LogoLogo
latest
latest
  • 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
    • 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
      • Vector index
    • Release notes
      • 1.3.0
      • 1.2.0
      • 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
        • Explain Plan (Single-Stage)
        • Filtering with IdSet
        • GapFill Function For Time-Series Dataset
        • Grouping Algorithm
        • JOINs
        • Lookup UDF Join
      • Query Options
      • Query Quotas
      • Query using Cursors
      • Multi-stage query
        • Understanding Stages
        • Stats
        • Optimizing joins
        • Join strategies
          • Random + broadcast join strategy
          • Query time partition join strategy
          • Colocated join strategy
          • Lookup join strategy
        • Hints
        • Operator Types
          • Aggregate
          • Filter
          • Join
          • Intersect
          • Leaf
          • Literal
          • Mailbox receive
          • Mailbox send
          • Minus
          • Sort or limit
          • Transform
          • Union
          • Window
        • Stage-Level Spooling
      • Explain plan
    • APIs
      • Broker Query API
        • Query Response Format
      • Broker GRPC API
      • 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)
      • 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
          • Examples and Scenarios
        • 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
        • Tuning Default MMAP Advice
        • Real-time
        • Routing
        • Query Routing using Adaptive Server Selection
        • Query Scheduling
      • Upgrading Pinot with confidence
      • Managing Logs
      • OOM Protection Using Automatic Query Killing
      • Pause ingestion based on resource utilization
    • 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
      • Segment Operations Throttling
      • Reload a table segment
  • Configuration Reference
    • Cluster
    • Controller
    • Broker
    • Server
    • Table
    • Ingestion
    • Schema
    • Database
    • Ingestion Job Spec
    • Monitoring Metrics
    • Plugin Reference
      • Stream Ingestion Connectors
      • VAR_POP
      • VAR_SAMP
      • STDDEV_POP
      • STDDEV_SAMP
    • Dynamic Environment
  • Manage Data
    • Import Data
      • SQL Insert Into From Files
      • Upload Pinot segment Using CommandLine
      • 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 CLP
      • Upsert and Dedup
        • Stream ingestion with Upsert
        • Segment compaction on upserts
        • Stream ingestion with Dedup
      • Supported Data Formats
      • File Systems
        • Amazon S3
        • Azure Data Lake Storage
        • HDFS
        • Google Cloud Storage
      • Complex Type (Array, Map) Handling
        • Complex Type Examples (Unnest)
      • Ingest records with dynamic schemas
  • Functions
    • Aggregation Functions
    • Transformation Functions
    • Array Functions
    • Binary Functions
    • DateTime Functions
    • Funnel Analysis Functions
    • GeoSpatial Functions
    • Hash Functions
    • JSON Functions
    • Math Functions
    • String Functions
    • User-Defined Functions (UDFs)
    • URL Functions
    • Unique Count and cardinality Estimation Functions
  • Window Functions
  • Function List
    • ABS
    • ADD
    • ago
    • EXPR_MIN / EXPR_MAX
    • ARRAY_AGG
    • 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
    • DISTINCTCOUNT
    • DISTINCTCOUNTMV
    • DISTINCT_COUNT_OFF_HEAP
    • SEGMENTPARTITIONEDDISTINCTCOUNT
    • DISTINCTCOUNTBITMAP
    • DISTINCTCOUNTBITMAPMV
    • DISTINCTCOUNTHLL
    • DISTINCTCOUNTHLLMV
    • DISTINCTCOUNTRAWHLL
    • DISTINCTCOUNTRAWHLLMV
    • DISTINCTCOUNTSMARTHLL
    • DISTINCTCOUNTHLLPLUS
    • DISTINCTCOUNTULL
    • DISTINCTCOUNTTHETASKETCH
    • DISTINCTCOUNTRAWTHETASKETCH
    • DISTINCTSUM
    • DISTINCTSUMMV
    • DISTINCTAVG
    • DISTINCTAVGMV
    • DIV
    • DATETIMECONVERT
    • DATETRUNC
    • exp
    • FIRSTWITHTIME
    • FLOOR
    • FrequentLongsSketch
    • FrequentStringsSketch
    • FromDateTime
    • FromEpoch
    • FromEpochBucket
    • FUNNELCOUNT
    • FunnelCompleteCount
    • FunnelMaxStep
    • FunnelMatchStep
    • GridDistance
    • 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
    • roundDecimal
    • ROW_NUMBER
    • rpad
    • rtrim
    • second
    • 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
    • Extract
    • yearOfWeek
    • FIRST_VALUE
    • LAST_VALUE
    • ST_GeomFromGeoJSON
    • ST_GeogFromGeoJSON
    • ST_AsGeoJSON
  • 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
      • Realtime Ingestion Stopped
  • 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

Was this helpful?

Edit on GitHub
Export as PDF

Last updated 1 year ago

Was this helpful?

Python DB-API and SQLAlchemy dialect for Pinot

Applications can use this python client library to query Apache Pinot.

Pypi Repo:

Source Code Repo:

Installation

Note:

  • pinotdb version >= 0.3.2 uses the Pinot SQL API (added in Pinot >= 0.3.0) and drops support for PQL API. So this client requires Pinot server version >= 0.3.0 in order to access Pinot.

  • pinotdb version in 0.2.x uses the Pinot PQL API, which works with pinot version <= 0.3.0, but may miss some new SQL query features added in newer Pinot version.

Usage

Using the DB API to query Pinot Broker directly:

Using SQLAlchemy:

The db engine connection string is formated like this: pinot://:?controller=://:/

Examples with Pinot Quickstart

Clone the Pinot DB repository

Pinot Batch Quickstart

Run below command to start Pinot Batch Quickstart in docker and expose Pinot controller port 9000 and Pinot broker port 8000.

Once pinot batch quickstart is up, you can run the sample code snippet to query Pinot:

Sample Output:

Using parameters:

Pinot Hybrid Quickstart

Run the command below to start Pinot Hybrid Quickstart in docker and expose Pinot controller port 9000 and Pinot broker port 8000.

Below is an example to query against Pinot Quickstart Hybrid:

from sqlalchemy import *
from sqlalchemy.engine import create_engine
from sqlalchemy.schema import *

engine = create_engine('pinot://localhost:8099/query/sql?controller=http://localhost:9000/')  # uses HTTP by default :(
# engine = create_engine('pinot+http://localhost:8099/query/sql?controller=http://localhost:9000/')
# engine = create_engine('pinot+https://localhost:8099/query/sql?controller=http://localhost:9000/')

places = Table('places', MetaData(bind=engine), autoload=True)
print(select([func.count('*')], from_obj=places).scalar())
git clone git@github.com:python-pinot-dbapi/pinot-dbapi.git
cd pinot-dbapi
docker run \
  --name pinot-quickstart \
  -p 2123:2123 \
  -p 9000:9000 \
  -p 8000:8000 \
  apachepinot/pinot:latest QuickStart -type batch
python3 examples/pinot-quickstart-batch.py
Sending SQL to Pinot: SELECT * FROM baseballStats LIMIT 5
[0, 11, 0, 0, 0, 0, 0, 0, 0, 0, 'NL', 11, 11, 'aardsda01', 'David Allan', 1, 0, 0, 0, 0, 0, 0, 'SFN', 0, 2004]
[2, 45, 0, 0, 0, 0, 0, 0, 0, 0, 'NL', 45, 43, 'aardsda01', 'David Allan', 1, 0, 0, 0, 1, 0, 0, 'CHN', 0, 2006]
[0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 'AL', 25, 2, 'aardsda01', 'David Allan', 1, 0, 0, 0, 0, 0, 0, 'CHA', 0, 2007]
[1, 5, 0, 0, 0, 0, 0, 0, 0, 0, 'AL', 47, 5, 'aardsda01', 'David Allan', 1, 0, 0, 0, 0, 0, 1, 'BOS', 0, 2008]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 'AL', 73, 3, 'aardsda01', 'David Allan', 1, 0, 0, 0, 0, 0, 0, 'SEA', 0, 2009]

Sending SQL to Pinot: SELECT playerName, sum(runs) FROM baseballStats WHERE yearID>=2000 GROUP BY playerName LIMIT 5
['Scott Michael', 26.0]
['Justin Morgan', 0.0]
['Jason Andre', 0.0]
['Jeffrey Ellis', 0.0]
['Maximiliano R.', 16.0]

Sending SQL to Pinot: SELECT playerName,sum(runs) AS sum_runs FROM baseballStats WHERE yearID>=2000 GROUP BY playerName ORDER BY sum_runs DESC LIMIT 5
['Adrian', 1820.0]
['Jose Antonio', 1692.0]
['Rafael', 1565.0]
['Brian Michael', 1500.0]
['Alexander Emmanuel', 1426.0]
from pinotdb import connect

conn = connect(host='localhost', port=8000, path='/query/sql', scheme='http')
curs = conn.cursor()

curs.execute("""
    SELECT * 
    FROM baseballStats
    WHERE league IN (%(leagues)s)
    """, {"leagues": ["AA", "NL"]})
for row in curs:
    print(row)
    
curs.execute("""
    SELECT *
    FROM baseballStats
    WHERE baseOnBalls > (%(score)d)
    """, {"score": 0})
for row in curs:
    print(row)
docker run \
  --name pinot-quickstart \
  -p 2123:2123 \
  -p 9000:9000 \
  -p 8000:8000 \
  apachepinot/pinot:latest QuickStart -type hybrid
python3 examples/pinot-quickstart-hybrid.py
Sending SQL to Pinot: SELECT * FROM airlineStats LIMIT 5
[171, 153, 19393, 0, 8, 8, 1433, '1400-1459', 0, 1425, 1240, 165, 'null', 0, 'WN', -2147483648, 1, 27, 17540, 0, 2, 2, 1242, '1200-1259', 0, 'MDW', 13232, 1323202, 30977, 'Chicago, IL', 'IL', 17, 'Illinois', 41, 861, 4, -2147483648, [-2147483648], 0, [-2147483648], ['null'], -2147483648, -2147483648, [-2147483648], -2147483648, ['null'], [-2147483648], [-2147483648], [-2147483648], 0, -2147483648, '2014-01-27', 402, 1, -2147483648, -2147483648, 1, -2147483648, 'BOS', 10721, 1072102, 30721, 'Boston, MA', 'MA', 25, 'Massachusetts', 13, 1, ['null'], -2147483648, 'N556WN', 6, 12, -2147483648, 'WN', -2147483648, 1254, 1427, 2014]
[183, 141, 20398, 1, 17, 17, 1302, '1200-1259', 1, 1245, 1005, 160, 'null', 0, 'MQ', 0, 1, 27, 17540, 0, -6, 0, 959, '1000-1059', -1, 'CMH', 11066, 1106603, 31066, 'Columbus, OH', 'OH', 39, 'Ohio', 44, 990, 4, -2147483648, [-2147483648], 0, [-2147483648], ['null'], -2147483648, -2147483648, [-2147483648], -2147483648, ['null'], [-2147483648], [-2147483648], [-2147483648], 0, -2147483648, '2014-01-27', 3574, 1, 0, -2147483648, 1, 17, 'MIA', 13303, 1330303, 32467, 'Miami, FL', 'FL', 12, 'Florida', 33, 1, ['null'], 0, 'N605MQ', 13, 29, -2147483648, 'MQ', 0, 1028, 1249, 2014]
[-2147483648, -2147483648, 20304, -2147483648, -2147483648, -2147483648, -2147483648, '2100-2159', -2147483648, 2131, 2005, 146, 'null', 0, 'OO', -2147483648, 1, 27, 17541, 1, 52, 52, 2057, '2000-2059', 3, 'COS', 11109, 1110902, 30189, 'Colorado Springs, CO', 'CO', 8, 'Colorado', 82, 809, 4, -2147483648, [11292], 1, [1129202], ['DEN'], -2147483648, 73, [9], 0, ['null'], [9], [-2147483648], [2304], 1, -2147483648, '2014-01-27', 5554, 1, -2147483648, -2147483648, 1, -2147483648, 'IAH', 12266, 1226603, 31453, 'Houston, TX', 'TX', 48, 'Texas', 74, 1, ['SEA', 'PSC', 'PHX', 'MSY', 'ATL', 'TYS', 'DEN', 'CHS', 'PDX', 'LAX', 'EWR', 'SFO', 'PIT', 'RDU', 'RAP', 'LSE', 'SAN', 'SBN', 'IAH', 'OAK', 'BRO', 'JFK', 'SAT', 'ORD', 'ACY', 'DFW', 'BWI'], -2147483648, 'N795SK', -2147483648, 19, -2147483648, 'OO', -2147483648, 2116, -2147483648, 2014]
[153, 125, 20436, 1, 41, 41, 1442, '1400-1459', 2, 1401, 1035, 146, 'null', 0, 'F9', 2, 1, 27, 17541, 1, 34, 34, 1109, '1000-1059', 2, 'DEN', 11292, 1129202, 30325, 'Denver, CO', 'CO', 8, 'Colorado', 82, 967, 4, -2147483648, [-2147483648], 0, [-2147483648], ['null'], -2147483648, -2147483648, [-2147483648], -2147483648, ['null'], [-2147483648], [-2147483648], [-2147483648], 0, -2147483648, '2014-01-27', 658, 1, 8, -2147483648, 1, 31, 'SFO', 14771, 1477101, 32457, 'San Francisco, CA', 'CA', 6, 'California', 91, 1, ['null'], 0, 'N923FR', 11, 17, -2147483648, 'F9', 0, 1126, 1431, 2014]
[-2147483648, -2147483648, 20304, -2147483648, -2147483648, -2147483648, -2147483648, '1400-1459', -2147483648, 1432, 1314, 78, 'B', 1, 'OO', -2147483648, 1, 27, 17541, -2147483648, -2147483648, -2147483648, -2147483648, '1300-1359', -2147483648, 'EAU', 11471, 1147103, 31471, 'Eau Claire, WI', 'WI', 55, 'Wisconsin', 45, 268, 2, -2147483648, [-2147483648], 0, [-2147483648], ['null'], -2147483648, -2147483648, [-2147483648], -2147483648, ['null'], [-2147483648], [-2147483648], [-2147483648], 0, -2147483648, '2014-01-27', 5455, 1, -2147483648, -2147483648, 1, -2147483648, 'ORD', 13930, 1393003, 30977, 'Chicago, IL', 'IL', 17, 'Illinois', 41, 1, ['null'], -2147483648, 'N903SW', -2147483648, -2147483648, -2147483648, 'OO', -2147483648, -2147483648, -2147483648, 2014]

Sending SQL to Pinot: SELECT count(*) FROM airlineStats LIMIT 5
[17772]

Sending SQL to Pinot: SELECT AirlineID, sum(Cancelled) FROM airlineStats WHERE Year > 2010 GROUP BY AirlineID LIMIT 5
[20409, 40.0]
[19930, 16.0]
[19805, 60.0]
[19790, 115.0]
[20366, 172.0]

Sending SQL to Pinot: select OriginCityName, max(Flights) from airlineStats group by OriginCityName ORDER BY max(Flights) DESC LIMIT 5
['Casper, WY', 1.0]
['Deadhorse, AK', 1.0]
['Austin, TX', 1.0]
['Chicago, IL', 1.0]
['Monterey, CA', 1.0]

Sending SQL to Pinot: SELECT OriginCityName, sum(Cancelled) AS sum_cancelled FROM airlineStats WHERE Year>2010 GROUP BY OriginCityName ORDER BY sum_cancelled DESC LIMIT 5
['Chicago, IL', 178.0]
['Atlanta, GA', 111.0]
['New York, NY', 65.0]
['Houston, TX', 62.0]
['Denver, CO', 49.0]

Sending Count(*) SQL to Pinot
17773

Sending SQL: "SELECT OriginCityName, sum(Cancelled) AS sum_cancelled FROM "airlineStats" WHERE Year>2010 GROUP BY OriginCityName ORDER BY sum_cancelled DESC LIMIT 5" to Pinot
[('Chicago, IL', 178.0), ('Atlanta, GA', 111.0), ('New York, NY', 65.0), ('Houston, TX', 62.0), ('Denver, CO', 49.0)]
  1. For Users
  2. External Clients

Python

PreviousJavaNextGolang
  • Python DB-API and SQLAlchemy dialect for Pinot
  • Installation
  • Usage
  • Examples with Pinot Quickstart
pip install pinotdb
from pinotdb import connect

conn = connect(host='localhost', port=8099, path='/query/sql', scheme='http')
curs = conn.cursor()
curs.execute("""
    SELECT place,
           CAST(REGEXP_EXTRACT(place, '(.*),', 1) AS FLOAT) AS lat,
           CAST(REGEXP_EXTRACT(place, ',(.*)', 1) AS FLOAT) AS lon
      FROM places
     LIMIT 10
""")
for row in curs:
    print(row)
https://pypi.org/project/pinotdb/
https://github.com/python-pinot-dbapi/pinot-dbapi