only months can result in an internal [mm, dd, ss] tuple where each of the fields is non-zero. Try this:

select interval_mm_dd_ss('11.674523 months '::interval)::text;

(The function interval_mm_dd_ss (interval) is defined in the section User-defined interval utility functions. This is the result:

 (11,20,20363.616)

The section Interval arithmetic explains that the semantics is critically different for each of the internal representation's fields. It recommends that you use only interval values where just one of the three fields is non-zero. The section Custom domain types for specializing the native interval functionality shows how to impose this discipline programmatically.

Possible upcoming implementation change

Heads up.

There has been some discussion on the pgsql-general and pgsql-hackers mail lists about the algorithm whose implementation that the function interval() documents. As a result, a patch has been developed for a future version of the PostgreSQL system that makes some subtle changes to the "spill-down" behavior in response to real number input values for years, months, days, hours, and minutes when you use the ::interval typecast approach to construct an interval value. When YugabyteDB adopts this patch, the implementation of the function interval_mm_dd_ss (interval_parameterization_t) will be changed accordingly.

If you follow Yugabyte's recommendation to construct interval values using only integral values for years, months, days, hours, and minutes (or, equivalently, always to use the make_interval() SQL built-in function rather than the ::interval typecast approach), then your application code will not see a behavior change when you move to a version of YugabyteDB that implements this patch. As mentioned above, the section Custom domain types for specializing the native interval functionality shows how to impose this discipline programmatically.

Ask our community
  • Slack
  • Github
  • Forum
  • StackOverflow
Yugabyte
Contact us

Copyright © 2017-2022 Yugabyte, Inc. All rights reserved.