GraphFrames: Graph Queries In Spark SQL
4 likes5,718 views
GraphFrames provides a unified API for graph queries and algorithms in Apache Spark SQL. It translates graph patterns and algorithms to relational operations optimized by the Spark SQL query optimizer. Materialized views can greatly improve performance of graph queries by enabling efficient join elimination and reordering. An evaluation found GraphFrames outperforms Neo4j for unanchored queries and approaches performance of GraphX for graph algorithms using whole-stage code generation in Spark SQL. Future work includes automatically suggesting optimal views and exploiting data partitioning.
1 of 20
Downloaded 185 times
![Graph Algorithms vs. Graph Queries
Graph Algorithm: PageRank
// Iterate until convergence
wikipedia.pregel(
sendMsg = { e =>
e.sendToDst(e.srcRank * e.weight)
},
mergeMsg = _ + _,
vprog = { (id, oldRank, msgSum) =>
0.15 + 0.85 * msgSum
})
Graph Query: Wikipedia Collaborators
wikipedia.find(
"(u1)-[e11]->(article1);
(u2)-[e21]->(article1);
(u1)-[e12]->(article2);
(u2)-[e22]->(article2)")
.select(
"*",
"e11.date – e21.date".as("d1"),
"e12.date – e22.date".as("d2"))
.sort("d1 + d2".desc).take(10)](https://image.slidesharecdn.com/4aankurdave-160614210556/85/GraphFrames-Graph-Queries-In-Spark-SQL-5-320.jpg)
Ad
Recommended
Apache Iceberg: An Architectural Look Under the Covers
Apache Iceberg: An Architectural Look Under the CoversScyllaDB The document provides an overview of Apache Iceberg, a new table format designed to enhance data lakehouse operations by improving performance, consistency, and efficiency. It outlines the architecture of Iceberg, its components like metadata and manifest files, and the benefits it offers over traditional formats like Hive. Additionally, it includes practical examples of how Iceberg tables are created, updated, and queried, demonstrating its advantages such as faster query execution and better support for evolving data schemas.
Observability for Data Pipelines With OpenLineage
Observability for Data Pipelines With OpenLineageDatabricks The document discusses the importance of metadata in data ecosystems and introduces OpenLineage, an open standard for lineage collection. It highlights the need for effective metadata management to avoid duplication and enhance collaboration across data pipelines, with an emphasis on integrations and observability in Spark. The document also outlines the core model and facets for metadata, detailing how OpenLineage and Marquez can facilitate understanding of data dependencies and support troubleshooting efforts.
Spark SQL
Spark SQLJoud Khattab The document summarizes Spark SQL, which is a Spark module for structured data processing. It introduces key concepts like RDDs, DataFrames, and interacting with data sources. The architecture of Spark SQL is explained, including how it works with different languages and data sources through its schema RDD abstraction. Features of Spark SQL are covered such as its integration with Spark programs, unified data access, compatibility with Hive, and standard connectivity.
Apache Spark Internals
Apache Spark InternalsKnoldus Inc. The document outlines the architecture and internal workings of Apache Spark, detailing components such as the master node, worker nodes, executors, and the driver program. It explains the concepts of tasks, stages, and jobs, along with the execution workflow involving a Directed Acyclic Graph (DAG). The presentation includes a demo and concludes with a Q&A session.
Spark SQL Join Improvement at Facebook
Spark SQL Join Improvement at FacebookDatabricks The document discusses improvements to Spark SQL joins, focusing on enhancements for shuffled hash joins, including full outer join support and proposing the use of bloom filters to optimize performance. It details specific changes implemented in Spark 3.1, such as code generation support and strategies for better handling large data sizes. Additionally, it outlines future work, including optimizing join strategies based on historical data.
Hive Bucketing in Apache Spark with Tejas Patil
Hive Bucketing in Apache Spark with Tejas PatilDatabricks The document discusses hive bucketing in Apache Spark, including the reasons for using bucketing, potential performance improvements, and the differences between Spark's and Hive's bucketing semantics. It highlights the inefficiencies of shuffling during joins and provides guidelines for when to implement bucketing to enhance performance. Additionally, it outlines Spark's support for creating and managing bucketed tables and the associated SQL planner improvements.
On Improving Broadcast Joins in Apache Spark SQL
On Improving Broadcast Joins in Apache Spark SQLDatabricks - The document discusses improving broadcast joins in Apache Spark SQL, which are more efficient than shuffle joins when the broadcasted data fits in memory.
- Experimenting with increasing the broadcast threshold showed that executor-side broadcasting performs better than driver-side broadcasting by avoiding data shuffling to the driver.
- Comparing the cost models of shuffle joins and broadcast joins showed that shuffle joins perform better with more cores while broadcast joins perform better when the size difference between tables is larger.
- Applying these techniques to joins in Workday HR customer data pipelines showed that increasing the broadcast threshold did not always improve performance due to the presence of self-joins and outer joins.
Productizing Structured Streaming Jobs
Productizing Structured Streaming JobsDatabricks The document discusses the operational aspects of productizing structured streaming jobs using Apache Spark, emphasizing testing, monitoring, deploying, and updating these jobs. It outlines strategies for testing with a focus on leveraging Spark's stream test harness, and the importance of maintaining a staging environment to ensure reliable deployments. Additionally, it covers monitoring techniques to track query progress and performance, and deployment considerations for handling multiple streaming sources efficiently.
Understanding Query Plans and Spark UIs
Understanding Query Plans and Spark UIsDatabricks The document presented by Xiao Li at the Spark + AI Summit covers key aspects of understanding query plans and Spark UI, focusing on performance optimization and execution tracking. It discusses the differences between various query execution plans, tips for improving Spark queries, and the advantages of using Delta Lake with Apache Spark for better metadata handling and performance. The presentation concludes with resources for further learning and data on Delta Lake's processing statistics.
How to Actually Tune Your Spark Jobs So They Work
How to Actually Tune Your Spark Jobs So They WorkIlya Ganelin This document summarizes a USF Spark workshop that covers Spark internals and how to optimize Spark jobs. It discusses how Spark works with partitions, caching, serialization and shuffling data. It provides lessons on using less memory by partitioning wisely, avoiding shuffles, using the driver carefully, and caching strategically to speed up jobs. The workshop emphasizes understanding Spark and tuning configurations to improve performance and stability.
The Apache Spark File Format Ecosystem
The Apache Spark File Format EcosystemDatabricks The document explores the Spark file format ecosystem as presented by Vinoo Ganesh, CTO of Veraset, focusing on data storage solutions for OLTP and OLAP workflows. It covers various file formats such as CSV, JSON, Avro, ORC, and Parquet, detailing their characteristics and optimal use cases. The session aims to educate on file format configurations and their impact on data processing efficiency and access patterns.
Hadoop Backup and Disaster Recovery
Hadoop Backup and Disaster RecoveryCloudera, Inc. The document discusses backup and disaster recovery strategies for Hadoop. It focuses on protecting data sets stored in HDFS. HDFS uses data replication and checksums to protect against disk and node failures. Snapshots can protect against data corruption and accidental deletes. The document recommends copying data from the primary to secondary site for disaster recovery rather than teeing, and discusses considerations for large data movement like bandwidth needs and security. It also notes the importance of backing up metadata like Hive configurations along with core data.
Deep Dive: Memory Management in Apache Spark
Deep Dive: Memory Management in Apache SparkDatabricks The document discusses memory management challenges in Apache Spark, focusing on efficient use of execution and storage memory. It outlines strategies for handling memory contention across various scenarios, including static and dynamic memory assignment for tasks and operators. Additionally, it introduces features from Spark 1.6 onwards, emphasizing unified memory management and the benefits of tungsten's optimized data representation.
Apache Iceberg - A Table Format for Hige Analytic Datasets
Apache Iceberg - A Table Format for Hige Analytic DatasetsAlluxio, Inc. The document discusses Apache Iceberg, a scalable table format designed to improve data processing in big data environments. Iceberg addresses challenges related to data maintenance, performance consistency, and usability by providing atomic changes, efficient schema evolution, and time-travel capabilities. The key takeaway is Iceberg's potential to enhance the handling of large datasets across various data processing engines while ensuring reliability and simplicity.
Stage Level Scheduling Improving Big Data and AI Integration
Stage Level Scheduling Improving Big Data and AI IntegrationDatabricks The document discusses stage-level scheduling and resource allocation in Apache Spark to enhance big data and AI integration. It outlines various resource requirements such as executors, memory, and accelerators, while presenting benefits like improved hardware utilization and simplified application pipelines. Additionally, it introduces the RAPIDS Accelerator for Spark and distributed deep learning with Horovod, emphasizing performance optimizations and future enhancements.
Pyspark Tutorial | Introduction to Apache Spark with Python | PySpark Trainin...
Pyspark Tutorial | Introduction to Apache Spark with Python | PySpark Trainin...Edureka! The document provides an overview of PySpark, an open-source cluster-computing framework that integrates with Python for data science and machine learning applications. It covers the advantages of PySpark, installation steps, and fundamental concepts such as RDDs, DataFrames, and the Spark context. Additionally, it outlines functionalities like transformations, actions, and the MLlib machine learning API offered by Spark.
Dynamic Partition Pruning in Apache Spark
Dynamic Partition Pruning in Apache SparkDatabricks The document discusses the implementation of dynamic partition pruning in Apache Spark to enhance the performance of SQL analytics workloads. It outlines the benefits of this approach, leading to significant speedups in query execution, particularly with TPC-DS benchmarks like query 98. The innovation allows Spark to efficiently handle star-schema queries, reducing the need for complex ETL processes with denormalized tables.
Monitor Apache Spark 3 on Kubernetes using Metrics and Plugins
Monitor Apache Spark 3 on Kubernetes using Metrics and PluginsDatabricks The document discusses the monitoring of Apache Spark 3 on Kubernetes, detailing tools, metrics systems, and plugin functionalities for improved performance monitoring. Key topics include the Spark metrics ecosystem, visualization through Grafana, and extending Spark with custom plugins for metrics concerning resource usage. A performance dashboard is also presented, emphasizing real-time visualization, custom instrumentation, and lessons learned from deployment at CERN.
Adaptive Query Execution: Speeding Up Spark SQL at Runtime
Adaptive Query Execution: Speeding Up Spark SQL at RuntimeDatabricks The document discusses Adaptive Query Execution (AQE) in Spark SQL, a dynamic optimization feature that improves query performance by making adjustments based on runtime statistics. Key enhancements introduced in Spark 3.0 include dynamically coalescing shuffle partitions, switching join strategies, and optimizing skew joins based on partition sizes. The effectiveness of AQE is demonstrated through performance improvements in production environments, achieving significant speedups in various queries.
Presto At Arm Treasure Data - 2019 Updates
Presto At Arm Treasure Data - 2019 UpdatesTaro L. Saito The document is a presentation from the Presto Conference Tokyo 2019 by Kai Sasaki and Taro L. Saito from Arm Treasure Data, highlighting the advancements and architecture of their unified data platform, Treasure Data. It discusses the use of Presto for data processing, the integration of various storage architectures, and techniques for optimizing queries and resource management. Additionally, it covers the development of plugins, extensions, and challenges faced in data optimization and processing at scale.
Deep Dive into Spark SQL with Advanced Performance Tuning with Xiao Li & Wenc...
Deep Dive into Spark SQL with Advanced Performance Tuning with Xiao Li & Wenc...Databricks The document discusses the features and performance tuning of Apache Spark SQL, highlighting its capabilities in processing and analyzing diverse data sources efficiently. It details advanced features introduced in Spark 2.3 and best practices for optimizing query execution, including memory management and data source selection. Additionally, it introduces Databricks Delta as a solution for improving data management reliability and simplifying ETL processes within cloud data lakes.
Introduction to ML with Apache Spark MLlib
Introduction to ML with Apache Spark MLlibTaras Matyashovsky The document provides an overview of using Apache Spark's MLlib for various machine learning tasks, including feature extraction, classification, and clustering. It discusses the process of cleansing and preparing datasets, particularly with song lyrics, and demonstrates using algorithms like logistic regression. Additionally, it highlights the importance of existing libraries and frameworks that simplify machine learning implementation.
Apache Spark Streaming in K8s with ArgoCD & Spark Operator
Apache Spark Streaming in K8s with ArgoCD & Spark OperatorDatabricks The document discusses the integration of Spark Streaming into Kubernetes using ArgoCD and Spark Operator, detailing the deployment process, requirements, and benefits of the technology. It highlights the evolution from EMR to Kubernetes, outlining the advantages of fine-grained IAM roles and resource management. The presentation also emphasizes the importance of adopting Data DevOps for improved efficiency and collaboration across teams.
Strata sf - Amundsen presentation
Strata sf - Amundsen presentationTao Feng The document discusses the challenges of data discovery at Lyft, highlighting the inefficiencies faced by data scientists and the need for effective data management tools. It introduces Amundsen, a data discovery platform that improves data search, lineage, and network understanding by centralizing metadata and utilizing graph databases. The document concludes with an overview of Amundsen’s architecture and its impact on productivity for data professionals at Lyft.
Presto Summit 2018 - 09 - Netflix Iceberg
Presto Summit 2018 - 09 - Netflix Icebergkbajda The document presents an overview of Iceberg, a modern table format for big data, its benefits, and performance results at Netflix. It highlights the challenges faced with traditional Hive tables and how Iceberg improves efficiency and supports features like snapshot isolation, schema evolution, and atomic writes. The comparison with Raptor further illustrates Iceberg's scalability and integration capabilities with various data processing engines.
Enabling Vectorized Engine in Apache Spark
Enabling Vectorized Engine in Apache SparkKazuaki Ishizaki The document discusses enabling a vectorized engine in Apache Spark to improve performance through vectorization and SIMD (Single Instruction, Multiple Data) techniques. It covers limitations of the current Spark architecture regarding SIMD compatibility and introduces the Vector API as a solution for ensuring efficient SIMD code generation. The document also outlines various applications and comparisons of SIMD implementations, highlighting performance benchmarks between different approaches.
Spark graphx
Spark graphxCarol McDonald This document provides an introduction to GraphX, which is an Apache Spark component for graphs and graph-parallel computations. It describes different types of graphs like regular graphs, directed graphs, and property graphs. It shows how to create a property graph in GraphX by defining vertex and edge RDDs. It also demonstrates various graph operators that can be used to perform operations on graphs, such as finding the number of vertices/edges, degrees, longest paths, and top vertices by degree. The goal is to introduce the basics of representing and analyzing graph data with GraphX.
Amazon S3 Best Practice and Tuning for Hadoop/Spark in the Cloud
Amazon S3 Best Practice and Tuning for Hadoop/Spark in the CloudNoritaka Sekiyama This document provides an overview and summary of Amazon S3 best practices and tuning for Hadoop/Spark in the cloud. It discusses the relationship between Hadoop/Spark and S3, the differences between HDFS and S3 and their use cases, details on how S3 behaves from the perspective of Hadoop/Spark, well-known pitfalls and tunings related to S3 consistency and multipart uploads, and recent community activities related to S3. The presentation aims to help users optimize their use of S3 storage with Hadoop/Spark frameworks.
GraphFrames: Graph Queries in Spark SQL by Ankur Dave
GraphFrames: Graph Queries in Spark SQL by Ankur DaveSpark Summit GraphFrames provides a unified API for graph queries and algorithms in Spark SQL. It translates graph patterns and algorithms to relational operations optimized by the Spark SQL query optimizer. Materializing the right views, such as the triplet view for GraphX algorithms or user-defined views for queries, can improve performance. An evaluation shows GraphFrames outperforms Neo4j for unanchored queries and approaches GraphX performance for graph algorithms using Spark SQL. Future work includes automatically suggesting optimal views and exploiting attribute-based partitioning.
GraphFrames: DataFrame-based graphs for Apache® Spark™
GraphFrames: DataFrame-based graphs for Apache® Spark™Databricks GraphFrames is a library built on Apache Spark that simplifies graph processing by leveraging DataFrames for higher-level APIs and optimizations. It offers capabilities like motif finding, graph algorithms, and integrates seamlessly with Spark's existing functionalities while allowing for the use of Java, Python, and Scala. The document highlights its architecture, use cases, and a roadmap for future enhancements and community contributions.
More Related Content
What's hot (20)
Understanding Query Plans and Spark UIs
Understanding Query Plans and Spark UIsDatabricks The document presented by Xiao Li at the Spark + AI Summit covers key aspects of understanding query plans and Spark UI, focusing on performance optimization and execution tracking. It discusses the differences between various query execution plans, tips for improving Spark queries, and the advantages of using Delta Lake with Apache Spark for better metadata handling and performance. The presentation concludes with resources for further learning and data on Delta Lake's processing statistics.
How to Actually Tune Your Spark Jobs So They Work
How to Actually Tune Your Spark Jobs So They WorkIlya Ganelin This document summarizes a USF Spark workshop that covers Spark internals and how to optimize Spark jobs. It discusses how Spark works with partitions, caching, serialization and shuffling data. It provides lessons on using less memory by partitioning wisely, avoiding shuffles, using the driver carefully, and caching strategically to speed up jobs. The workshop emphasizes understanding Spark and tuning configurations to improve performance and stability.
The Apache Spark File Format Ecosystem
The Apache Spark File Format EcosystemDatabricks The document explores the Spark file format ecosystem as presented by Vinoo Ganesh, CTO of Veraset, focusing on data storage solutions for OLTP and OLAP workflows. It covers various file formats such as CSV, JSON, Avro, ORC, and Parquet, detailing their characteristics and optimal use cases. The session aims to educate on file format configurations and their impact on data processing efficiency and access patterns.
Hadoop Backup and Disaster Recovery
Hadoop Backup and Disaster RecoveryCloudera, Inc. The document discusses backup and disaster recovery strategies for Hadoop. It focuses on protecting data sets stored in HDFS. HDFS uses data replication and checksums to protect against disk and node failures. Snapshots can protect against data corruption and accidental deletes. The document recommends copying data from the primary to secondary site for disaster recovery rather than teeing, and discusses considerations for large data movement like bandwidth needs and security. It also notes the importance of backing up metadata like Hive configurations along with core data.
Deep Dive: Memory Management in Apache Spark
Deep Dive: Memory Management in Apache SparkDatabricks The document discusses memory management challenges in Apache Spark, focusing on efficient use of execution and storage memory. It outlines strategies for handling memory contention across various scenarios, including static and dynamic memory assignment for tasks and operators. Additionally, it introduces features from Spark 1.6 onwards, emphasizing unified memory management and the benefits of tungsten's optimized data representation.
Apache Iceberg - A Table Format for Hige Analytic Datasets
Apache Iceberg - A Table Format for Hige Analytic DatasetsAlluxio, Inc. The document discusses Apache Iceberg, a scalable table format designed to improve data processing in big data environments. Iceberg addresses challenges related to data maintenance, performance consistency, and usability by providing atomic changes, efficient schema evolution, and time-travel capabilities. The key takeaway is Iceberg's potential to enhance the handling of large datasets across various data processing engines while ensuring reliability and simplicity.
Stage Level Scheduling Improving Big Data and AI Integration
Stage Level Scheduling Improving Big Data and AI IntegrationDatabricks The document discusses stage-level scheduling and resource allocation in Apache Spark to enhance big data and AI integration. It outlines various resource requirements such as executors, memory, and accelerators, while presenting benefits like improved hardware utilization and simplified application pipelines. Additionally, it introduces the RAPIDS Accelerator for Spark and distributed deep learning with Horovod, emphasizing performance optimizations and future enhancements.
Pyspark Tutorial | Introduction to Apache Spark with Python | PySpark Trainin...
Pyspark Tutorial | Introduction to Apache Spark with Python | PySpark Trainin...Edureka! The document provides an overview of PySpark, an open-source cluster-computing framework that integrates with Python for data science and machine learning applications. It covers the advantages of PySpark, installation steps, and fundamental concepts such as RDDs, DataFrames, and the Spark context. Additionally, it outlines functionalities like transformations, actions, and the MLlib machine learning API offered by Spark.
Dynamic Partition Pruning in Apache Spark
Dynamic Partition Pruning in Apache SparkDatabricks The document discusses the implementation of dynamic partition pruning in Apache Spark to enhance the performance of SQL analytics workloads. It outlines the benefits of this approach, leading to significant speedups in query execution, particularly with TPC-DS benchmarks like query 98. The innovation allows Spark to efficiently handle star-schema queries, reducing the need for complex ETL processes with denormalized tables.
Monitor Apache Spark 3 on Kubernetes using Metrics and Plugins
Monitor Apache Spark 3 on Kubernetes using Metrics and PluginsDatabricks The document discusses the monitoring of Apache Spark 3 on Kubernetes, detailing tools, metrics systems, and plugin functionalities for improved performance monitoring. Key topics include the Spark metrics ecosystem, visualization through Grafana, and extending Spark with custom plugins for metrics concerning resource usage. A performance dashboard is also presented, emphasizing real-time visualization, custom instrumentation, and lessons learned from deployment at CERN.
Adaptive Query Execution: Speeding Up Spark SQL at Runtime
Adaptive Query Execution: Speeding Up Spark SQL at RuntimeDatabricks The document discusses Adaptive Query Execution (AQE) in Spark SQL, a dynamic optimization feature that improves query performance by making adjustments based on runtime statistics. Key enhancements introduced in Spark 3.0 include dynamically coalescing shuffle partitions, switching join strategies, and optimizing skew joins based on partition sizes. The effectiveness of AQE is demonstrated through performance improvements in production environments, achieving significant speedups in various queries.
Presto At Arm Treasure Data - 2019 Updates
Presto At Arm Treasure Data - 2019 UpdatesTaro L. Saito The document is a presentation from the Presto Conference Tokyo 2019 by Kai Sasaki and Taro L. Saito from Arm Treasure Data, highlighting the advancements and architecture of their unified data platform, Treasure Data. It discusses the use of Presto for data processing, the integration of various storage architectures, and techniques for optimizing queries and resource management. Additionally, it covers the development of plugins, extensions, and challenges faced in data optimization and processing at scale.
Deep Dive into Spark SQL with Advanced Performance Tuning with Xiao Li & Wenc...
Deep Dive into Spark SQL with Advanced Performance Tuning with Xiao Li & Wenc...Databricks The document discusses the features and performance tuning of Apache Spark SQL, highlighting its capabilities in processing and analyzing diverse data sources efficiently. It details advanced features introduced in Spark 2.3 and best practices for optimizing query execution, including memory management and data source selection. Additionally, it introduces Databricks Delta as a solution for improving data management reliability and simplifying ETL processes within cloud data lakes.
Introduction to ML with Apache Spark MLlib
Introduction to ML with Apache Spark MLlibTaras Matyashovsky The document provides an overview of using Apache Spark's MLlib for various machine learning tasks, including feature extraction, classification, and clustering. It discusses the process of cleansing and preparing datasets, particularly with song lyrics, and demonstrates using algorithms like logistic regression. Additionally, it highlights the importance of existing libraries and frameworks that simplify machine learning implementation.
Apache Spark Streaming in K8s with ArgoCD & Spark Operator
Apache Spark Streaming in K8s with ArgoCD & Spark OperatorDatabricks The document discusses the integration of Spark Streaming into Kubernetes using ArgoCD and Spark Operator, detailing the deployment process, requirements, and benefits of the technology. It highlights the evolution from EMR to Kubernetes, outlining the advantages of fine-grained IAM roles and resource management. The presentation also emphasizes the importance of adopting Data DevOps for improved efficiency and collaboration across teams.
Strata sf - Amundsen presentation
Strata sf - Amundsen presentationTao Feng The document discusses the challenges of data discovery at Lyft, highlighting the inefficiencies faced by data scientists and the need for effective data management tools. It introduces Amundsen, a data discovery platform that improves data search, lineage, and network understanding by centralizing metadata and utilizing graph databases. The document concludes with an overview of Amundsen’s architecture and its impact on productivity for data professionals at Lyft.
Presto Summit 2018 - 09 - Netflix Iceberg
Presto Summit 2018 - 09 - Netflix Icebergkbajda The document presents an overview of Iceberg, a modern table format for big data, its benefits, and performance results at Netflix. It highlights the challenges faced with traditional Hive tables and how Iceberg improves efficiency and supports features like snapshot isolation, schema evolution, and atomic writes. The comparison with Raptor further illustrates Iceberg's scalability and integration capabilities with various data processing engines.
Enabling Vectorized Engine in Apache Spark
Enabling Vectorized Engine in Apache SparkKazuaki Ishizaki The document discusses enabling a vectorized engine in Apache Spark to improve performance through vectorization and SIMD (Single Instruction, Multiple Data) techniques. It covers limitations of the current Spark architecture regarding SIMD compatibility and introduces the Vector API as a solution for ensuring efficient SIMD code generation. The document also outlines various applications and comparisons of SIMD implementations, highlighting performance benchmarks between different approaches.
Spark graphx
Spark graphxCarol McDonald This document provides an introduction to GraphX, which is an Apache Spark component for graphs and graph-parallel computations. It describes different types of graphs like regular graphs, directed graphs, and property graphs. It shows how to create a property graph in GraphX by defining vertex and edge RDDs. It also demonstrates various graph operators that can be used to perform operations on graphs, such as finding the number of vertices/edges, degrees, longest paths, and top vertices by degree. The goal is to introduce the basics of representing and analyzing graph data with GraphX.
Amazon S3 Best Practice and Tuning for Hadoop/Spark in the Cloud
Amazon S3 Best Practice and Tuning for Hadoop/Spark in the CloudNoritaka Sekiyama This document provides an overview and summary of Amazon S3 best practices and tuning for Hadoop/Spark in the cloud. It discusses the relationship between Hadoop/Spark and S3, the differences between HDFS and S3 and their use cases, details on how S3 behaves from the perspective of Hadoop/Spark, well-known pitfalls and tunings related to S3 consistency and multipart uploads, and recent community activities related to S3. The presentation aims to help users optimize their use of S3 storage with Hadoop/Spark frameworks.
Similar to GraphFrames: Graph Queries In Spark SQL (20)
GraphFrames: Graph Queries in Spark SQL by Ankur Dave
GraphFrames: Graph Queries in Spark SQL by Ankur DaveSpark Summit GraphFrames provides a unified API for graph queries and algorithms in Spark SQL. It translates graph patterns and algorithms to relational operations optimized by the Spark SQL query optimizer. Materializing the right views, such as the triplet view for GraphX algorithms or user-defined views for queries, can improve performance. An evaluation shows GraphFrames outperforms Neo4j for unanchored queries and approaches GraphX performance for graph algorithms using Spark SQL. Future work includes automatically suggesting optimal views and exploiting attribute-based partitioning.
GraphFrames: DataFrame-based graphs for Apache® Spark™
GraphFrames: DataFrame-based graphs for Apache® Spark™Databricks GraphFrames is a library built on Apache Spark that simplifies graph processing by leveraging DataFrames for higher-level APIs and optimizations. It offers capabilities like motif finding, graph algorithms, and integrates seamlessly with Spark's existing functionalities while allowing for the use of Java, Python, and Scala. The document highlights its architecture, use cases, and a roadmap for future enhancements and community contributions.
Web-Scale Graph Analytics with Apache® Spark™
Web-Scale Graph Analytics with Apache® Spark™Databricks The document discusses web-scale graph analytics using Apache Spark, highlighting the integration of GraphFrames, a library for handling dataframe-based graphs. It addresses key features, challenges, and implementations related to graph processing, including vertex indexing and connected components. Experiments demonstrate the performance of GraphFrames compared to GraphX in handling large datasets, emphasizing future improvements and collaborative offerings available through Databricks.
Graphs in data structures are non-linear data structures made up of a finite ...
Graphs in data structures are non-linear data structures made up of a finite ...bhargavi804095 GraphX is an integrated framework that combines data-parallel and graph-parallel analytics for efficient graph processing. It focuses on enabling users to express entire graph analytics pipelines seamlessly, minimizing data movement and facilitating the use of both tables and graphs as first-class objects. The system supports various graph algorithms and is optimized for performance, demonstrating significant speed enhancements compared to traditional systems.
What Makes Graph Queries Difficult?
What Makes Graph Queries Difficult?Gábor Szárnyas Graph queries and analytics pose several challenges. Graphs have an unstructured, connected nature that makes them difficult for computers to process efficiently. This is due to poor cache locality and difficulties in parallelization. Adding properties, types, weights, or global queries further increases complexity. There is also no consensus on a unified theory for graph processing, between relational algebra and linear algebra approaches. The speaker's PhD dissertation aims to address these challenges through contributions across different domains including databases, high-performance computing, network science, and software engineering.
Web-Scale Graph Analytics with Apache® Spark™
Web-Scale Graph Analytics with Apache® Spark™Databricks This document discusses web-scale graph analytics using Apache Spark and the GraphFrames library, including its features such as simplified interactive queries and support for motif-finding. It covers the advantages of using dataframes for graph processing, various algorithms for graph analysis, and the challenges faced in implementing graph computations, such as handling skewed joins and maintaining performance. The document also highlights comparative performance results of GraphFrames versus GraphX across different datasets.
Compiling openCypher graph queries with Spark Catalyst
Compiling openCypher graph queries with Spark CatalystGábor Szárnyas The document discusses the development of a scalable graph query engine utilizing OpenCypher and Spark Catalyst, focusing on incrementally maintaining views of large graphs with complex queries. It explains the use of a cache for efficient evaluation and highlights various research objectives, including support for incremental views and pattern matching in graph databases. Key research initiatives and benchmarks, such as the LDBC social network benchmark, are also addressed, showcasing the framework and tools supporting graph data management and analytics.
Distributed graph processing
Distributed graph processingBartosz Konieczny This document discusses challenges and approaches for working with graphs at scale in a distributed environment. It begins with an overview of graph terminology and representations. It then covers approaches for distributed storage of graphs using techniques like adjacency matrices, lists, and NoSQL databases. Methods for distributed graph processing are discussed, including vertex-centric, graph-centric, and streaming approaches. Graph query languages like Cypher, Gremlin and GraphQL are presented. The document also reviews algorithms like PageRank and tools for visualizing large graphs. It concludes by noting challenges like partitioning graphs across servers and providing real-time capabilities at large scale.
An excursion into Graph Analytics with Apache Spark GraphX
An excursion into Graph Analytics with Apache Spark GraphXKrishna Sankar The document discusses the use of Apache Spark's GraphX for graph processing, outlining its API landscape, algorithms, and real-world applications. It includes a tutorial agenda covering topics such as graph structures, community analysis, and case studies like the AlphaGo tweet analysis. Additionally, it highlights various graph processing frameworks and partitioning strategies to efficiently manage large datasets.
GraphFrames Access Methods in DSE Graph
GraphFrames Access Methods in DSE GraphJim Hatcher The document discusses Datastax's GraphFrames access methods, showcasing key concepts like OSS Spark GraphFrames, DSE GraphFrames, and various graph algorithms. It provides code examples for using Spark SQL and Tinkerpop/Gremlin for graph operations including shortest path calculations and motif finding. Additional resources and a demo dataset are also included to illustrate the practical applications of these graph technologies.
Ted Willke, Senior Principal Engineer & GM, Datacenter Group, Intel at MLconf SF
Ted Willke, Senior Principal Engineer & GM, Datacenter Group, Intel at MLconf SFMLconf The document discusses the evolution of graphs as a big data primitive, emphasizing their significance in various applications like product recommendations and social network analysis. It highlights the limitations of current graph tools, such as their lack of language bindings and streaming support, while advocating for the integration of graph processing with existing data systems. The talk aims to showcase how advancements in technology and workflows can position graphs as standard components in the big data landscape.
Graph Analytics in Spark
Graph Analytics in SparkPaco Nathan Graph analytics can be used to analyze a social graph constructed from email messages on the Spark user mailing list. Key metrics like PageRank, in-degrees, and strongly connected components can be computed using the GraphX API in Spark. For example, PageRank was computed on the 4Q2014 email graph, identifying the top contributors to the mailing list.
GraphX: Graph analytics for insights about developer communities
GraphX: Graph analytics for insights about developer communitiesPaco Nathan The document provides an overview of Graph Analytics in Spark. It discusses Spark components and key distinctions from MapReduce. It also covers GraphX terminology and examples of composing node and edge RDDs into a graph. The document provides examples of simple traversals and routing problems on graphs. It discusses using GraphX for topic modeling with LDA and provides further reading resources on GraphX, algebraic graph theory, and graph analysis tools and frameworks.
Challenging Web-Scale Graph Analytics with Apache Spark with Xiangrui Meng
Challenging Web-Scale Graph Analytics with Apache Spark with Xiangrui MengDatabricks The document discusses challenging web-scale graph analytics using Apache Spark, particularly focusing on the GraphFrames package introduced in 2016 which enhances graph processing capabilities. It highlights the transition from RDDs to DataFrames for improved performance, along with supported graph algorithms like PageRank and connected components. The presentation also outlines lessons learned from implementing these algorithms, including strategies for handling data skewness and optimizing performance in large-scale graph processing.
Challenging Web-Scale Graph Analytics with Apache Spark
Challenging Web-Scale Graph Analytics with Apache SparkDatabricks The document discusses graph analytics using Apache Spark's GraphFrames, a package that allows for optimized graph processing with DataFrames and supports various graph algorithms. It highlights lessons learned from implementing algorithms like connected components, addressing challenges such as data skew in graph iterations, and the advantages of using integral vertex IDs. The document also compares performance experiments between GraphFrames and GraphX, showing GraphFrames' efficiency for large datasets.
Processing Large Graphs
Processing Large GraphsNishant Gandhi This document discusses processing large graphs. It introduces graph processing with MapReduce and Apache Giraph. MapReduce algorithms for finding triangles and connected components in graphs are described. The limitations of MapReduce for graph processing are discussed. Alternative graph processing technologies including Neo4j, a graph database, are presented. A movie recommendation use case is demonstrated using Neo4j to find similar users and recommend unseen movies.
A quick review of Python and Graph Databases
A quick review of Python and Graph DatabasesNicholas Crouch The document provides an overview of using Python with graph databases, focusing primarily on Neo4j and its associated libraries, such as Py2neo and neo4j-rest-client, highlighting their features, performance, and use cases. It contrasts Neo4j with other graph databases like Titan and OrientDB, discussing their scalability, support, and potential drawbacks. Additionally, it touches on pure Python graph database implementations like GrapheekDB and AJGU, encouraging users to consider their specific needs when selecting a graph database.
Sparksee Technology overview
Sparksee Technology overviewSparsity Technologies The document provides an overview of the Sparksee graph database, a high-performance system designed for large-scale labeled and attributed multigraphs. It discusses Sparksee's architecture, performance metrics, and innovative storage techniques using bitmap structures, highlighting its scalability, efficiency, and support for complex graph queries. Additionally, it reviews comparison benchmarks against other databases, showcasing Sparksee's capabilities in handling extensive data with subsecond query responses.
8th TUC Meeting - Peter Boncz (CWI). Query Language Task Force status
8th TUC Meeting - Peter Boncz (CWI). Query Language Task Force statusLDBC council The GraphQL Task Force focuses on developing a robust query language for property graph data management systems, targeting essential features for use-cases like the LDBC's social network benchmark. Over its first year, the task force has outlined desired functionalities, evaluated existing languages, and discussed various technical aspects such as graph types, shortest paths, and relational graph querying. The group's progress included numerous case studies and proposals, with ongoing discussions about extending SQL and designing a native graph query language.
Graph processing at scale using spark & graph frames
Graph processing at scale using spark & graph framesRon Barabash The document discusses GraphFrames, a library for graph processing in Spark. It allows for both graph algorithms and graph queries using a unified API. Some key points made:
- GraphFrames provides a unified API for graph algorithms (e.g. connected components, PageRank) and graph queries in Scala, Java, and Python.
- It uses Spark SQL's Catalyst optimizer to translate graph queries into relational operations on DataFrames for efficient execution.
- An example algorithm discussed is connected components, where GraphFrames' implementation using small/big star operations converges faster than GraphX's naive approach on large graphs.
- Performance tests showed GraphFrames outperforms GraphX on connected components for graphs
Ad
More from Spark Summit (20)
FPGA-Based Acceleration Architecture for Spark SQL Qi Xie and Quanfu Wang
FPGA-Based Acceleration Architecture for Spark SQL Qi Xie and Quanfu Wang Spark Summit The document discusses an FPGA-based acceleration architecture for Spark SQL, detailing its components and advantages such as reduced execution times and CPU utilization improvements. It presents a performance comparison showing a significant speedup in query execution from 86 seconds to 44 seconds with FPGA acceleration. Additionally, it outlines future work, including expanding SQL expression support and configuring the FPGA SQL acceleration engine for more versatile application.
VEGAS: The Missing Matplotlib for Scala/Apache Spark with DB Tsai and Roger M...
VEGAS: The Missing Matplotlib for Scala/Apache Spark with DB Tsai and Roger M...Spark Summit Vegas is a visualization library for Scala, designed to fill the gap in the Scala/Spark ecosystem similar to Matplotlib in Python. It allows for declarative statistical visualizations with built-in operations like filtering and aggregation, enhancing productivity in machine learning experimentation. The library also supports various plot types, interactive selections, and is designed for seamless integration within notebooks.
Apache Spark Structured Streaming Helps Smart Manufacturing with Xiaochang Wu
Apache Spark Structured Streaming Helps Smart Manufacturing with Xiaochang WuSpark Summit The document outlines the use of Spark Structured Streaming to build a smart manufacturing IoT data platform, focusing on enhancing efficiency in PCB manufacturing through predictive fault repair and material tracking. It details architectural features, data processing capabilities, and handling of stateless and stateful operations, along with improvement proposals for more complex event processing. Key components include real-time analytics, device management, data visualization, and the support for various data types.
Improving Traffic Prediction Using Weather Data with Ramya Raghavendra
Improving Traffic Prediction Using Weather Data with Ramya RaghavendraSpark Summit The document outlines a research project by IBM to improve traffic prediction using weather data, highlighting the challenges of data collection and modeling. It details the machine learning processes used, including various algorithms like ARIMA and LSTM, to predict traffic flows based on historical traffic and weather data across multiple cities. The study demonstrates significant reductions in prediction errors, emphasizing the potential of predictive traffic systems to enhance commuting efficiency.
A Tale of Two Graph Frameworks on Spark: GraphFrames and Tinkerpop OLAP Artem...
A Tale of Two Graph Frameworks on Spark: GraphFrames and Tinkerpop OLAP Artem...Spark Summit The document compares two graph analytics frameworks, Tinkerpop and GraphFrames, used with Apache Spark, highlighting their distinct approaches. Tinkerpop is a flexible OLAP framework that operates on a node-centric model, while GraphFrames offers a relational structure and integrates closely with Spark's DataFrame API for optimization. The choice of framework depends on the complexity of traversals and the need for interoperability with external data sources.
No More Cumbersomeness: Automatic Predictive Modeling on Apache Spark Marcin ...
No More Cumbersomeness: Automatic Predictive Modeling on Apache Spark Marcin ...Spark Summit The document discusses automatic predictive modeling on Apache Spark, emphasizing the challenges and solutions associated with predictive modeling in industry. It highlights the advantages of the authors' technology that combines Spark with native machine learning engines to automate the process, providing accurate results in a fraction of the time typically required. The evaluation of their approach demonstrates competitive accuracy and efficiency across various datasets.
Apache Spark and Tensorflow as a Service with Jim Dowling
Apache Spark and Tensorflow as a Service with Jim DowlingSpark Summit The document discusses advancements in deep learning, particularly the integration of TensorFlow and Spark for distributed training, highlighting the need for robust data pipelines and hardware configurations. It outlines various models and frameworks, such as Horovod and TensorFlow on Spark, to enhance efficiency in training using multiple GPUs and optimization techniques. Additionally, it presents the AI hierarchy of needs and the role of Hopsworks in supporting machine learning workflows.
Apache Spark and Tensorflow as a Service with Jim Dowling
Apache Spark and Tensorflow as a Service with Jim DowlingSpark Summit The document discusses advances in distributed deep learning (DDL), highlighting the use of technologies such as TensorFlow and Spark for training large models efficiently. It details the AI hierarchy of needs, the integration of GPU resources, and various methods like allreduce for optimizing training times. Additionally, it presents the capabilities of Hopsworks, a platform for managing AI workflows and resources.
MMLSpark: Lessons from Building a SparkML-Compatible Machine Learning Library...
MMLSpark: Lessons from Building a SparkML-Compatible Machine Learning Library...Spark Summit The document discusses MMLSpark, a machine learning library designed for Apache Spark, detailing its goals, architecture, and lessons learned during development. Key aspects include enhancing the Spark ecosystem through integration, improving model management, and reducing overhead from user-defined functions while ensuring composability. An example use case highlights its application in automatic image classification for snow leopard conservation, saving time and increasing accuracy.
Next CERN Accelerator Logging Service with Jakub Wozniak
Next CERN Accelerator Logging Service with Jakub WozniakSpark Summit The document outlines the architecture and functionality of the Next CERN Accelerator Logging Service (nxcals), which improves upon the previous logging system by addressing scalability issues and enabling big data capabilities. It discusses the technologies used, such as Kafka, Hadoop, and Spark, along with the structured way to handle metadata and changing record schemas over time. Additionally, it describes various APIs for data ingestion and extraction to facilitate efficient data access and analysis.
Powering a Startup with Apache Spark with Kevin Kim
Powering a Startup with Apache Spark with Kevin KimSpark Summit The document discusses the evolution of a startup called 'Between', led by Kevin Kim, detailing its growth from 100 beta users in 2011 to 20 million downloads by 2016. It highlights the team's use of Apache Spark for big data processing, the infrastructure they have built, and their current hiring needs for data analysts and machine learning experts. The document also emphasizes the importance of data analysis for product development and marketing strategies, along with the ongoing challenges and future plans for expanding operations.
Improving Traffic Prediction Using Weather Datawith Ramya Raghavendra
Improving Traffic Prediction Using Weather Datawith Ramya RaghavendraSpark Summit The document discusses the development of a cognitive model by IBM Research that improves traffic prediction by integrating weather data, utilizing machine learning techniques like ARIMA, random forest, and LSTM. It highlights the significant impact of weather on traffic flow, emphasizes data collection challenges, and presents results from analyzing extensive traffic and weather records across multiple cities. The model aims to enhance commuter experiences by providing timely and precise traffic predictions, ultimately improving efficiency and reducing congestion.
Hiding Apache Spark Complexity for Fast Prototyping of Big Data Applications—...
Hiding Apache Spark Complexity for Fast Prototyping of Big Data Applications—...Spark Summit The document discusses the Everismoriarty platform, which simplifies the development of data analytics models using Apache Spark, facilitating collaboration between developers and data scientists. It outlines the platform's capabilities, including machine learning and data streaming, highlighting various work items and use cases such as dynamic supply networks and urban mobility. Additionally, it emphasizes the integration of Spark for efficient data processing and prototyping in developement environments.
How Nielsen Utilized Databricks for Large-Scale Research and Development with...
How Nielsen Utilized Databricks for Large-Scale Research and Development with...Spark Summit Nielsen used Databricks to test new digital advertising rating methodologies on a large scale. Databricks allowed Nielsen to run analyses on thousands of advertising campaigns using both small panel data and large production data. This identified edge cases and performance gains faster than traditional methods. Using Databricks reduced the time required to test and deploy improved rating methodologies to benefit Nielsen's clients.
Spline: Apache Spark Lineage not Only for the Banking Industry with Marek Nov...
Spline: Apache Spark Lineage not Only for the Banking Industry with Marek Nov...Spark Summit The document discusses the usage of Apache Spark lineage in Barclays Africa Group Limited to meet regulatory compliance and enhance data clarity. It introduces the Spline tool that enables visualization of Spark execution plans, emphasizing its functionality and future development plans. The presentation includes a demonstration of a use case analyzing beer consumption per capita in correlation with GDP across various countries.
Goal Based Data Production with Sim Simeonov
Goal Based Data Production with Sim SimeonovSpark Summit Simeon Simeonov discusses the concept of goal-based data production within the context of a data-powered culture, emphasizing the need for agile data production methods utilizing machine learning and AI. He highlights the complexity and inflexibility of traditional SQL abstractions and the advantages of a generative approach that enhances metadata queries and allows for real-time context resolution. The document advocates for a self-service model for data scientists and business users, aiming to improve productivity, flexibility, and collaboration through open-source solutions.
Preventing Revenue Leakage and Monitoring Distributed Systems with Machine Le...
Preventing Revenue Leakage and Monitoring Distributed Systems with Machine Le...Spark Summit Movile implemented machine learning to monitor their subscription and billing platform, utilizing data analysis to predict and prevent revenue leakage effectively. The project involved extensive data handling with millions of daily billing requests and various machine learning models, achieving significant accuracy improvements. Key outcomes include saving over $3 million and reducing recovery time from 6 hours to 1 hour, demonstrating a successful application of machine learning in operational efficiency.
Getting Ready to Use Redis with Apache Spark with Dvir Volk
Getting Ready to Use Redis with Apache Spark with Dvir VolkSpark Summit The document outlines a presentation on using Redis for real-time machine learning, focusing on Redis-ML integrated with Apache Spark. It covers the capabilities of Redis as a key-value store, the functionalities of Redis-ML for model serving, and a case study involving a recommendation system. The concluding remarks emphasize the advantages of combining Spark for training with Redis for efficient model serving, highlighting reduced resource costs and simplified machine learning lifecycle.
Deduplication and Author-Disambiguation of Streaming Records via Supervised M...
Deduplication and Author-Disambiguation of Streaming Records via Supervised M...Spark Summit The document discusses advancements in deduplication and author disambiguation of scholarly records using supervised models and content encoders within the Elsevier platform, emphasizing the importance of efficient data processing techniques. It outlines the challenges and solutions for managing peer-reviewed publications in Scopus, focusing on machine learning and cloud scalability for real-time document analysis. The implementation of algorithms like word2vec for document encoding aims to improve recommendation systems and personalized user experiences across various academic entities.
MatFast: In-Memory Distributed Matrix Computation Processing and Optimization...
MatFast: In-Memory Distributed Matrix Computation Processing and Optimization...Spark Summit The document presents MatFast, an in-memory distributed matrix computation platform optimized for processing queries over big matrix data, focused on applications like recommender systems and social network analysis. It discusses its implementation, optimization strategies, and comparison with existing systems in the Spark ecosystem, including data partitioning techniques for efficient execution. Future plans for MatFast include enhancing user APIs and developing additional optimization features.
Ad
Recently uploaded (20)
Artigo - Playing to Win.planejamento docx
Artigo - Playing to Win.planejamento docxKellyXavier15 Excelente artifo para quem está iniciando processo de aquisiçãode planejamento estratégico
Data Warehousing and Analytics IFI Techsolutions .pptx
Data Warehousing and Analytics IFI Techsolutions .pptxIFI Techsolutions IFI Techsolutions is a leading cloud solutions and managed services provider. We are an Azure Expert Managed Service Provider (AEMSP) and boast multiple advanced specializations in Infra and Database Migration to Microsoft Azure, Migrate Enterprise Apps to Microsoft Azure, and Microsoft Azure Virtual Desktop.
Simplify SQL migration to Azure with Microsoft’s DMA tool—assess, plan, and move your data seamlessly with expert support from IFI Tech.
Flextronics Employee Safety Data-Project-2.pptx
Flextronics Employee Safety Data-Project-2.pptxkilarihemadri This PPT are using to show employee safety
Verweven van EM Legacy en OTL-data bij AWV
Verweven van EM Legacy en OTL-data bij AWVjacoba18 Het project heeft als doel legacy- en OTL-data samen te brengen in één consistente, OTL-conforme databank. In deze sessie laten we zien hoe we data vergelijken, verschillen helder rapporteren aan de business en feedback op een geautomatiseerde manier verwerken.
Boost Business Efficiency with Professional Data Entry Services
Boost Business Efficiency with Professional Data Entry Serviceseloiacs eloiacs Boost Business Efficiency with Professional Data Entry Services
In today’s digital-first world, businesses generate and handle massive amounts of data every day — customer records, sales data, inventory logs, survey results, and much more. But raw data has no value unless it is well-organized, accurate, and easily accessible. That’s where professional data entry services come in.
By outsourcing data entry tasks to experts, businesses can streamline operations, reduce manual errors, and improve overall efficiency — while focusing their internal resources on core activities like growth and customer engagement.
What Are Data Entry Services?
Data entry services refer to the process of converting information from various formats (handwritten, scanned, PDF, image, or audio) into structured, digital formats such as Excel sheets, CRM databases, or cloud storage systems. This work may be done online or offline, manually or using automation tools, depending on the client’s requirements.
Popular Data Entry Services Include:
Manual data entry from paper documents or scanned files
Online data entry directly into websites, forms, or portals
Offline data entry in formats like MS Word, Excel, or custom software
Product data entry for eCommerce platforms like Shopify, Amazon, and Flipkart
Document and image conversion into editable formats
Data cleansing and validation to remove duplicates and fix errors
Remote data entry support for real-time operations
CRM and ERP data management
These services are essential for organizing business data and making it usable for analysis, reporting, and decision-making.
Who Can Benefit from Data Entry Services?
Outsourcing data entry is not limited to any one industry — it's a universal need for businesses of all types and sizes. Here are some examples:
eCommerce Businesses – For managing product catalogs, inventory updates, pricing, and customer orders.
Healthcare Providers – For digitizing patient records, prescriptions, and insurance documents.
Education Institutes – To maintain student records, exam results, and staff data.
Financial Services – For processing invoices, bank statements, transaction records.
Real Estate Companies – To handle property listings, legal paperwork, client records.
Research & Marketing Firms – To compile survey data, leads, and analytics reports.
Even startups and freelancers often require virtual data entry services to stay organized and competitive.
Top Benefits of Outsourcing Data Entry Services
Outsourcing data entry work to a professional company or virtual assistant offers multiple benefits — whether you're running a small business or managing a large enterprise.
1. Reduced Costs
Maintaining an in-house data entry team means salaries, hardware, training, and software expenses. Outsourcing eliminates these costs and provides flexible, pay-as-you-go solutions.
QUALITATIVE EXPLANATORY VARIABLES REGRESSION MODELS
QUALITATIVE EXPLANATORY VARIABLES REGRESSION MODELSAmeya Patekar QUALITATIVE EXPLANATORY VARIABLES REGRESSION MODELS
最新版美国佐治亚大学毕业证（UGA毕业证书）原版定制
最新版美国佐治亚大学毕业证（UGA毕业证书）原版定制Taqyea 2025原版佐治亚大学毕业证书pdf电子版【q薇1954292140】美国毕业证办理UGA佐治亚大学毕业证书多少钱？【q薇1954292140】海外各大学Diploma版本，因为疫情学校推迟发放证书、证书原件丢失补办、没有正常毕业未能认证学历面临就业提供解决办法。当遭遇挂科、旷课导致无法修满学分，或者直接被学校退学，最后无法毕业拿不到毕业证。此时的你一定手足无措，因为留学一场，没有获得毕业证以及学历证明肯定是无法给自己和父母一个交代的。
【复刻佐治亚大学成绩单信封,Buy The University of Georgia Transcripts】
购买日韩成绩单、英国大学成绩单、美国大学成绩单、澳洲大学成绩单、加拿大大学成绩单（q微1954292140）新加坡大学成绩单、新西兰大学成绩单、爱尔兰成绩单、西班牙成绩单、德国成绩单。成绩单的意义主要体现在证明学习能力、评估学术背景、展示综合素质、提高录取率，以及是作为留信认证申请材料的一部分。
佐治亚大学成绩单能够体现您的的学习能力，包括佐治亚大学课程成绩、专业能力、研究能力。（q微1954292140）具体来说，成绩报告单通常包含学生的学习技能与习惯、各科成绩以及老师评语等部分，因此，成绩单不仅是学生学术能力的证明，也是评估学生是否适合某个教育项目的重要依据！
我们承诺采用的是学校原版纸张（原版纸质、底色、纹路）我们工厂拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有成品以及工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！
【主营项目】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理毕业证|办理文凭: 买大学毕业证|买大学文凭【q薇1954292140】佐治亚大学学位证明书如何办理申请？
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理美国成绩单佐治亚大学毕业证【q薇1954292140】国外大学毕业证, 文凭办理, 国外文凭办理, 留信网认证
Residential Zone 4 for industrial village
Residential Zone 4 for industrial villageMdYasinArafat13 based on assumption that failure of such a weld is by shear on the
effective area whether the shear transfer is parallel to or
perpendicular to the axis of the line of fillet weld. In fact, the
strength is greater for shear transfer perpendicular to the weld axis;
however, for simplicity the situations are treated the same.
Shifting Focus on AI: How it Can Make a Positive Difference
Shifting Focus on AI: How it Can Make a Positive Difference1508 A/S This morgenbooster will share how to find the positive impact of AI and how to integrate it into your own digital process.
Prescriptive Process Monitoring Under Uncertainty and Resource Constraints: A...
Prescriptive Process Monitoring Under Uncertainty and Resource Constraints: A...Mahmoud Shoush We introduced Black-Box Prescriptive Process Monitoring (BB-PrPM) – a reinforcement learning approach that learns when, whether, and how to intervene in business processes to boost performance under real-world constraints.
This work is presented at the International Conference on Advanced Information Systems Engineering CAiSE Conference #CAiSE2025
GraphFrames: Graph Queries In Spark SQL
- 1. GraphFrames: Graph Queries in Apache Spark SQL Ankur Dave UC Berkeley AMPLab Joint work with Alekh Jindal (Microsoft), Li Erran Li (Uber), Reynold Xin (Databricks), Joseph Gonzalez (UC Berkeley), and Matei Zaharia (MIT and Databricks)
- 2. + Graph Queries 2016 Apache Spark + GraphFrames GraphFrames (2016) + Graph Algorithms 2013 Apache Spark + GraphX Relational Queries 2009 Spark
- 3. Graph Algorithms vs. Graph Queries ≈ x PageRank Alternating Least Squares Graph Algorithms Graph Queries
- 4. Graph Algorithms vs. Graph Queries Graph Algorithm: PageRank Graph Query: Wikipedia Collaborators Editor 1 Editor 2 Article 1 Article 2 ⇓ Article 1 Article 2 Editor 1 Editor 2 same day} same day}
- 5. Graph Algorithms vs. Graph Queries Graph Algorithm: PageRank // Iterate until convergence wikipedia.pregel( sendMsg = { e => e.sendToDst(e.srcRank * e.weight) }, mergeMsg = _ + _, vprog = { (id, oldRank, msgSum) => 0.15 + 0.85 * msgSum }) Graph Query: Wikipedia Collaborators wikipedia.find( "(u1)-[e11]->(article1); (u2)-[e21]->(article1); (u1)-[e12]->(article2); (u2)-[e22]->(article2)") .select( "*", "e11.date – e21.date".as("d1"), "e12.date – e22.date".as("d2")) .sort("d1 + d2".desc).take(10)
- 6. Separate Systems Graph Algorithms Graph Queries
- 7. Raw Wikipedia < / >< / >< / > XML Text Table Edit Graph Edit Table Frequent Collaborators Problem: Mixed Graph Analysis Hyperlinks PageRank Article Text User Article Vandalism Suspects User User User Article
- 8. Solution: GraphFrames Graph Algorithms Graph Queries Spark SQL GraphFramesAPI Pattern Query Optimizer
- 9. GraphFrames API • Unifies graph algorithms, graph queries, and DataFrames • Available in Scala,Java, and Python class GraphFrame { def vertices: DataFrame def edges: DataFrame def find(pattern: String): DataFrame def registerView(pattern: String, df: DataFrame): Unit def degrees(): DataFrame def pageRank(): GraphFrame def connectedComponents(): GraphFrame ... }
- 10. Implementation Parsed Pattern Logical Plan Materialized Views Optimized Logical Plan DataFrame Result Query String Graph–Relational Translation Join Elimination and Reordering Spark SQL View Selection Graph Algorithms GraphX
- 11. Graph–Relational Translation B D A C Existing Logical Plan Output: A,B,C Src Dst ⋈C=Src Edge Table ID Attr VertexTable ⋈D=ID
- 12. Materialized View Selection GraphX: Triplet view enabled efficient message-passing algorithms Vertices B A C D Edges A B A C B C C D A B Triplet View A C B C C D Graph + Updated PageRanks B A C D A
- 13. Materialized View Selection GraphFrames: User-defined views enable efficient graph queries Vertices B A C D Edges A B A C B C C D A B Triplet View A C B C C D Graph User-Defined Views PageRank Community Detection … Graph Queries
- 14. Join Elimination Src Dst 1 2 1 3 2 3 2 5 Edges ID Attr 1 A 2 B 3 C 4 D Vertices SELECT src, dst FROM edges INNER JOIN vertices ON src = id; Unnecessaryjoin can be eliminated if tables satisfy referential integrity, simplifying graph–relational translation: SELECT src, dst FROM edges;
- 15. Join Reordering A → B B → A ⋈A, B B → D C → B⋈B B → E⋈B C → D⋈B C → E⋈C, D ⋈C, E Example Query Left-Deep Plan BushyPlan A → B B → A ⋈A, B B → D C → B ⋈B B → E⋈B ⋈B ⋈B, C User-Defined View
- 16. Evaluation Faster than Neo4j for unanchored patternqueries 0 0.5 1 1.5 2 2.5 GraphFrames Neo4j Querylatency,s AnchoredPatternQuery 0 10 20 30 40 50 60 70 80 GraphFrames Neo4j Querylatency,s UnanchoredPatternQuery Triangle query on 1M edge subgraph of web-Google. Each system configured touse a single core.
- 17. Evaluation Approaches performance of GraphX for graph algorithms using Spark SQL whole-stage code generation 0 1 2 3 4 5 6 7 GraphFrames GraphX Naïve Spark Per-iterationruntime,s PageRankPerformance Per-iteration performance on web-Google, single 8-core machine. Naïve SparkusesScala RDD API.
- 18. Evaluation Registering the right views cangreatlyimprove performance for some queries Workload: J. Huang, K. Venkatraman, and D.J. Abadi.Query optimization of distributed pattern matching. In ICDE 2014.
- 19. Future Work • Suggest views automatically • Exploit attribute-based partitioning in optimizer • Code generationfor single node
- 20. Try It Out! Releasedas a Spark Package at: https://github.com/graphframes/graphframes Thanks to Joseph Bradley,Xiangrui Meng,and Timothy Hunter. [email protected]