SlideShare a Scribd company logo
1 of 19
Presto Overview
Shixiong Zhu
Overview

Register
Ask active nodes

Discovery
Server
Coordinator
SQL
SQL
QueryInfo

SQLQueryManager

QueryResults
NextUri
CLI

SQLQueryExecution

StatementResource

QueryStarter

…

HttpRemoteTask
Fetch Data

QueryResults

Coordinator

Partial Data

OutputReceiver

Worker
SubPlan
ExchangeNode

AggregationNode(FINAL)

Plan
TableScanNode

OutputNode

FilterNode
SubPlan

AggregationNode

TableScanNode
FilterNode

OutputNode
AggregationNode(PARTIAL)
SinkNode
SubPlan

T: TableScanNode
A: AggregationNode
E: ExchangeNode

E

E

A(FINAL)

A(FINAL)

Plan
T

JoinNode
T
OutputNode

A

A
SubPlan

SubPlan

T

JoinNode

T

A(PARTIAL)

A(PARTIAL)

SinkNode

SinkNode

OutputNode
Stage

Task

Worker

Results

Stage
Stage

Worker

Coordinator

Worker

Worker

Worker
Worker
LocalExecutionPlan

SubPlan

Node1

Op1

Node2

Op2

Node3

Op3

…

…

Node3

Opn
LocalExecutionPlan

SubPlan
Node1

Node2

LocalExecutionPlan

Op1

Op2

SourceHash
JoinNode

HashJoinOperator

Node3

Op3

HashBuilderOperator
Page(max page size: 1MB, max rows:
16 * 1024 )

Row

Block

Slice
A byte array

Block

Block

Block

Block
Split

Split

Split

Split

Split

Split

Split
Is the data
ready?

Register a
callback

N
When the data of this
Split is ready, put the
Split back.

Y
Fetch one Page

Execute
Operator
Y

Has next
Operator?

N

N

TaskExecutor

Is the Split
done?

Thread number = core nubmer * 4

Y

Y

N
Time's up?
Execution Operators
Op1

page = op1.getOutput
op2.addInput(page)

Op2
page = op2.getOutput
op3.addInput(page)
Op3

…

Opn
Input
TableScanOperator

HiveSplit

DataStreamManager
RecordSetDataS
treamProvider
RecordProjectOperator

ConnectorData
StreamProvider
HiveRecordSet

HiveClient

ConnectorData
StreamProvider
HiveSplit

InputFormat

RecordReader
HiveRecordSet

Lines

TableScanOperator
RecordProjectOperator
Page
Next Operator
Load Balance
NodeMap

Split

Map: Rack -> Nodes

NodeSelector

NodeScheduler
Map: Host -> Nodes

Map: Host:Port -> Nodes

Node
NodeSelector.selectNode
• Select acceptable nodes (as least 10 nodes by
default)
– Nodes has the same address
– If not enough, add nodes in the same rack
– If not enough, randomly select nodes in other
racks

• Select the node with the smallest number of
assignments (pending tasks)
Output
• Only has SELETE statement
– Currently query results are streamed to the client
Communication
• Protocol: HTTP
• Data Format: JSON
• Every instance has one server and one client
Q&A

More Related Content

What's hot

Data Source API in Spark
Data Source API in SparkData Source API in Spark
Data Source API in SparkDatabricks
 
ClickHouse Deep Dive, by Aleksei Milovidov
ClickHouse Deep Dive, by Aleksei MilovidovClickHouse Deep Dive, by Aleksei Milovidov
ClickHouse Deep Dive, by Aleksei MilovidovAltinity Ltd
 
Adaptive Query Execution: Speeding Up Spark SQL at Runtime
Adaptive Query Execution: Speeding Up Spark SQL at RuntimeAdaptive Query Execution: Speeding Up Spark SQL at Runtime
Adaptive Query Execution: Speeding Up Spark SQL at RuntimeDatabricks
 
GraphFrames: Graph Queries In Spark SQL
GraphFrames: Graph Queries In Spark SQLGraphFrames: Graph Queries In Spark SQL
GraphFrames: Graph Queries In Spark SQLSpark Summit
 
Building Reliable Lakehouses with Apache Flink and Delta Lake
Building Reliable Lakehouses with Apache Flink and Delta LakeBuilding Reliable Lakehouses with Apache Flink and Delta Lake
Building Reliable Lakehouses with Apache Flink and Delta LakeFlink Forward
 
Tame the small files problem and optimize data layout for streaming ingestion...
Tame the small files problem and optimize data layout for streaming ingestion...Tame the small files problem and optimize data layout for streaming ingestion...
Tame the small files problem and optimize data layout for streaming ingestion...Flink Forward
 
Deep Dive on ClickHouse Sharding and Replication-2202-09-22.pdf
Deep Dive on ClickHouse Sharding and Replication-2202-09-22.pdfDeep Dive on ClickHouse Sharding and Replication-2202-09-22.pdf
Deep Dive on ClickHouse Sharding and Replication-2202-09-22.pdfAltinity Ltd
 
Apache Arrow Flight Overview
Apache Arrow Flight OverviewApache Arrow Flight Overview
Apache Arrow Flight OverviewJacques Nadeau
 
Hardening Kafka Replication
Hardening Kafka Replication Hardening Kafka Replication
Hardening Kafka Replication confluent
 
Deep Dive into Project Tungsten: Bringing Spark Closer to Bare Metal-(Josh Ro...
Deep Dive into Project Tungsten: Bringing Spark Closer to Bare Metal-(Josh Ro...Deep Dive into Project Tungsten: Bringing Spark Closer to Bare Metal-(Josh Ro...
Deep Dive into Project Tungsten: Bringing Spark Closer to Bare Metal-(Josh Ro...Spark Summit
 
Deep Dive: Memory Management in Apache Spark
Deep Dive: Memory Management in Apache SparkDeep Dive: Memory Management in Apache Spark
Deep Dive: Memory Management in Apache SparkDatabricks
 
Evening out the uneven: dealing with skew in Flink
Evening out the uneven: dealing with skew in FlinkEvening out the uneven: dealing with skew in Flink
Evening out the uneven: dealing with skew in FlinkFlink Forward
 
Differences between MariaDB 10.3 & MySQL 8.0
Differences between MariaDB 10.3 & MySQL 8.0Differences between MariaDB 10.3 & MySQL 8.0
Differences between MariaDB 10.3 & MySQL 8.0Colin Charles
 
It's Time To Stop Using Lambda Architecture
It's Time To Stop Using Lambda ArchitectureIt's Time To Stop Using Lambda Architecture
It's Time To Stop Using Lambda ArchitectureYaroslav Tkachenko
 
Tez Shuffle Handler: Shuffling at Scale with Apache Hadoop
Tez Shuffle Handler: Shuffling at Scale with Apache HadoopTez Shuffle Handler: Shuffling at Scale with Apache Hadoop
Tez Shuffle Handler: Shuffling at Scale with Apache HadoopDataWorks Summit
 
From Query Plan to Query Performance: Supercharging your Apache Spark Queries...
From Query Plan to Query Performance: Supercharging your Apache Spark Queries...From Query Plan to Query Performance: Supercharging your Apache Spark Queries...
From Query Plan to Query Performance: Supercharging your Apache Spark Queries...Databricks
 
Using ClickHouse for Experimentation
Using ClickHouse for ExperimentationUsing ClickHouse for Experimentation
Using ClickHouse for ExperimentationGleb Kanterov
 
Getting Started with Apache Spark on Kubernetes
Getting Started with Apache Spark on KubernetesGetting Started with Apache Spark on Kubernetes
Getting Started with Apache Spark on KubernetesDatabricks
 
Analytics at Speed: Introduction to ClickHouse and Common Use Cases. By Mikha...
Analytics at Speed: Introduction to ClickHouse and Common Use Cases. By Mikha...Analytics at Speed: Introduction to ClickHouse and Common Use Cases. By Mikha...
Analytics at Speed: Introduction to ClickHouse and Common Use Cases. By Mikha...Altinity Ltd
 

What's hot (20)

Data Source API in Spark
Data Source API in SparkData Source API in Spark
Data Source API in Spark
 
ClickHouse Deep Dive, by Aleksei Milovidov
ClickHouse Deep Dive, by Aleksei MilovidovClickHouse Deep Dive, by Aleksei Milovidov
ClickHouse Deep Dive, by Aleksei Milovidov
 
Adaptive Query Execution: Speeding Up Spark SQL at Runtime
Adaptive Query Execution: Speeding Up Spark SQL at RuntimeAdaptive Query Execution: Speeding Up Spark SQL at Runtime
Adaptive Query Execution: Speeding Up Spark SQL at Runtime
 
GraphFrames: Graph Queries In Spark SQL
GraphFrames: Graph Queries In Spark SQLGraphFrames: Graph Queries In Spark SQL
GraphFrames: Graph Queries In Spark SQL
 
Building Reliable Lakehouses with Apache Flink and Delta Lake
Building Reliable Lakehouses with Apache Flink and Delta LakeBuilding Reliable Lakehouses with Apache Flink and Delta Lake
Building Reliable Lakehouses with Apache Flink and Delta Lake
 
Tame the small files problem and optimize data layout for streaming ingestion...
Tame the small files problem and optimize data layout for streaming ingestion...Tame the small files problem and optimize data layout for streaming ingestion...
Tame the small files problem and optimize data layout for streaming ingestion...
 
Deep Dive on ClickHouse Sharding and Replication-2202-09-22.pdf
Deep Dive on ClickHouse Sharding and Replication-2202-09-22.pdfDeep Dive on ClickHouse Sharding and Replication-2202-09-22.pdf
Deep Dive on ClickHouse Sharding and Replication-2202-09-22.pdf
 
Apache Arrow Flight Overview
Apache Arrow Flight OverviewApache Arrow Flight Overview
Apache Arrow Flight Overview
 
Hardening Kafka Replication
Hardening Kafka Replication Hardening Kafka Replication
Hardening Kafka Replication
 
Deep Dive into Project Tungsten: Bringing Spark Closer to Bare Metal-(Josh Ro...
Deep Dive into Project Tungsten: Bringing Spark Closer to Bare Metal-(Josh Ro...Deep Dive into Project Tungsten: Bringing Spark Closer to Bare Metal-(Josh Ro...
Deep Dive into Project Tungsten: Bringing Spark Closer to Bare Metal-(Josh Ro...
 
Deep Dive: Memory Management in Apache Spark
Deep Dive: Memory Management in Apache SparkDeep Dive: Memory Management in Apache Spark
Deep Dive: Memory Management in Apache Spark
 
Evening out the uneven: dealing with skew in Flink
Evening out the uneven: dealing with skew in FlinkEvening out the uneven: dealing with skew in Flink
Evening out the uneven: dealing with skew in Flink
 
Differences between MariaDB 10.3 & MySQL 8.0
Differences between MariaDB 10.3 & MySQL 8.0Differences between MariaDB 10.3 & MySQL 8.0
Differences between MariaDB 10.3 & MySQL 8.0
 
It's Time To Stop Using Lambda Architecture
It's Time To Stop Using Lambda ArchitectureIt's Time To Stop Using Lambda Architecture
It's Time To Stop Using Lambda Architecture
 
Presto: SQL-on-anything
Presto: SQL-on-anythingPresto: SQL-on-anything
Presto: SQL-on-anything
 
Tez Shuffle Handler: Shuffling at Scale with Apache Hadoop
Tez Shuffle Handler: Shuffling at Scale with Apache HadoopTez Shuffle Handler: Shuffling at Scale with Apache Hadoop
Tez Shuffle Handler: Shuffling at Scale with Apache Hadoop
 
From Query Plan to Query Performance: Supercharging your Apache Spark Queries...
From Query Plan to Query Performance: Supercharging your Apache Spark Queries...From Query Plan to Query Performance: Supercharging your Apache Spark Queries...
From Query Plan to Query Performance: Supercharging your Apache Spark Queries...
 
Using ClickHouse for Experimentation
Using ClickHouse for ExperimentationUsing ClickHouse for Experimentation
Using ClickHouse for Experimentation
 
Getting Started with Apache Spark on Kubernetes
Getting Started with Apache Spark on KubernetesGetting Started with Apache Spark on Kubernetes
Getting Started with Apache Spark on Kubernetes
 
Analytics at Speed: Introduction to ClickHouse and Common Use Cases. By Mikha...
Analytics at Speed: Introduction to ClickHouse and Common Use Cases. By Mikha...Analytics at Speed: Introduction to ClickHouse and Common Use Cases. By Mikha...
Analytics at Speed: Introduction to ClickHouse and Common Use Cases. By Mikha...
 

Viewers also liked

Presto @ Facebook: Past, Present and Future
Presto @ Facebook: Past, Present and FuturePresto @ Facebook: Past, Present and Future
Presto @ Facebook: Past, Present and FutureDataWorks Summit
 
Presto Meetup @ Facebook (3/22/2016)
Presto Meetup @ Facebook (3/22/2016)Presto Meetup @ Facebook (3/22/2016)
Presto Meetup @ Facebook (3/22/2016)Martin Traverso
 
Presto at Facebook - Presto Meetup @ Boston (10/6/2015)
Presto at Facebook - Presto Meetup @ Boston (10/6/2015)Presto at Facebook - Presto Meetup @ Boston (10/6/2015)
Presto at Facebook - Presto Meetup @ Boston (10/6/2015)Martin Traverso
 
Presto meetup 2015-03-19 @Facebook
Presto meetup 2015-03-19 @FacebookPresto meetup 2015-03-19 @Facebook
Presto meetup 2015-03-19 @FacebookTreasure Data, Inc.
 
Introduction to Kafka Streams
Introduction to Kafka StreamsIntroduction to Kafka Streams
Introduction to Kafka StreamsGuozhang Wang
 
Facebook Presto presentation
Facebook Presto presentationFacebook Presto presentation
Facebook Presto presentationCyanny LIANG
 

Viewers also liked (6)

Presto @ Facebook: Past, Present and Future
Presto @ Facebook: Past, Present and FuturePresto @ Facebook: Past, Present and Future
Presto @ Facebook: Past, Present and Future
 
Presto Meetup @ Facebook (3/22/2016)
Presto Meetup @ Facebook (3/22/2016)Presto Meetup @ Facebook (3/22/2016)
Presto Meetup @ Facebook (3/22/2016)
 
Presto at Facebook - Presto Meetup @ Boston (10/6/2015)
Presto at Facebook - Presto Meetup @ Boston (10/6/2015)Presto at Facebook - Presto Meetup @ Boston (10/6/2015)
Presto at Facebook - Presto Meetup @ Boston (10/6/2015)
 
Presto meetup 2015-03-19 @Facebook
Presto meetup 2015-03-19 @FacebookPresto meetup 2015-03-19 @Facebook
Presto meetup 2015-03-19 @Facebook
 
Introduction to Kafka Streams
Introduction to Kafka StreamsIntroduction to Kafka Streams
Introduction to Kafka Streams
 
Facebook Presto presentation
Facebook Presto presentationFacebook Presto presentation
Facebook Presto presentation
 

Similar to Presto overview

Do flink on web with flow - Dongwon Kim & Haemee park, SK Telecom)
Do flink on web with flow - Dongwon Kim & Haemee park, SK Telecom)Do flink on web with flow - Dongwon Kim & Haemee park, SK Telecom)
Do flink on web with flow - Dongwon Kim & Haemee park, SK Telecom)Flink Forward
 
Do Flink on Web with FLOW
Do Flink on Web with FLOWDo Flink on Web with FLOW
Do Flink on Web with FLOWDongwon Kim
 
Big Data Day LA 2016/ Hadoop/ Spark/ Kafka track - Data Provenance Support in...
Big Data Day LA 2016/ Hadoop/ Spark/ Kafka track - Data Provenance Support in...Big Data Day LA 2016/ Hadoop/ Spark/ Kafka track - Data Provenance Support in...
Big Data Day LA 2016/ Hadoop/ Spark/ Kafka track - Data Provenance Support in...Data Con LA
 
Exactly Once Semantics Revisited (Jason Gustafson, Confluent) Kafka Summit NY...
Exactly Once Semantics Revisited (Jason Gustafson, Confluent) Kafka Summit NY...Exactly Once Semantics Revisited (Jason Gustafson, Confluent) Kafka Summit NY...
Exactly Once Semantics Revisited (Jason Gustafson, Confluent) Kafka Summit NY...confluent
 
Apache Flink internals
Apache Flink internalsApache Flink internals
Apache Flink internalsKostas Tzoumas
 
The Stream Processor as the Database - Apache Flink @ Berlin buzzwords
The Stream Processor as the Database - Apache Flink @ Berlin buzzwords   The Stream Processor as the Database - Apache Flink @ Berlin buzzwords
The Stream Processor as the Database - Apache Flink @ Berlin buzzwords Stephan Ewen
 
Unified stateful big data processing in Apache Beam (incubating)
Unified stateful big data processing in Apache Beam (incubating)Unified stateful big data processing in Apache Beam (incubating)
Unified stateful big data processing in Apache Beam (incubating)Aljoscha Krettek
 
Aljoscha Krettek - Portable stateful big data processing in Apache Beam
Aljoscha Krettek - Portable stateful big data processing in Apache BeamAljoscha Krettek - Portable stateful big data processing in Apache Beam
Aljoscha Krettek - Portable stateful big data processing in Apache BeamVerverica
 
Apache Flink: Better, Faster & Uncut - Piotr Nowojski, data Artisans
Apache Flink: Better, Faster & Uncut - Piotr Nowojski, data ArtisansApache Flink: Better, Faster & Uncut - Piotr Nowojski, data Artisans
Apache Flink: Better, Faster & Uncut - Piotr Nowojski, data ArtisansEvention
 
Apache Flink Training: System Overview
Apache Flink Training: System OverviewApache Flink Training: System Overview
Apache Flink Training: System OverviewFlink Forward
 
Delta Lake Streaming: Under the Hood
Delta Lake Streaming: Under the HoodDelta Lake Streaming: Under the Hood
Delta Lake Streaming: Under the HoodDatabricks
 
Tech Talk: ONOS- A Distributed SDN Network Operating System
Tech Talk: ONOS- A Distributed SDN Network Operating SystemTech Talk: ONOS- A Distributed SDN Network Operating System
Tech Talk: ONOS- A Distributed SDN Network Operating Systemnvirters
 
Vertica And Spark: Connecting Computation And Data
Vertica And Spark: Connecting Computation And DataVertica And Spark: Connecting Computation And Data
Vertica And Spark: Connecting Computation And DataRui Liu
 
Vertica And Spark: Connecting Computation And Data
Vertica And Spark: Connecting Computation And DataVertica And Spark: Connecting Computation And Data
Vertica And Spark: Connecting Computation And DataSpark Summit
 
BlinkDB and G-OLA: Supporting Continuous Answers with Error Bars in SparkSQL-...
BlinkDB and G-OLA: Supporting Continuous Answers with Error Bars in SparkSQL-...BlinkDB and G-OLA: Supporting Continuous Answers with Error Bars in SparkSQL-...
BlinkDB and G-OLA: Supporting Continuous Answers with Error Bars in SparkSQL-...Spark Summit
 
Flink SQL: The Challenges to Build a Streaming SQL Engine
Flink SQL: The Challenges to Build a Streaming SQL EngineFlink SQL: The Challenges to Build a Streaming SQL Engine
Flink SQL: The Challenges to Build a Streaming SQL EngineHostedbyConfluent
 

Similar to Presto overview (20)

Do flink on web with flow - Dongwon Kim & Haemee park, SK Telecom)
Do flink on web with flow - Dongwon Kim & Haemee park, SK Telecom)Do flink on web with flow - Dongwon Kim & Haemee park, SK Telecom)
Do flink on web with flow - Dongwon Kim & Haemee park, SK Telecom)
 
Do Flink on Web with FLOW
Do Flink on Web with FLOWDo Flink on Web with FLOW
Do Flink on Web with FLOW
 
Big Data Day LA 2016/ Hadoop/ Spark/ Kafka track - Data Provenance Support in...
Big Data Day LA 2016/ Hadoop/ Spark/ Kafka track - Data Provenance Support in...Big Data Day LA 2016/ Hadoop/ Spark/ Kafka track - Data Provenance Support in...
Big Data Day LA 2016/ Hadoop/ Spark/ Kafka track - Data Provenance Support in...
 
Exactly Once Semantics Revisited (Jason Gustafson, Confluent) Kafka Summit NY...
Exactly Once Semantics Revisited (Jason Gustafson, Confluent) Kafka Summit NY...Exactly Once Semantics Revisited (Jason Gustafson, Confluent) Kafka Summit NY...
Exactly Once Semantics Revisited (Jason Gustafson, Confluent) Kafka Summit NY...
 
Apache Flink internals
Apache Flink internalsApache Flink internals
Apache Flink internals
 
Second Level Cache in JPA Explained
Second Level Cache in JPA ExplainedSecond Level Cache in JPA Explained
Second Level Cache in JPA Explained
 
Flink internals web
Flink internals web Flink internals web
Flink internals web
 
The Stream Processor as a Database Apache Flink
The Stream Processor as a Database Apache FlinkThe Stream Processor as a Database Apache Flink
The Stream Processor as a Database Apache Flink
 
The Stream Processor as the Database - Apache Flink @ Berlin buzzwords
The Stream Processor as the Database - Apache Flink @ Berlin buzzwords   The Stream Processor as the Database - Apache Flink @ Berlin buzzwords
The Stream Processor as the Database - Apache Flink @ Berlin buzzwords
 
Unified stateful big data processing in Apache Beam (incubating)
Unified stateful big data processing in Apache Beam (incubating)Unified stateful big data processing in Apache Beam (incubating)
Unified stateful big data processing in Apache Beam (incubating)
 
Aljoscha Krettek - Portable stateful big data processing in Apache Beam
Aljoscha Krettek - Portable stateful big data processing in Apache BeamAljoscha Krettek - Portable stateful big data processing in Apache Beam
Aljoscha Krettek - Portable stateful big data processing in Apache Beam
 
Apache Flink: Better, Faster & Uncut - Piotr Nowojski, data Artisans
Apache Flink: Better, Faster & Uncut - Piotr Nowojski, data ArtisansApache Flink: Better, Faster & Uncut - Piotr Nowojski, data Artisans
Apache Flink: Better, Faster & Uncut - Piotr Nowojski, data Artisans
 
Apache Flink Training: System Overview
Apache Flink Training: System OverviewApache Flink Training: System Overview
Apache Flink Training: System Overview
 
Delta Lake Streaming: Under the Hood
Delta Lake Streaming: Under the HoodDelta Lake Streaming: Under the Hood
Delta Lake Streaming: Under the Hood
 
Tech Talk: ONOS- A Distributed SDN Network Operating System
Tech Talk: ONOS- A Distributed SDN Network Operating SystemTech Talk: ONOS- A Distributed SDN Network Operating System
Tech Talk: ONOS- A Distributed SDN Network Operating System
 
Vertica And Spark: Connecting Computation And Data
Vertica And Spark: Connecting Computation And DataVertica And Spark: Connecting Computation And Data
Vertica And Spark: Connecting Computation And Data
 
Vertica And Spark: Connecting Computation And Data
Vertica And Spark: Connecting Computation And DataVertica And Spark: Connecting Computation And Data
Vertica And Spark: Connecting Computation And Data
 
BlinkDB and G-OLA: Supporting Continuous Answers with Error Bars in SparkSQL-...
BlinkDB and G-OLA: Supporting Continuous Answers with Error Bars in SparkSQL-...BlinkDB and G-OLA: Supporting Continuous Answers with Error Bars in SparkSQL-...
BlinkDB and G-OLA: Supporting Continuous Answers with Error Bars in SparkSQL-...
 
Flink SQL: The Challenges to Build a Streaming SQL Engine
Flink SQL: The Challenges to Build a Streaming SQL EngineFlink SQL: The Challenges to Build a Streaming SQL Engine
Flink SQL: The Challenges to Build a Streaming SQL Engine
 
Flink meetup
Flink meetupFlink meetup
Flink meetup
 

Recently uploaded

How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.Curtis Poe
 
From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .Alan Dix
 
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfHyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfPrecisely
 
Commit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyCommit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyAlfredo García Lavilla
 
SAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxSAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxNavinnSomaal
 
Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Scott Keck-Warren
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek SchlawackFwdays
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):comworks
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebUiPathCommunity
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr BaganFwdays
 
DSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningDSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningLars Bell
 
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Mark Simos
 
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Mattias Andersson
 
Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 3652toLead Limited
 
DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsSergiu Bodiu
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024Stephanie Beckett
 
Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Manik S Magar
 
H2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo Day
H2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo DayH2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo Day
H2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo DaySri Ambati
 

Recently uploaded (20)

How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.
 
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptxE-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
 
From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .
 
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfHyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
 
Commit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyCommit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easy
 
SAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxSAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptx
 
Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio Web
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan
 
DSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningDSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine Tuning
 
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
 
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?
 
Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365
 
DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platforms
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024
 
Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!
 
H2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo Day
H2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo DayH2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo Day
H2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo Day
 

Presto overview

Editor's Notes

  1. A SubPlan will to convert by LocalExecutionPlanner to LocalExecutionPlan which has a operator sequence.
  2. HashJoinOperator andHashBuilderOperator is connected by SourceHash which contains the output of HashBuilderOperator.
  3. You can image Slice is a byte array. The Slice size is the array size. The Block size is the Slice size. The Page size is sum of all the Block sizes.
  4. Every Split is only allowed to execute 1s by default. When the time is up, the split will be put back to the queue.
  5. RecordSetDataStreamProvider is a subclass of ConnectorDataStreamProvider.
  6. When DiscoveryNodeManager receives any Node information query, it will check if the cache is expired (5 seconds).If so, it will ask the ServiceSelectorto fetch the active nodes and drop the failure nodes.ServiceSelector will fetch the new node list from the Discovery Server every 10s by default.There is a thread in HeartbeatFailureDetector which will send the heartbeat to every active node 500ms by default.