SlideShare a Scribd company logo
Paper Reading
Orca: A Modular Query Optimizer Architecture for Big Data
Presented by Weizhen Wang
Background
In this paper we present the architecture of Orca, the new query
optimizer for all Pivotal data management products, including
Pivotal Greenplum Database and Pivotal HAWQ.
ORCA = GPORCA = Greenplum ORCA = ...
Jul 2015, PQO: Pivotal Query Optimizer (Released in GPDB 4.3.5.0)
Jun 2014, ORCA: SIGMOD 2014 Paper
https://github.com/greenplum-db/gporca
In source code GPOPT
src/backend/gpopt
psql: select gp_opt_version();
High level GPDB architecture
• Storage and processing of large amounts of data are handledby distributing the load across several
servers or hosts to create an array of individual databases. all working together to present a single
database image.
• The master is the entry point to GPDB, where clients connect and submit SQL statements. The
master coordinates work with other database instances, called segments, to handle data processing
and storage.
• When a query is submitted to the master, it is optimized and broken into smaller components
dispatched to segments to work together on delivering the final results.
• The interconnect is the networking layer responsible for inter-process communication between the
segments. The interconnect uses a standard Gigabit Ethernet switching fabric.
High level GPDB architecture
GPDB Using Planner (`set optimizer=off`)
GPDB Using GPORCA (`set optimizer=on`)
Orca architecture
DXL: Data exchange
Language
Interaction of Orca with database system
Five Optimization Steps in Orca
● [Step 0] Pre-Process: e.g., predicates pushdown
● [Step 1] Exploration: All equivalent logical plans
● [Step 2] Statistics Derivation: histograms
● [Step 3] Implementation: Logical to Physical
● [Step 4] MPP Optimization: Enforcing and Costing
Step 0 Pre-Process with 25 iterations (TL, DR)
Step 1 Exploration 1/2: Memoization
Compact in-memory data
structure
capturing plan space:
Group: Container of equivalent
expressions
Group Expression: Operator
that has
other groups as its children
Step 1 Exploration 2/2: Transformation
Step 2 Statistics Derivation
Step 3 Implementation
Step 4 MPP Optimization 1/3 Requirement
Step 4 MPP Optimization 2/3 Cost
Step 4 MPP Optimization 3/3 Enforcement
Processing optimization requests in the Memo
Parallel Query Optimization
Exp(g): Generate logically equivalent expressions of all group expressions in group g.
Exp(gexpr): Generate logically equivalent expressions of a group expression gexpr.
Imp(g): Generate implementations of all group expressions in group g.
Imp(gexpr): Generate implementation alternatives of a group expression gexpr.
Opt(g, req): Return the plan with the least estimated cost that is rooted by an operator in
group g and satises optimization request req.
Opt(gexpr, req): Return the plan with the least estimated cost that is rooted by gexpr and
satises optimization request req.
Xform(gexpr, t): Transform group expression gexpr using rule t.
Optimization process is broken to small work units called optimization jobs.
Orca currently has seven different types of optimization jobs:
Parallel Query Optimization
Optimization jobs dependency graph
Test
Minimal Repros
AMPERe is a tool for Automatic capture of
Minimal
Portable and Executable Repros.
Replay of AMPERe dump
TAQO
MEASURING ACCURACY
• Discordance of plan pairs.
• Relevance of plan
• Pairwise distance
MEASURING ACCURACY
PingCAP.com
TAQO components
● Configuration Generator
● Execution Tracker
● Plan Deduplicator
● Ranker
Great Query Performance of Orca vs Planner
(TPC-DS 10TB)
Thanks
Ad

More Related Content

What's hot (20)

Implementing Near-Realtime Datacenter Health Analytics using Model-driven Ver...
Implementing Near-Realtime Datacenter Health Analytics using Model-driven Ver...Implementing Near-Realtime Datacenter Health Analytics using Model-driven Ver...
Implementing Near-Realtime Datacenter Health Analytics using Model-driven Ver...
Spark Summit
 
Inside Apache SystemML by Frederick Reiss
Inside Apache SystemML by Frederick ReissInside Apache SystemML by Frederick Reiss
Inside Apache SystemML by Frederick Reiss
Spark Summit
 
Implementation of linear regression and logistic regression on Spark
Implementation of linear regression and logistic regression on SparkImplementation of linear regression and logistic regression on Spark
Implementation of linear regression and logistic regression on Spark
Dalei Li
 
H2O World - GLM - Tomas Nykodym
H2O World - GLM - Tomas NykodymH2O World - GLM - Tomas Nykodym
H2O World - GLM - Tomas Nykodym
Sri Ambati
 
Tokyo Webmining Talk1
Tokyo Webmining Talk1Tokyo Webmining Talk1
Tokyo Webmining Talk1
Kenta Oono
 
A Scalable Hierarchical Clustering Algorithm Using Spark: Spark Summit East t...
A Scalable Hierarchical Clustering Algorithm Using Spark: Spark Summit East t...A Scalable Hierarchical Clustering Algorithm Using Spark: Spark Summit East t...
A Scalable Hierarchical Clustering Algorithm Using Spark: Spark Summit East t...
Spark Summit
 
Surge: Rise of Scalable Machine Learning at Yahoo!
Surge: Rise of Scalable Machine Learning at Yahoo!Surge: Rise of Scalable Machine Learning at Yahoo!
Surge: Rise of Scalable Machine Learning at Yahoo!
DataWorks Summit
 
How Machine Learning and AI Can Support the Fight Against COVID-19
How Machine Learning and AI Can Support the Fight Against COVID-19How Machine Learning and AI Can Support the Fight Against COVID-19
How Machine Learning and AI Can Support the Fight Against COVID-19
Databricks
 
Enhancements on Spark SQL optimizer by Min Qiu
Enhancements on Spark SQL optimizer by Min QiuEnhancements on Spark SQL optimizer by Min Qiu
Enhancements on Spark SQL optimizer by Min Qiu
Spark Summit
 
Lazy Join Optimizations Without Upfront Statistics with Matteo Interlandi
Lazy Join Optimizations Without Upfront Statistics with Matteo InterlandiLazy Join Optimizations Without Upfront Statistics with Matteo Interlandi
Lazy Join Optimizations Without Upfront Statistics with Matteo Interlandi
Databricks
 
Google's Dremel
Google's DremelGoogle's Dremel
Google's Dremel
Maria Stylianou
 
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
 
Optimizing Terascale Machine Learning Pipelines with Keystone ML
Optimizing Terascale Machine Learning Pipelines with Keystone MLOptimizing Terascale Machine Learning Pipelines with Keystone ML
Optimizing Terascale Machine Learning Pipelines with Keystone ML
Spark Summit
 
Block Sampling: Efficient Accurate Online Aggregation in MapReduce
Block Sampling: Efficient Accurate Online Aggregation in MapReduceBlock Sampling: Efficient Accurate Online Aggregation in MapReduce
Block Sampling: Efficient Accurate Online Aggregation in MapReduce
Vasia Kalavri
 
Accumulo Summit 2016: GeoMesa: Using Accumulo for Optimized Spatio-Temporal P...
Accumulo Summit 2016: GeoMesa: Using Accumulo for Optimized Spatio-Temporal P...Accumulo Summit 2016: GeoMesa: Using Accumulo for Optimized Spatio-Temporal P...
Accumulo Summit 2016: GeoMesa: Using Accumulo for Optimized Spatio-Temporal P...
Accumulo Summit
 
Overview of Apache SystemML by Berthold Reinwald and Nakul Jindal
Overview of Apache SystemML by Berthold Reinwald and Nakul JindalOverview of Apache SystemML by Berthold Reinwald and Nakul Jindal
Overview of Apache SystemML by Berthold Reinwald and Nakul Jindal
Arvind Surve
 
Generalized Linear Models in Spark MLlib and SparkR
Generalized Linear Models in Spark MLlib and SparkRGeneralized Linear Models in Spark MLlib and SparkR
Generalized Linear Models in Spark MLlib and SparkR
Databricks
 
Some experiences for porting application to Intel Xeon Phi
Some experiences for porting application to Intel Xeon PhiSome experiences for porting application to Intel Xeon Phi
Some experiences for porting application to Intel Xeon Phi
Maho Nakata
 
Accumulo and the Convergence of Machine Learning, Big Data, and Supercomputing
Accumulo and the Convergence of Machine Learning, Big Data, and SupercomputingAccumulo and the Convergence of Machine Learning, Big Data, and Supercomputing
Accumulo and the Convergence of Machine Learning, Big Data, and Supercomputing
Accumulo Summit
 
Java/Scala Lab 2016. Сергей Моренец: Способы повышения эффективности в Java 8.
Java/Scala Lab 2016. Сергей Моренец: Способы повышения эффективности в Java 8.Java/Scala Lab 2016. Сергей Моренец: Способы повышения эффективности в Java 8.
Java/Scala Lab 2016. Сергей Моренец: Способы повышения эффективности в Java 8.
GeeksLab Odessa
 
Implementing Near-Realtime Datacenter Health Analytics using Model-driven Ver...
Implementing Near-Realtime Datacenter Health Analytics using Model-driven Ver...Implementing Near-Realtime Datacenter Health Analytics using Model-driven Ver...
Implementing Near-Realtime Datacenter Health Analytics using Model-driven Ver...
Spark Summit
 
Inside Apache SystemML by Frederick Reiss
Inside Apache SystemML by Frederick ReissInside Apache SystemML by Frederick Reiss
Inside Apache SystemML by Frederick Reiss
Spark Summit
 
Implementation of linear regression and logistic regression on Spark
Implementation of linear regression and logistic regression on SparkImplementation of linear regression and logistic regression on Spark
Implementation of linear regression and logistic regression on Spark
Dalei Li
 
H2O World - GLM - Tomas Nykodym
H2O World - GLM - Tomas NykodymH2O World - GLM - Tomas Nykodym
H2O World - GLM - Tomas Nykodym
Sri Ambati
 
Tokyo Webmining Talk1
Tokyo Webmining Talk1Tokyo Webmining Talk1
Tokyo Webmining Talk1
Kenta Oono
 
A Scalable Hierarchical Clustering Algorithm Using Spark: Spark Summit East t...
A Scalable Hierarchical Clustering Algorithm Using Spark: Spark Summit East t...A Scalable Hierarchical Clustering Algorithm Using Spark: Spark Summit East t...
A Scalable Hierarchical Clustering Algorithm Using Spark: Spark Summit East t...
Spark Summit
 
Surge: Rise of Scalable Machine Learning at Yahoo!
Surge: Rise of Scalable Machine Learning at Yahoo!Surge: Rise of Scalable Machine Learning at Yahoo!
Surge: Rise of Scalable Machine Learning at Yahoo!
DataWorks Summit
 
How Machine Learning and AI Can Support the Fight Against COVID-19
How Machine Learning and AI Can Support the Fight Against COVID-19How Machine Learning and AI Can Support the Fight Against COVID-19
How Machine Learning and AI Can Support the Fight Against COVID-19
Databricks
 
Enhancements on Spark SQL optimizer by Min Qiu
Enhancements on Spark SQL optimizer by Min QiuEnhancements on Spark SQL optimizer by Min Qiu
Enhancements on Spark SQL optimizer by Min Qiu
Spark Summit
 
Lazy Join Optimizations Without Upfront Statistics with Matteo Interlandi
Lazy Join Optimizations Without Upfront Statistics with Matteo InterlandiLazy Join Optimizations Without Upfront Statistics with Matteo Interlandi
Lazy Join Optimizations Without Upfront Statistics with Matteo Interlandi
Databricks
 
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
 
Optimizing Terascale Machine Learning Pipelines with Keystone ML
Optimizing Terascale Machine Learning Pipelines with Keystone MLOptimizing Terascale Machine Learning Pipelines with Keystone ML
Optimizing Terascale Machine Learning Pipelines with Keystone ML
Spark Summit
 
Block Sampling: Efficient Accurate Online Aggregation in MapReduce
Block Sampling: Efficient Accurate Online Aggregation in MapReduceBlock Sampling: Efficient Accurate Online Aggregation in MapReduce
Block Sampling: Efficient Accurate Online Aggregation in MapReduce
Vasia Kalavri
 
Accumulo Summit 2016: GeoMesa: Using Accumulo for Optimized Spatio-Temporal P...
Accumulo Summit 2016: GeoMesa: Using Accumulo for Optimized Spatio-Temporal P...Accumulo Summit 2016: GeoMesa: Using Accumulo for Optimized Spatio-Temporal P...
Accumulo Summit 2016: GeoMesa: Using Accumulo for Optimized Spatio-Temporal P...
Accumulo Summit
 
Overview of Apache SystemML by Berthold Reinwald and Nakul Jindal
Overview of Apache SystemML by Berthold Reinwald and Nakul JindalOverview of Apache SystemML by Berthold Reinwald and Nakul Jindal
Overview of Apache SystemML by Berthold Reinwald and Nakul Jindal
Arvind Surve
 
Generalized Linear Models in Spark MLlib and SparkR
Generalized Linear Models in Spark MLlib and SparkRGeneralized Linear Models in Spark MLlib and SparkR
Generalized Linear Models in Spark MLlib and SparkR
Databricks
 
Some experiences for porting application to Intel Xeon Phi
Some experiences for porting application to Intel Xeon PhiSome experiences for porting application to Intel Xeon Phi
Some experiences for porting application to Intel Xeon Phi
Maho Nakata
 
Accumulo and the Convergence of Machine Learning, Big Data, and Supercomputing
Accumulo and the Convergence of Machine Learning, Big Data, and SupercomputingAccumulo and the Convergence of Machine Learning, Big Data, and Supercomputing
Accumulo and the Convergence of Machine Learning, Big Data, and Supercomputing
Accumulo Summit
 
Java/Scala Lab 2016. Сергей Моренец: Способы повышения эффективности в Java 8.
Java/Scala Lab 2016. Сергей Моренец: Способы повышения эффективности в Java 8.Java/Scala Lab 2016. Сергей Моренец: Способы повышения эффективности в Java 8.
Java/Scala Lab 2016. Сергей Моренец: Способы повышения эффективности в Java 8.
GeeksLab Odessa
 

Similar to [Paper Reading]Orca: A Modular Query Optimizer Architecture for Big Data (20)

PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems
PRETZEL: Opening the Black Box of Machine Learning Prediction Serving SystemsPRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems
PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems
NECST Lab @ Politecnico di Milano
 
Pivotal Data Labs - Technology and Tools in our Data Scientist's Arsenal
Pivotal Data Labs - Technology and Tools in our Data Scientist's Arsenal Pivotal Data Labs - Technology and Tools in our Data Scientist's Arsenal
Pivotal Data Labs - Technology and Tools in our Data Scientist's Arsenal
Srivatsan Ramanujam
 
Learn How Dell Improved Postgres/Greenplum Performance 20x with a Database Pr...
Learn How Dell Improved Postgres/Greenplum Performance 20x with a Database Pr...Learn How Dell Improved Postgres/Greenplum Performance 20x with a Database Pr...
Learn How Dell Improved Postgres/Greenplum Performance 20x with a Database Pr...
VMware Tanzu
 
Kerberizing Spark: Spark Summit East talk by Abel Rincon and Jorge Lopez-Malla
Kerberizing Spark: Spark Summit East talk by Abel Rincon and Jorge Lopez-MallaKerberizing Spark: Spark Summit East talk by Abel Rincon and Jorge Lopez-Malla
Kerberizing Spark: Spark Summit East talk by Abel Rincon and Jorge Lopez-Malla
Spark Summit
 
Graphite & Metrictank - Meetup Tel Aviv Yafo
Graphite & Metrictank - Meetup Tel Aviv YafoGraphite & Metrictank - Meetup Tel Aviv Yafo
Graphite & Metrictank - Meetup Tel Aviv Yafo
Dieter Plaetinck
 
Orca: A Modular Query Optimizer Architecture for Big Data
Orca: A Modular Query Optimizer Architecture for Big DataOrca: A Modular Query Optimizer Architecture for Big Data
Orca: A Modular Query Optimizer Architecture for Big Data
EMC
 
SOME WORKLOAD SCHEDULING ALTERNATIVES 11.07.2013
SOME WORKLOAD SCHEDULING ALTERNATIVES 11.07.2013SOME WORKLOAD SCHEDULING ALTERNATIVES 11.07.2013
SOME WORKLOAD SCHEDULING ALTERNATIVES 11.07.2013
James McGalliard
 
GPORCA: Query Optimization as a Service
GPORCA: Query Optimization as a ServiceGPORCA: Query Optimization as a Service
GPORCA: Query Optimization as a Service
PivotalOpenSourceHub
 
Distributed Deep Learning with Apache Spark and TensorFlow with Jim Dowling
Distributed Deep Learning with Apache Spark and TensorFlow with Jim DowlingDistributed Deep Learning with Apache Spark and TensorFlow with Jim Dowling
Distributed Deep Learning with Apache Spark and TensorFlow with Jim Dowling
Databricks
 
Sprint 44 review
Sprint 44 reviewSprint 44 review
Sprint 44 review
ManageIQ
 
pMatlab on BlueGene
pMatlab on BlueGenepMatlab on BlueGene
pMatlab on BlueGene
vsachde
 
IRJET- Review of Existing Methods in K-Means Clustering Algorithm
IRJET- Review of Existing Methods in K-Means Clustering AlgorithmIRJET- Review of Existing Methods in K-Means Clustering Algorithm
IRJET- Review of Existing Methods in K-Means Clustering Algorithm
IRJET Journal
 
Greenplum for Kubernetes - Greenplum Summit 2019
Greenplum for Kubernetes - Greenplum Summit 2019Greenplum for Kubernetes - Greenplum Summit 2019
Greenplum for Kubernetes - Greenplum Summit 2019
VMware Tanzu
 
Boost Your Neo4j with User-Defined Procedures
Boost Your Neo4j with User-Defined ProceduresBoost Your Neo4j with User-Defined Procedures
Boost Your Neo4j with User-Defined Procedures
Neo4j
 
Sawmill - Integrating R and Large Data Clouds
Sawmill - Integrating R and Large Data CloudsSawmill - Integrating R and Large Data Clouds
Sawmill - Integrating R and Large Data Clouds
Robert Grossman
 
SF Big Analytics 20191112: How to performance-tune Spark applications in larg...
SF Big Analytics 20191112: How to performance-tune Spark applications in larg...SF Big Analytics 20191112: How to performance-tune Spark applications in larg...
SF Big Analytics 20191112: How to performance-tune Spark applications in larg...
Chester Chen
 
Strata Conference + Hadoop World NY 2013: Running On-premise Hadoop as a Busi...
Strata Conference + Hadoop World NY 2013: Running On-premise Hadoop as a Busi...Strata Conference + Hadoop World NY 2013: Running On-premise Hadoop as a Busi...
Strata Conference + Hadoop World NY 2013: Running On-premise Hadoop as a Busi...
Sumeet Singh
 
A Database-Hadoop Hybrid Approach to Scalable Machine Learning
A Database-Hadoop Hybrid Approach to Scalable Machine LearningA Database-Hadoop Hybrid Approach to Scalable Machine Learning
A Database-Hadoop Hybrid Approach to Scalable Machine Learning
Makoto Yui
 
ASSESSING THE PERFORMANCE AND ENERGY USAGE OF MULTI-CPUS, MULTI-CORE AND MANY...
ASSESSING THE PERFORMANCE AND ENERGY USAGE OF MULTI-CPUS, MULTI-CORE AND MANY...ASSESSING THE PERFORMANCE AND ENERGY USAGE OF MULTI-CPUS, MULTI-CORE AND MANY...
ASSESSING THE PERFORMANCE AND ENERGY USAGE OF MULTI-CPUS, MULTI-CORE AND MANY...
ijdpsjournal
 
ASSESSING THE PERFORMANCE AND ENERGY USAGE OF MULTI-CPUS, MULTI-CORE AND MANY...
ASSESSING THE PERFORMANCE AND ENERGY USAGE OF MULTI-CPUS, MULTI-CORE AND MANY...ASSESSING THE PERFORMANCE AND ENERGY USAGE OF MULTI-CPUS, MULTI-CORE AND MANY...
ASSESSING THE PERFORMANCE AND ENERGY USAGE OF MULTI-CPUS, MULTI-CORE AND MANY...
ijdpsjournal
 
PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems
PRETZEL: Opening the Black Box of Machine Learning Prediction Serving SystemsPRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems
PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems
NECST Lab @ Politecnico di Milano
 
Pivotal Data Labs - Technology and Tools in our Data Scientist's Arsenal
Pivotal Data Labs - Technology and Tools in our Data Scientist's Arsenal Pivotal Data Labs - Technology and Tools in our Data Scientist's Arsenal
Pivotal Data Labs - Technology and Tools in our Data Scientist's Arsenal
Srivatsan Ramanujam
 
Learn How Dell Improved Postgres/Greenplum Performance 20x with a Database Pr...
Learn How Dell Improved Postgres/Greenplum Performance 20x with a Database Pr...Learn How Dell Improved Postgres/Greenplum Performance 20x with a Database Pr...
Learn How Dell Improved Postgres/Greenplum Performance 20x with a Database Pr...
VMware Tanzu
 
Kerberizing Spark: Spark Summit East talk by Abel Rincon and Jorge Lopez-Malla
Kerberizing Spark: Spark Summit East talk by Abel Rincon and Jorge Lopez-MallaKerberizing Spark: Spark Summit East talk by Abel Rincon and Jorge Lopez-Malla
Kerberizing Spark: Spark Summit East talk by Abel Rincon and Jorge Lopez-Malla
Spark Summit
 
Graphite & Metrictank - Meetup Tel Aviv Yafo
Graphite & Metrictank - Meetup Tel Aviv YafoGraphite & Metrictank - Meetup Tel Aviv Yafo
Graphite & Metrictank - Meetup Tel Aviv Yafo
Dieter Plaetinck
 
Orca: A Modular Query Optimizer Architecture for Big Data
Orca: A Modular Query Optimizer Architecture for Big DataOrca: A Modular Query Optimizer Architecture for Big Data
Orca: A Modular Query Optimizer Architecture for Big Data
EMC
 
SOME WORKLOAD SCHEDULING ALTERNATIVES 11.07.2013
SOME WORKLOAD SCHEDULING ALTERNATIVES 11.07.2013SOME WORKLOAD SCHEDULING ALTERNATIVES 11.07.2013
SOME WORKLOAD SCHEDULING ALTERNATIVES 11.07.2013
James McGalliard
 
GPORCA: Query Optimization as a Service
GPORCA: Query Optimization as a ServiceGPORCA: Query Optimization as a Service
GPORCA: Query Optimization as a Service
PivotalOpenSourceHub
 
Distributed Deep Learning with Apache Spark and TensorFlow with Jim Dowling
Distributed Deep Learning with Apache Spark and TensorFlow with Jim DowlingDistributed Deep Learning with Apache Spark and TensorFlow with Jim Dowling
Distributed Deep Learning with Apache Spark and TensorFlow with Jim Dowling
Databricks
 
Sprint 44 review
Sprint 44 reviewSprint 44 review
Sprint 44 review
ManageIQ
 
pMatlab on BlueGene
pMatlab on BlueGenepMatlab on BlueGene
pMatlab on BlueGene
vsachde
 
IRJET- Review of Existing Methods in K-Means Clustering Algorithm
IRJET- Review of Existing Methods in K-Means Clustering AlgorithmIRJET- Review of Existing Methods in K-Means Clustering Algorithm
IRJET- Review of Existing Methods in K-Means Clustering Algorithm
IRJET Journal
 
Greenplum for Kubernetes - Greenplum Summit 2019
Greenplum for Kubernetes - Greenplum Summit 2019Greenplum for Kubernetes - Greenplum Summit 2019
Greenplum for Kubernetes - Greenplum Summit 2019
VMware Tanzu
 
Boost Your Neo4j with User-Defined Procedures
Boost Your Neo4j with User-Defined ProceduresBoost Your Neo4j with User-Defined Procedures
Boost Your Neo4j with User-Defined Procedures
Neo4j
 
Sawmill - Integrating R and Large Data Clouds
Sawmill - Integrating R and Large Data CloudsSawmill - Integrating R and Large Data Clouds
Sawmill - Integrating R and Large Data Clouds
Robert Grossman
 
SF Big Analytics 20191112: How to performance-tune Spark applications in larg...
SF Big Analytics 20191112: How to performance-tune Spark applications in larg...SF Big Analytics 20191112: How to performance-tune Spark applications in larg...
SF Big Analytics 20191112: How to performance-tune Spark applications in larg...
Chester Chen
 
Strata Conference + Hadoop World NY 2013: Running On-premise Hadoop as a Busi...
Strata Conference + Hadoop World NY 2013: Running On-premise Hadoop as a Busi...Strata Conference + Hadoop World NY 2013: Running On-premise Hadoop as a Busi...
Strata Conference + Hadoop World NY 2013: Running On-premise Hadoop as a Busi...
Sumeet Singh
 
A Database-Hadoop Hybrid Approach to Scalable Machine Learning
A Database-Hadoop Hybrid Approach to Scalable Machine LearningA Database-Hadoop Hybrid Approach to Scalable Machine Learning
A Database-Hadoop Hybrid Approach to Scalable Machine Learning
Makoto Yui
 
ASSESSING THE PERFORMANCE AND ENERGY USAGE OF MULTI-CPUS, MULTI-CORE AND MANY...
ASSESSING THE PERFORMANCE AND ENERGY USAGE OF MULTI-CPUS, MULTI-CORE AND MANY...ASSESSING THE PERFORMANCE AND ENERGY USAGE OF MULTI-CPUS, MULTI-CORE AND MANY...
ASSESSING THE PERFORMANCE AND ENERGY USAGE OF MULTI-CPUS, MULTI-CORE AND MANY...
ijdpsjournal
 
ASSESSING THE PERFORMANCE AND ENERGY USAGE OF MULTI-CPUS, MULTI-CORE AND MANY...
ASSESSING THE PERFORMANCE AND ENERGY USAGE OF MULTI-CPUS, MULTI-CORE AND MANY...ASSESSING THE PERFORMANCE AND ENERGY USAGE OF MULTI-CPUS, MULTI-CORE AND MANY...
ASSESSING THE PERFORMANCE AND ENERGY USAGE OF MULTI-CPUS, MULTI-CORE AND MANY...
ijdpsjournal
 
Ad

More from PingCAP (20)

[Paper Reading] Efficient Query Processing with Optimistically Compressed Has...
[Paper Reading] Efficient Query Processing with Optimistically Compressed Has...[Paper Reading] Efficient Query Processing with Optimistically Compressed Has...
[Paper Reading] Efficient Query Processing with Optimistically Compressed Has...
PingCAP
 
[Paper Reading]KVSSD: Close integration of LSM trees and flash translation la...
[Paper Reading]KVSSD: Close integration of LSM trees and flash translation la...[Paper Reading]KVSSD: Close integration of LSM trees and flash translation la...
[Paper Reading]KVSSD: Close integration of LSM trees and flash translation la...
PingCAP
 
[Paper Reading]Chucky: A Succinct Cuckoo Filter for LSM-Tree
[Paper Reading]Chucky: A Succinct Cuckoo Filter for LSM-Tree[Paper Reading]Chucky: A Succinct Cuckoo Filter for LSM-Tree
[Paper Reading]Chucky: A Succinct Cuckoo Filter for LSM-Tree
PingCAP
 
[Paper Reading]The Bw-Tree: A B-tree for New Hardware Platforms
[Paper Reading]The Bw-Tree: A B-tree for New Hardware Platforms[Paper Reading]The Bw-Tree: A B-tree for New Hardware Platforms
[Paper Reading]The Bw-Tree: A B-tree for New Hardware Platforms
PingCAP
 
[Paper Reading] QAGen: Generating query-aware test databases
[Paper Reading] QAGen: Generating query-aware test databases[Paper Reading] QAGen: Generating query-aware test databases
[Paper Reading] QAGen: Generating query-aware test databases
PingCAP
 
[Paper Reading] Leases: An Efficient Fault-Tolerant Mechanism for Distribute...
[Paper Reading]  Leases: An Efficient Fault-Tolerant Mechanism for Distribute...[Paper Reading]  Leases: An Efficient Fault-Tolerant Mechanism for Distribute...
[Paper Reading] Leases: An Efficient Fault-Tolerant Mechanism for Distribute...
PingCAP
 
[Paperreading] Paxos made easy (by sen han)
[Paperreading]  Paxos made easy (by sen han)[Paperreading]  Paxos made easy (by sen han)
[Paperreading] Paxos made easy (by sen han)
PingCAP
 
[Paper Reading] Generalized Sub-Query Fusion for Eliminating Redundant I/O fr...
[Paper Reading] Generalized Sub-Query Fusion for Eliminating Redundant I/O fr...[Paper Reading] Generalized Sub-Query Fusion for Eliminating Redundant I/O fr...
[Paper Reading] Generalized Sub-Query Fusion for Eliminating Redundant I/O fr...
PingCAP
 
The Dark Side Of Go -- Go runtime related problems in TiDB in production
The Dark Side Of Go -- Go runtime related problems in TiDB  in productionThe Dark Side Of Go -- Go runtime related problems in TiDB  in production
The Dark Side Of Go -- Go runtime related problems in TiDB in production
PingCAP
 
TiDB DevCon 2020 Opening Keynote
TiDB DevCon 2020 Opening Keynote TiDB DevCon 2020 Opening Keynote
TiDB DevCon 2020 Opening Keynote
PingCAP
 
Finding Logic Bugs in Database Management Systems
Finding Logic Bugs in Database Management SystemsFinding Logic Bugs in Database Management Systems
Finding Logic Bugs in Database Management Systems
PingCAP
 
Chaos Practice in PingCAP
Chaos Practice in PingCAPChaos Practice in PingCAP
Chaos Practice in PingCAP
PingCAP
 
TiDB at PayPay
TiDB at PayPayTiDB at PayPay
TiDB at PayPay
PingCAP
 
Paper Reading: FPTree
Paper Reading: FPTreePaper Reading: FPTree
Paper Reading: FPTree
PingCAP
 
Paper Reading: Smooth Scan
Paper Reading: Smooth ScanPaper Reading: Smooth Scan
Paper Reading: Smooth Scan
PingCAP
 
Paper Reading: Flexible Paxos
Paper Reading: Flexible PaxosPaper Reading: Flexible Paxos
Paper Reading: Flexible Paxos
PingCAP
 
Paper reading: Cost-based Query Transformation in Oracle
Paper reading: Cost-based Query Transformation in OraclePaper reading: Cost-based Query Transformation in Oracle
Paper reading: Cost-based Query Transformation in Oracle
PingCAP
 
Paper reading: HashKV and beyond
Paper reading: HashKV and beyondPaper reading: HashKV and beyond
Paper reading: HashKV and beyond
PingCAP
 
Paper Reading: Pessimistic Cardinality Estimation
Paper Reading: Pessimistic Cardinality EstimationPaper Reading: Pessimistic Cardinality Estimation
Paper Reading: Pessimistic Cardinality Estimation
PingCAP
 
Building a transactional key-value store that scales to 100+ nodes (percona l...
Building a transactional key-value store that scales to 100+ nodes (percona l...Building a transactional key-value store that scales to 100+ nodes (percona l...
Building a transactional key-value store that scales to 100+ nodes (percona l...
PingCAP
 
[Paper Reading] Efficient Query Processing with Optimistically Compressed Has...
[Paper Reading] Efficient Query Processing with Optimistically Compressed Has...[Paper Reading] Efficient Query Processing with Optimistically Compressed Has...
[Paper Reading] Efficient Query Processing with Optimistically Compressed Has...
PingCAP
 
[Paper Reading]KVSSD: Close integration of LSM trees and flash translation la...
[Paper Reading]KVSSD: Close integration of LSM trees and flash translation la...[Paper Reading]KVSSD: Close integration of LSM trees and flash translation la...
[Paper Reading]KVSSD: Close integration of LSM trees and flash translation la...
PingCAP
 
[Paper Reading]Chucky: A Succinct Cuckoo Filter for LSM-Tree
[Paper Reading]Chucky: A Succinct Cuckoo Filter for LSM-Tree[Paper Reading]Chucky: A Succinct Cuckoo Filter for LSM-Tree
[Paper Reading]Chucky: A Succinct Cuckoo Filter for LSM-Tree
PingCAP
 
[Paper Reading]The Bw-Tree: A B-tree for New Hardware Platforms
[Paper Reading]The Bw-Tree: A B-tree for New Hardware Platforms[Paper Reading]The Bw-Tree: A B-tree for New Hardware Platforms
[Paper Reading]The Bw-Tree: A B-tree for New Hardware Platforms
PingCAP
 
[Paper Reading] QAGen: Generating query-aware test databases
[Paper Reading] QAGen: Generating query-aware test databases[Paper Reading] QAGen: Generating query-aware test databases
[Paper Reading] QAGen: Generating query-aware test databases
PingCAP
 
[Paper Reading] Leases: An Efficient Fault-Tolerant Mechanism for Distribute...
[Paper Reading]  Leases: An Efficient Fault-Tolerant Mechanism for Distribute...[Paper Reading]  Leases: An Efficient Fault-Tolerant Mechanism for Distribute...
[Paper Reading] Leases: An Efficient Fault-Tolerant Mechanism for Distribute...
PingCAP
 
[Paperreading] Paxos made easy (by sen han)
[Paperreading]  Paxos made easy (by sen han)[Paperreading]  Paxos made easy (by sen han)
[Paperreading] Paxos made easy (by sen han)
PingCAP
 
[Paper Reading] Generalized Sub-Query Fusion for Eliminating Redundant I/O fr...
[Paper Reading] Generalized Sub-Query Fusion for Eliminating Redundant I/O fr...[Paper Reading] Generalized Sub-Query Fusion for Eliminating Redundant I/O fr...
[Paper Reading] Generalized Sub-Query Fusion for Eliminating Redundant I/O fr...
PingCAP
 
The Dark Side Of Go -- Go runtime related problems in TiDB in production
The Dark Side Of Go -- Go runtime related problems in TiDB  in productionThe Dark Side Of Go -- Go runtime related problems in TiDB  in production
The Dark Side Of Go -- Go runtime related problems in TiDB in production
PingCAP
 
TiDB DevCon 2020 Opening Keynote
TiDB DevCon 2020 Opening Keynote TiDB DevCon 2020 Opening Keynote
TiDB DevCon 2020 Opening Keynote
PingCAP
 
Finding Logic Bugs in Database Management Systems
Finding Logic Bugs in Database Management SystemsFinding Logic Bugs in Database Management Systems
Finding Logic Bugs in Database Management Systems
PingCAP
 
Chaos Practice in PingCAP
Chaos Practice in PingCAPChaos Practice in PingCAP
Chaos Practice in PingCAP
PingCAP
 
TiDB at PayPay
TiDB at PayPayTiDB at PayPay
TiDB at PayPay
PingCAP
 
Paper Reading: FPTree
Paper Reading: FPTreePaper Reading: FPTree
Paper Reading: FPTree
PingCAP
 
Paper Reading: Smooth Scan
Paper Reading: Smooth ScanPaper Reading: Smooth Scan
Paper Reading: Smooth Scan
PingCAP
 
Paper Reading: Flexible Paxos
Paper Reading: Flexible PaxosPaper Reading: Flexible Paxos
Paper Reading: Flexible Paxos
PingCAP
 
Paper reading: Cost-based Query Transformation in Oracle
Paper reading: Cost-based Query Transformation in OraclePaper reading: Cost-based Query Transformation in Oracle
Paper reading: Cost-based Query Transformation in Oracle
PingCAP
 
Paper reading: HashKV and beyond
Paper reading: HashKV and beyondPaper reading: HashKV and beyond
Paper reading: HashKV and beyond
PingCAP
 
Paper Reading: Pessimistic Cardinality Estimation
Paper Reading: Pessimistic Cardinality EstimationPaper Reading: Pessimistic Cardinality Estimation
Paper Reading: Pessimistic Cardinality Estimation
PingCAP
 
Building a transactional key-value store that scales to 100+ nodes (percona l...
Building a transactional key-value store that scales to 100+ nodes (percona l...Building a transactional key-value store that scales to 100+ nodes (percona l...
Building a transactional key-value store that scales to 100+ nodes (percona l...
PingCAP
 
Ad

Recently uploaded (20)

The Evolution of Meme Coins A New Era for Digital Currency ppt.pdf
The Evolution of Meme Coins A New Era for Digital Currency ppt.pdfThe Evolution of Meme Coins A New Era for Digital Currency ppt.pdf
The Evolution of Meme Coins A New Era for Digital Currency ppt.pdf
Abi john
 
DevOpsDays Atlanta 2025 - Building 10x Development Organizations.pptx
DevOpsDays Atlanta 2025 - Building 10x Development Organizations.pptxDevOpsDays Atlanta 2025 - Building 10x Development Organizations.pptx
DevOpsDays Atlanta 2025 - Building 10x Development Organizations.pptx
Justin Reock
 
Technology Trends in 2025: AI and Big Data Analytics
Technology Trends in 2025: AI and Big Data AnalyticsTechnology Trends in 2025: AI and Big Data Analytics
Technology Trends in 2025: AI and Big Data Analytics
InData Labs
 
TrustArc Webinar: Consumer Expectations vs Corporate Realities on Data Broker...
TrustArc Webinar: Consumer Expectations vs Corporate Realities on Data Broker...TrustArc Webinar: Consumer Expectations vs Corporate Realities on Data Broker...
TrustArc Webinar: Consumer Expectations vs Corporate Realities on Data Broker...
TrustArc
 
Linux Professional Institute LPIC-1 Exam.pdf
Linux Professional Institute LPIC-1 Exam.pdfLinux Professional Institute LPIC-1 Exam.pdf
Linux Professional Institute LPIC-1 Exam.pdf
RHCSA Guru
 
tecnologias de las primeras civilizaciones.pdf
tecnologias de las primeras civilizaciones.pdftecnologias de las primeras civilizaciones.pdf
tecnologias de las primeras civilizaciones.pdf
fjgm517
 
Procurement Insights Cost To Value Guide.pptx
Procurement Insights Cost To Value Guide.pptxProcurement Insights Cost To Value Guide.pptx
Procurement Insights Cost To Value Guide.pptx
Jon Hansen
 
Transcript: #StandardsGoals for 2025: Standards & certification roundup - Tec...
Transcript: #StandardsGoals for 2025: Standards & certification roundup - Tec...Transcript: #StandardsGoals for 2025: Standards & certification roundup - Tec...
Transcript: #StandardsGoals for 2025: Standards & certification roundup - Tec...
BookNet Canada
 
Designing Low-Latency Systems with Rust and ScyllaDB: An Architectural Deep Dive
Designing Low-Latency Systems with Rust and ScyllaDB: An Architectural Deep DiveDesigning Low-Latency Systems with Rust and ScyllaDB: An Architectural Deep Dive
Designing Low-Latency Systems with Rust and ScyllaDB: An Architectural Deep Dive
ScyllaDB
 
What is Model Context Protocol(MCP) - The new technology for communication bw...
What is Model Context Protocol(MCP) - The new technology for communication bw...What is Model Context Protocol(MCP) - The new technology for communication bw...
What is Model Context Protocol(MCP) - The new technology for communication bw...
Vishnu Singh Chundawat
 
Role of Data Annotation Services in AI-Powered Manufacturing
Role of Data Annotation Services in AI-Powered ManufacturingRole of Data Annotation Services in AI-Powered Manufacturing
Role of Data Annotation Services in AI-Powered Manufacturing
Andrew Leo
 
Noah Loul Shares 5 Steps to Implement AI Agents for Maximum Business Efficien...
Noah Loul Shares 5 Steps to Implement AI Agents for Maximum Business Efficien...Noah Loul Shares 5 Steps to Implement AI Agents for Maximum Business Efficien...
Noah Loul Shares 5 Steps to Implement AI Agents for Maximum Business Efficien...
Noah Loul
 
TrsLabs - Fintech Product & Business Consulting
TrsLabs - Fintech Product & Business ConsultingTrsLabs - Fintech Product & Business Consulting
TrsLabs - Fintech Product & Business Consulting
Trs Labs
 
Build Your Own Copilot & Agents For Devs
Build Your Own Copilot & Agents For DevsBuild Your Own Copilot & Agents For Devs
Build Your Own Copilot & Agents For Devs
Brian McKeiver
 
Complete Guide to Advanced Logistics Management Software in Riyadh.pdf
Complete Guide to Advanced Logistics Management Software in Riyadh.pdfComplete Guide to Advanced Logistics Management Software in Riyadh.pdf
Complete Guide to Advanced Logistics Management Software in Riyadh.pdf
Software Company
 
Increasing Retail Store Efficiency How can Planograms Save Time and Money.pptx
Increasing Retail Store Efficiency How can Planograms Save Time and Money.pptxIncreasing Retail Store Efficiency How can Planograms Save Time and Money.pptx
Increasing Retail Store Efficiency How can Planograms Save Time and Money.pptx
Anoop Ashok
 
Generative Artificial Intelligence (GenAI) in Business
Generative Artificial Intelligence (GenAI) in BusinessGenerative Artificial Intelligence (GenAI) in Business
Generative Artificial Intelligence (GenAI) in Business
Dr. Tathagat Varma
 
Heap, Types of Heap, Insertion and Deletion
Heap, Types of Heap, Insertion and DeletionHeap, Types of Heap, Insertion and Deletion
Heap, Types of Heap, Insertion and Deletion
Jaydeep Kale
 
Rusty Waters: Elevating Lakehouses Beyond Spark
Rusty Waters: Elevating Lakehouses Beyond SparkRusty Waters: Elevating Lakehouses Beyond Spark
Rusty Waters: Elevating Lakehouses Beyond Spark
carlyakerly1
 
#StandardsGoals for 2025: Standards & certification roundup - Tech Forum 2025
#StandardsGoals for 2025: Standards & certification roundup - Tech Forum 2025#StandardsGoals for 2025: Standards & certification roundup - Tech Forum 2025
#StandardsGoals for 2025: Standards & certification roundup - Tech Forum 2025
BookNet Canada
 
The Evolution of Meme Coins A New Era for Digital Currency ppt.pdf
The Evolution of Meme Coins A New Era for Digital Currency ppt.pdfThe Evolution of Meme Coins A New Era for Digital Currency ppt.pdf
The Evolution of Meme Coins A New Era for Digital Currency ppt.pdf
Abi john
 
DevOpsDays Atlanta 2025 - Building 10x Development Organizations.pptx
DevOpsDays Atlanta 2025 - Building 10x Development Organizations.pptxDevOpsDays Atlanta 2025 - Building 10x Development Organizations.pptx
DevOpsDays Atlanta 2025 - Building 10x Development Organizations.pptx
Justin Reock
 
Technology Trends in 2025: AI and Big Data Analytics
Technology Trends in 2025: AI and Big Data AnalyticsTechnology Trends in 2025: AI and Big Data Analytics
Technology Trends in 2025: AI and Big Data Analytics
InData Labs
 
TrustArc Webinar: Consumer Expectations vs Corporate Realities on Data Broker...
TrustArc Webinar: Consumer Expectations vs Corporate Realities on Data Broker...TrustArc Webinar: Consumer Expectations vs Corporate Realities on Data Broker...
TrustArc Webinar: Consumer Expectations vs Corporate Realities on Data Broker...
TrustArc
 
Linux Professional Institute LPIC-1 Exam.pdf
Linux Professional Institute LPIC-1 Exam.pdfLinux Professional Institute LPIC-1 Exam.pdf
Linux Professional Institute LPIC-1 Exam.pdf
RHCSA Guru
 
tecnologias de las primeras civilizaciones.pdf
tecnologias de las primeras civilizaciones.pdftecnologias de las primeras civilizaciones.pdf
tecnologias de las primeras civilizaciones.pdf
fjgm517
 
Procurement Insights Cost To Value Guide.pptx
Procurement Insights Cost To Value Guide.pptxProcurement Insights Cost To Value Guide.pptx
Procurement Insights Cost To Value Guide.pptx
Jon Hansen
 
Transcript: #StandardsGoals for 2025: Standards & certification roundup - Tec...
Transcript: #StandardsGoals for 2025: Standards & certification roundup - Tec...Transcript: #StandardsGoals for 2025: Standards & certification roundup - Tec...
Transcript: #StandardsGoals for 2025: Standards & certification roundup - Tec...
BookNet Canada
 
Designing Low-Latency Systems with Rust and ScyllaDB: An Architectural Deep Dive
Designing Low-Latency Systems with Rust and ScyllaDB: An Architectural Deep DiveDesigning Low-Latency Systems with Rust and ScyllaDB: An Architectural Deep Dive
Designing Low-Latency Systems with Rust and ScyllaDB: An Architectural Deep Dive
ScyllaDB
 
What is Model Context Protocol(MCP) - The new technology for communication bw...
What is Model Context Protocol(MCP) - The new technology for communication bw...What is Model Context Protocol(MCP) - The new technology for communication bw...
What is Model Context Protocol(MCP) - The new technology for communication bw...
Vishnu Singh Chundawat
 
Role of Data Annotation Services in AI-Powered Manufacturing
Role of Data Annotation Services in AI-Powered ManufacturingRole of Data Annotation Services in AI-Powered Manufacturing
Role of Data Annotation Services in AI-Powered Manufacturing
Andrew Leo
 
Noah Loul Shares 5 Steps to Implement AI Agents for Maximum Business Efficien...
Noah Loul Shares 5 Steps to Implement AI Agents for Maximum Business Efficien...Noah Loul Shares 5 Steps to Implement AI Agents for Maximum Business Efficien...
Noah Loul Shares 5 Steps to Implement AI Agents for Maximum Business Efficien...
Noah Loul
 
TrsLabs - Fintech Product & Business Consulting
TrsLabs - Fintech Product & Business ConsultingTrsLabs - Fintech Product & Business Consulting
TrsLabs - Fintech Product & Business Consulting
Trs Labs
 
Build Your Own Copilot & Agents For Devs
Build Your Own Copilot & Agents For DevsBuild Your Own Copilot & Agents For Devs
Build Your Own Copilot & Agents For Devs
Brian McKeiver
 
Complete Guide to Advanced Logistics Management Software in Riyadh.pdf
Complete Guide to Advanced Logistics Management Software in Riyadh.pdfComplete Guide to Advanced Logistics Management Software in Riyadh.pdf
Complete Guide to Advanced Logistics Management Software in Riyadh.pdf
Software Company
 
Increasing Retail Store Efficiency How can Planograms Save Time and Money.pptx
Increasing Retail Store Efficiency How can Planograms Save Time and Money.pptxIncreasing Retail Store Efficiency How can Planograms Save Time and Money.pptx
Increasing Retail Store Efficiency How can Planograms Save Time and Money.pptx
Anoop Ashok
 
Generative Artificial Intelligence (GenAI) in Business
Generative Artificial Intelligence (GenAI) in BusinessGenerative Artificial Intelligence (GenAI) in Business
Generative Artificial Intelligence (GenAI) in Business
Dr. Tathagat Varma
 
Heap, Types of Heap, Insertion and Deletion
Heap, Types of Heap, Insertion and DeletionHeap, Types of Heap, Insertion and Deletion
Heap, Types of Heap, Insertion and Deletion
Jaydeep Kale
 
Rusty Waters: Elevating Lakehouses Beyond Spark
Rusty Waters: Elevating Lakehouses Beyond SparkRusty Waters: Elevating Lakehouses Beyond Spark
Rusty Waters: Elevating Lakehouses Beyond Spark
carlyakerly1
 
#StandardsGoals for 2025: Standards & certification roundup - Tech Forum 2025
#StandardsGoals for 2025: Standards & certification roundup - Tech Forum 2025#StandardsGoals for 2025: Standards & certification roundup - Tech Forum 2025
#StandardsGoals for 2025: Standards & certification roundup - Tech Forum 2025
BookNet Canada
 

[Paper Reading]Orca: A Modular Query Optimizer Architecture for Big Data

  • 1. Paper Reading Orca: A Modular Query Optimizer Architecture for Big Data Presented by Weizhen Wang
  • 2. Background In this paper we present the architecture of Orca, the new query optimizer for all Pivotal data management products, including Pivotal Greenplum Database and Pivotal HAWQ.
  • 3. ORCA = GPORCA = Greenplum ORCA = ... Jul 2015, PQO: Pivotal Query Optimizer (Released in GPDB 4.3.5.0) Jun 2014, ORCA: SIGMOD 2014 Paper https://github.com/greenplum-db/gporca In source code GPOPT src/backend/gpopt psql: select gp_opt_version();
  • 4. High level GPDB architecture • Storage and processing of large amounts of data are handledby distributing the load across several servers or hosts to create an array of individual databases. all working together to present a single database image. • The master is the entry point to GPDB, where clients connect and submit SQL statements. The master coordinates work with other database instances, called segments, to handle data processing and storage. • When a query is submitted to the master, it is optimized and broken into smaller components dispatched to segments to work together on delivering the final results. • The interconnect is the networking layer responsible for inter-process communication between the segments. The interconnect uses a standard Gigabit Ethernet switching fabric.
  • 5. High level GPDB architecture
  • 6. GPDB Using Planner (`set optimizer=off`)
  • 7. GPDB Using GPORCA (`set optimizer=on`)
  • 8. Orca architecture DXL: Data exchange Language
  • 9. Interaction of Orca with database system
  • 10. Five Optimization Steps in Orca ● [Step 0] Pre-Process: e.g., predicates pushdown ● [Step 1] Exploration: All equivalent logical plans ● [Step 2] Statistics Derivation: histograms ● [Step 3] Implementation: Logical to Physical ● [Step 4] MPP Optimization: Enforcing and Costing
  • 11. Step 0 Pre-Process with 25 iterations (TL, DR)
  • 12. Step 1 Exploration 1/2: Memoization Compact in-memory data structure capturing plan space: Group: Container of equivalent expressions Group Expression: Operator that has other groups as its children
  • 13. Step 1 Exploration 2/2: Transformation
  • 14. Step 2 Statistics Derivation
  • 16. Step 4 MPP Optimization 1/3 Requirement
  • 17. Step 4 MPP Optimization 2/3 Cost
  • 18. Step 4 MPP Optimization 3/3 Enforcement
  • 20. Parallel Query Optimization Exp(g): Generate logically equivalent expressions of all group expressions in group g. Exp(gexpr): Generate logically equivalent expressions of a group expression gexpr. Imp(g): Generate implementations of all group expressions in group g. Imp(gexpr): Generate implementation alternatives of a group expression gexpr. Opt(g, req): Return the plan with the least estimated cost that is rooted by an operator in group g and satises optimization request req. Opt(gexpr, req): Return the plan with the least estimated cost that is rooted by gexpr and satises optimization request req. Xform(gexpr, t): Transform group expression gexpr using rule t. Optimization process is broken to small work units called optimization jobs. Orca currently has seven different types of optimization jobs:
  • 22. Test
  • 23. Minimal Repros AMPERe is a tool for Automatic capture of Minimal Portable and Executable Repros.
  • 25. TAQO
  • 26. MEASURING ACCURACY • Discordance of plan pairs. • Relevance of plan • Pairwise distance
  • 28. PingCAP.com TAQO components ● Configuration Generator ● Execution Tracker ● Plan Deduplicator ● Ranker
  • 29. Great Query Performance of Orca vs Planner (TPC-DS 10TB)