LogoLogo
release-1.2.0
release-1.2.0
  • Introduction
  • Basics
    • Concepts
      • Pinot storage model
      • Architecture
      • Components
        • Cluster
          • Tenant
          • Server
          • Controller
          • Broker
          • Minion
        • Table
          • Segment
            • Deep Store
            • Segment threshold
            • Segment retention
          • Schema
          • Time boundary
        • Pinot Data Explorer
    • Getting Started
      • Running Pinot locally
      • Running Pinot in Docker
      • Quick Start Examples
      • Running in Kubernetes
      • Running on public clouds
        • Running on Azure
        • Running on GCP
        • Running on AWS
      • Create and update a table configuration
      • Batch import example
      • Stream ingestion example
      • HDFS as Deep Storage
      • Troubleshooting Pinot
      • Frequently Asked Questions (FAQs)
        • General
        • Pinot On Kubernetes FAQ
        • Ingestion FAQ
        • Query FAQ
        • Operations FAQ
    • Import Data
      • From Query Console
      • Batch Ingestion
        • Spark
        • Flink
        • Hadoop
        • Backfill Data
        • Dimension table
      • Stream ingestion
        • Ingest streaming data from Apache Kafka
        • Ingest streaming data from Amazon Kinesis
        • Ingest streaming data from Apache Pulsar
        • Configure indexes
      • Stream ingestion with Upsert
      • Segment compaction on upserts
      • Stream ingestion with Dedup
      • Stream ingestion with CLP
      • File Systems
        • Amazon S3
        • Azure Data Lake Storage
        • HDFS
        • Google Cloud Storage
      • Input formats
        • Complex Type (Array, Map) Handling
        • Ingest records with dynamic schemas
      • Reload a table segment
      • Upload a table segment
    • Indexing
      • Bloom filter
      • Dictionary index
      • Forward index
      • FST index
      • Geospatial
      • Inverted index
      • JSON index
      • Native text index
      • Range index
      • Star-tree index
      • Text search support
      • Timestamp index
    • Release notes
      • 1.1.0
      • 1.0.0
      • 0.12.1
      • 0.12.0
      • 0.11.0
      • 0.10.0
      • 0.9.3
      • 0.9.2
      • 0.9.1
      • 0.9.0
      • 0.8.0
      • 0.7.1
      • 0.6.0
      • 0.5.0
      • 0.4.0
      • 0.3.0
      • 0.2.0
      • 0.1.0
    • Recipes
      • Connect to Streamlit
      • Connect to Dash
      • Visualize data with Redash
      • GitHub Events Stream
  • For Users
    • Query
      • Querying Pinot
      • Query Syntax
        • Aggregation Functions
        • Cardinality Estimation
        • Explain Plan (Single-Stage)
        • Explain Plan (Multi-Stage)
        • Filtering with IdSet
        • GapFill Function For Time-Series Dataset
        • Grouping Algorithm
        • JOINs
        • Lookup UDF Join
        • Querying JSON data
        • Transformation Functions
        • Window aggregate
        • Funnel Analysis
      • Query Options
      • Multi stage query
        • Operator Types
          • Aggregate
          • Filter
          • Join
          • Intersect
          • Leaf
          • Literal
          • Mailbox receive
          • Mailbox send
          • Minus
          • Sort or limit
          • Transform
          • Union
          • Window
        • Understanding Stages
        • Explain
        • Stats
      • User-Defined Functions (UDFs)
    • APIs
      • Broker Query API
        • Query Response Format
      • Controller Admin API
      • Controller API Reference
    • External Clients
      • JDBC
      • Java
      • Python
      • Golang
    • Tutorials
      • Use OSS as Deep Storage for Pinot
      • Ingest Parquet Files from S3 Using Spark
      • Creating Pinot Segments
      • Use S3 as Deep Storage for Pinot
      • Use S3 and Pinot in Docker
      • Batch Data Ingestion In Practice
      • Schema Evolution
  • For Developers
    • Basics
      • Extending Pinot
        • Writing Custom Aggregation Function
        • Segment Fetchers
      • Contribution Guidelines
      • Code Setup
      • Code Modules and Organization
      • Dependency Management
      • Update documentation
    • Advanced
      • Data Ingestion Overview
      • Ingestion Aggregations
      • Ingestion Transformations
      • Null value support
      • Use the multi-stage query engine (v2)
      • Troubleshoot issues with the multi-stage query engine (v2)
      • Advanced Pinot Setup
    • Plugins
      • Write Custom Plugins
        • Input Format Plugin
        • Filesystem Plugin
        • Batch Segment Fetcher Plugin
        • Stream Ingestion Plugin
    • Design Documents
      • Segment Writer API
  • For Operators
    • Deployment and Monitoring
      • Set up cluster
      • Server Startup Status Checkers
      • Set up table
      • Set up ingestion
      • Decoupling Controller from the Data Path
      • Segment Assignment
      • Instance Assignment
      • Rebalance
        • Rebalance Servers
        • Rebalance Brokers
        • Rebalance Tenant
      • Separating data storage by age
        • Using multiple tenants
        • Using multiple directories
      • Pinot managed Offline flows
      • Minion merge rollup task
      • Consistent Push and Rollback
      • Access Control
      • Monitoring
      • Tuning
        • Real-time
        • Routing
        • Query Routing using Adaptive Server Selection
        • Query Scheduling
      • Upgrading Pinot with confidence
      • Managing Logs
      • OOM Protection Using Automatic Query Killing
    • Command-Line Interface (CLI)
    • Configuration Recommendation Engine
    • Tutorials
      • Authentication
        • Basic auth access control
        • ZkBasicAuthAccessControl
      • Configuring TLS/SSL
      • Build Docker Images
      • Running Pinot in Production
      • Kubernetes Deployment
      • Amazon EKS (Kafka)
      • Amazon MSK (Kafka)
      • Monitor Pinot using Prometheus and Grafana
      • Performance Optimization Configurations
  • Configuration Reference
    • Cluster
    • Controller
    • Broker
    • Server
    • Table
    • Ingestion
    • Schema
    • Ingestion Job Spec
    • Monitoring Metrics
    • Functions
      • ABS
      • ADD
      • ago
      • EXPR_MIN / EXPR_MAX
      • arrayConcatDouble
      • arrayConcatFloat
      • arrayConcatInt
      • arrayConcatLong
      • arrayConcatString
      • arrayContainsInt
      • arrayContainsString
      • arrayDistinctInt
      • arrayDistinctString
      • arrayIndexOfInt
      • arrayIndexOfString
      • ARRAYLENGTH
      • arrayRemoveInt
      • arrayRemoveString
      • arrayReverseInt
      • arrayReverseString
      • arraySliceInt
      • arraySliceString
      • arraySortInt
      • arraySortString
      • arrayUnionInt
      • arrayUnionString
      • AVGMV
      • Base64
      • caseWhen
      • ceil
      • CHR
      • codepoint
      • concat
      • count
      • COUNTMV
      • COVAR_POP
      • COVAR_SAMP
      • day
      • dayOfWeek
      • dayOfYear
      • DISTINCT
      • DISTINCTAVG
      • DISTINCTAVGMV
      • DISTINCTCOUNT
      • DISTINCTCOUNTBITMAP
      • DISTINCTCOUNTHLLMV
      • DISTINCTCOUNTHLL
      • DISTINCTCOUNTBITMAPMV
      • DISTINCTCOUNTMV
      • DISTINCTCOUNTRAWHLL
      • DISTINCTCOUNTRAWHLLMV
      • DISTINCTCOUNTRAWTHETASKETCH
      • DISTINCTCOUNTTHETASKETCH
      • DISTINCTSUM
      • DISTINCTSUMMV
      • DIV
      • DATETIMECONVERT
      • DATETRUNC
      • exp
      • FIRSTWITHTIME
      • FLOOR
      • FrequentLongsSketch
      • FrequentStringsSketch
      • FromDateTime
      • FromEpoch
      • FromEpochBucket
      • FUNNELCOUNT
      • FunnelCompleteCount
      • FunnelMaxStep
      • FunnelMatchStep
      • Histogram
      • hour
      • isSubnetOf
      • JSONFORMAT
      • JSONPATH
      • JSONPATHARRAY
      • JSONPATHARRAYDEFAULTEMPTY
      • JSONPATHDOUBLE
      • JSONPATHLONG
      • JSONPATHSTRING
      • jsonextractkey
      • jsonextractscalar
      • LAG
      • LASTWITHTIME
      • LEAD
      • length
      • ln
      • lower
      • lpad
      • ltrim
      • max
      • MAXMV
      • MD5
      • millisecond
      • min
      • minmaxrange
      • MINMAXRANGEMV
      • MINMV
      • minute
      • MOD
      • mode
      • month
      • mult
      • now
      • percentile
      • percentileest
      • percentileestmv
      • percentilemv
      • percentiletdigest
      • percentiletdigestmv
      • percentilekll
      • percentilerawkll
      • percentilekllmv
      • percentilerawkllmv
      • quarter
      • regexpExtract
      • regexpReplace
      • remove
      • replace
      • reverse
      • round
      • ROW_NUMBER
      • rpad
      • rtrim
      • second
      • SEGMENTPARTITIONEDDISTINCTCOUNT
      • sha
      • sha256
      • sha512
      • sqrt
      • startswith
      • ST_AsBinary
      • ST_AsText
      • ST_Contains
      • ST_Distance
      • ST_GeogFromText
      • ST_GeogFromWKB
      • ST_GeometryType
      • ST_GeomFromText
      • ST_GeomFromWKB
      • STPOINT
      • ST_Polygon
      • strpos
      • ST_Union
      • SUB
      • substr
      • sum
      • summv
      • TIMECONVERT
      • timezoneHour
      • timezoneMinute
      • ToDateTime
      • ToEpoch
      • ToEpochBucket
      • ToEpochRounded
      • TOJSONMAPSTR
      • toGeometry
      • toSphericalGeography
      • trim
      • upper
      • Url
      • UTF8
      • VALUEIN
      • week
      • year
      • yearOfWeek
      • Extract
    • Plugin Reference
      • Stream Ingestion Connectors
      • VAR_POP
      • VAR_SAMP
      • STDDEV_POP
      • STDDEV_SAMP
    • Dynamic Environment
  • Reference
    • Single-stage query engine (v1)
    • Multi-stage query engine (v2)
    • Troubleshooting
      • Troubleshoot issues with the multi-stage query engine (v2)
      • Troubleshoot issues with ZooKeeper znodes
  • RESOURCES
    • Community
    • Team
    • Blogs
    • Presentations
    • Videos
  • Integrations
    • Tableau
    • Trino
    • ThirdEye
    • Superset
    • Presto
    • Spark-Pinot Connector
  • Contributing
    • Contribute Pinot documentation
    • Style guide
Powered by GitBook
On this page
  • Signature
  • Arguments
  • Example

Was this helpful?

Edit on GitHub
Export as PDF
  1. Configuration Reference
  2. Functions

LEAD

This section contains reference documentation for the LEAD function.

Returns the value from a following row in the same result set, based on a specified physical offset. It can be used to compare values in the current row with values in a subsequent row.

Signature

LEAD(any expression [, bigint offset [, any default]])

Arguments

  • expression: The column or calculation from which the value is to be returned.

  • offset: The number of rows before the current row from which to retrieve the value. The default is 1 if not specified.

  • default: The value to return if the offset goes beyond the scope of the window. If not specified, NULL is returned.

Example

Forecast next day's sales based on current data.

Anticipate the next payment amount for budget planning.

Identify potential increases in expenses or revenue.

Forecast next day's sales based on current data This example shows how to use the LEAD function to anticipate sales for the next day.

SELECT
    sales_date,
    sales_amount,
    LEAD(sales_amount, 1) OVER (ORDER BY sales_date) AS next_day_sales
FROM
    daily_sales;

Output:

sales_date
sales_amount
next_day_sales

2023-02-14

200

180

2023-02-15

180

220

2023-02-16

220

NULL

Anticipate the next payment amount for budget planning This query retrieves the next payment amount for each transaction to assist in financial forecasting and budgeting.

SELECT
    transaction_id,
    payment_date,
    amount,
    LEAD(amount, 1) OVER (ORDER BY payment_date) AS next_payment_amount
FROM
    payments;

Output:

transaction_id
payment_date
amount
next_payment_amount

416

2023-02-14 21:21:59.996577

2.99

4.99

516

2023-02-14 21:23:39.996577

4.99

4.99

239

2023-02-14 21:29:00.996577

4.99

6.99

Identify potential increases in expenses or revenue Utilize the LEAD function to examine monthly data and predict potential increases or trends in expenses or revenue for future planning.

SELECT
    month,
    year,
    expenses,
    LEAD(expenses, 1) OVER (ORDER BY year, month) AS next_month_expenses
FROM
    financials;

Output:

month
year
expenses
next_month_expenses

1

2023

1000

1100

2

2023

1100

1200

3

2023

1200

NULL

Use with CTE:

WITH tmp AS (
  select count(*) as num_trips,
    DaysSinceEpoch
  from airlineStats
  GROUP BY DaysSinceEpoch
)

SELECT DaysSinceEpoch,
  num_trips,
  LEAD(num_trips, 2) OVER (
    ORDER BY DaysSinceEpoch
  ) AS previous_num_trips,
  num_trips - LEAD(num_trips, 2) OVER (
    ORDER BY DaysSinceEpoch
  ) AS difference
FROM tmp;
PreviousLASTWITHTIMENextlength

Was this helpful?