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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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

Advice on PostGIS and Apache Spark
Nilesh Akhade
Technical Architect at Self Employed · | 5 upvotes · 514.5K views

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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Replies (2)
Recommends
on
ElasticsearchElasticsearch

The first solution that came to me is to use upsert to update ElasticSearch:

  1. Use the primary-key as ES document id
  2. Upsert the records to ES as soon as you receive them. As you are using upsert, the 2nd record of the same primary-key will not overwrite the 1st one, but will be merged with it.

Cons: The load on ES will be higher, due to upsert.

To use Flink:

  1. Create a KeyedDataStream by the primary-key
  2. In the ProcessFunction, save the first record in a State. At the same time, create a Timer for 15 minutes in the future
  3. When the 2nd record comes, read the 1st record from the State, merge those two, and send out the result, and clear the State and the Timer if it has not fired
  4. When the Timer fires, read the 1st record from the State and send out as the output record.
  5. Have a 2nd Timer of 6 hours (or more) if you are not using Windowing to clean up the State

Pro: if you have already having Flink ingesting this stream. Otherwise, I would just go with the 1st solution.

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Akshaya Rawat
Senior Specialist Platform at Publicis Sapient · | 3 upvotes · 359.5K views
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Apache SparkApache Spark

Please refer "Structured Streaming" feature of Spark. Refer "Stream - Stream Join" at https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#stream-stream-joins . In short you need to specify "Define watermark delays on both inputs" and "Define a constraint on time across the two inputs"

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Pros of PostGIS
Pros of Apache Spark
  • 25
    De facto GIS in SQL
  • 5
    Good Documentation
  • 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
  • 3
    Works well for most Datascience usecases
  • 2
    Interactive Query
  • 2
    Machine learning libratimery, Streaming in real
  • 2
    In memory Computation

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Cons of PostGIS
Cons of Apache Spark
    Be the first to leave a con
    • 4
      Speed

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    What is PostGIS?

    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.

    What is Apache Spark?

    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.

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    What are some alternatives to PostGIS and Apache Spark?
    MongoDB
    MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
    MySQL
    The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.
    Elasticsearch
    Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).
    PostgreSQL
    PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.
    ArcGIS
    It is a geographic information system for working with maps and geographic information. It is used for creating and using maps, compiling geographic data, analyzing mapped information, sharing and much more.
    See all alternatives