Apache Spark vs PostGIS

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

PostGIS

Stacks381

Followers377

Votes30

GitHub Stars2.0K

Forks407

Apache Spark vs PostGIS: What are the differences?

Introduction

Apache Spark and PostGIS are two commonly used technologies in the field of data processing and analysis. While Apache Spark is a distributed computing framework, PostGIS is an extension to the PostgreSQL relational database management system that adds support for geographic objects.

Scalability: One key difference between Apache Spark and PostGIS is their scalability. Apache Spark is designed to handle large-scale data processing and analytics tasks by distributing the workload across a cluster of machines. On the other hand, PostGIS is primarily focused on storing and querying geographic data within a relational database, making it more suitable for smaller datasets or specific use cases.
Data Processing Capabilities: Another major difference lies in their data processing capabilities. Apache Spark provides a wide range of built-in data processing libraries and APIs, such as Spark SQL, Spark Streaming, and Spark MLlib, which enable developers to perform tasks like batch processing, real-time streaming, and machine learning. PostGIS, on the other hand, primarily focuses on spatial data processing and provides functions and operators for spatial queries, geospatial analysis, and map rendering.
Data Storage: Apache Spark does not provide its own storage system but can integrate with various data storage systems, including HDFS, HBase, and Amazon S3. In contrast, PostGIS is an extension to the PostgreSQL database and stores its data within a PostgreSQL database, leveraging its reliability, transaction support, and rich query capabilities. This difference in data storage options can impact the choice of technology based on the specific requirements of the project.
Geospatial Capabilities: As PostGIS is primarily designed for geographic data processing, it offers advanced geospatial capabilities such as spatial indexing, distance calculations, coordinate transformations, and spatial analysis functions. Apache Spark, although it can handle some geospatial data processing, does not have the same level of specialized support for geographic objects as PostGIS.
Tool Ecosystem: Apache Spark has a large and active community, which has resulted in a rich ecosystem of tools and libraries built around Spark. This ecosystem includes data connectors, visualization tools, machine learning libraries, and data integration frameworks. While PostGIS has its own set of extensions and plugins, the tool ecosystem around Spark is more extensive and diverse, making it easier to integrate with other technologies and extend its functionality.
Performance and Use Cases: The performance characteristics and use cases of Apache Spark and PostGIS differ based on the nature of the data and processing requirements. Apache Spark excels in scenarios where massive parallel processing is required, such as data transformation, machine learning, and stream processing. PostGIS, on the other hand, is well-suited for spatial analysis, geolocation-based applications, and managing geospatial data within a relational database.

In Summary, Apache Spark and PostGIS have key differences in terms of scalability, data processing capabilities, data storage options, geospatial capabilities, tool ecosystem, and performance characteristics, making them suitable for different use cases in the field of data processing and analysis.

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Advice on Apache Spark, PostGIS

Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceon

Elasticsearch

Kafka

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

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Detailed Comparison

Apache Spark	PostGIS
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.	PostGIS is a spatial database extender for PostgreSQL object-relational database. It adds support for geographic objects allowing location queries to be run in SQL.
Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk;Write applications quickly in Java, Scala or Python;Combine SQL, streaming, and complex analytics;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3	Processing and analytic functions for both vector and raster data for splicing, dicing, morphing, reclassifying, and collecting/unioning with the power of SQL;raster map algebra for fine-grained raster processing;Spatial reprojection SQL callable functions for both vector and raster data;Support for importing / exporting ESRI shapefile vector data via both commandline and GUI packaged tools and support for more formats via other 3rd-party Open Source tools
Statistics
GitHub Stars 42.2K	GitHub Stars 2.0K
GitHub Forks 28.9K	GitHub Forks 407
Stacks 3.1K	Stacks 381
Followers 3.5K	Followers 377
Votes 140	Votes 30
Pros & Cons
Pros 61 Open-source 48 Fast and Flexible 8 One platform for every big data problem 8 Great for distributed SQL like applications 6 Easy to install and to use Cons 4 Speed	Pros 25 De facto GIS in SQL 5 Good Documentation
Integrations
No integrations available	PostgreSQL

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Related Comparisons

Apache Spark vs PostGIS: What are the differences?

Introduction

Scalability: One key difference between Apache Spark and PostGIS is their scalability. Apache Spark is designed to handle large-scale data processing and analytics tasks by distributing the workload across a cluster of machines. On the other hand, PostGIS is primarily focused on storing and querying geographic data within a relational database, making it more suitable for smaller datasets or specific use cases.
Data Processing Capabilities: Another major difference lies in their data processing capabilities. Apache Spark provides a wide range of built-in data processing libraries and APIs, such as Spark SQL, Spark Streaming, and Spark MLlib, which enable developers to perform tasks like batch processing, real-time streaming, and machine learning. PostGIS, on the other hand, primarily focuses on spatial data processing and provides functions and operators for spatial queries, geospatial analysis, and map rendering.
Data Storage: Apache Spark does not provide its own storage system but can integrate with various data storage systems, including HDFS, HBase, and Amazon S3. In contrast, PostGIS is an extension to the PostgreSQL database and stores its data within a PostgreSQL database, leveraging its reliability, transaction support, and rich query capabilities. This difference in data storage options can impact the choice of technology based on the specific requirements of the project.
Geospatial Capabilities: As PostGIS is primarily designed for geographic data processing, it offers advanced geospatial capabilities such as spatial indexing, distance calculations, coordinate transformations, and spatial analysis functions. Apache Spark, although it can handle some geospatial data processing, does not have the same level of specialized support for geographic objects as PostGIS.
Tool Ecosystem: Apache Spark has a large and active community, which has resulted in a rich ecosystem of tools and libraries built around Spark. This ecosystem includes data connectors, visualization tools, machine learning libraries, and data integration frameworks. While PostGIS has its own set of extensions and plugins, the tool ecosystem around Spark is more extensive and diverse, making it easier to integrate with other technologies and extend its functionality.
Performance and Use Cases: The performance characteristics and use cases of Apache Spark and PostGIS differ based on the nature of the data and processing requirements. Apache Spark excels in scenarios where massive parallel processing is required, such as data transformation, machine learning, and stream processing. PostGIS, on the other hand, is well-suited for spatial analysis, geolocation-based applications, and managing geospatial data within a relational database.

Apache Spark vs PostGIS

Overview

Apache Spark vs PostGIS: What are the differences?