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PostGIS vs Apache Spark: What are the differences?

PostGIS: Open source spatial database. 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; Apache Spark: Fast and general engine for large-scale data processing. 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 belongs to "Database Tools" category of the tech stack, while Apache Spark can be primarily classified under "Big Data Tools".

Some of the features offered by PostGIS are:

  • 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

On the other hand, Apache Spark provides the following key features:

  • 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

"De facto GIS in SQL" is the top reason why over 22 developers like PostGIS, while over 45 developers mention "Open-source" as the leading cause for choosing Apache Spark.

PostGIS and Apache Spark are both open source tools. It seems that Apache Spark with 22.3K GitHub stars and 19.3K forks on GitHub has more adoption than PostGIS with 636 GitHub stars and 242 GitHub forks.

According to the StackShare community, Apache Spark has a broader approval, being mentioned in 263 company stacks & 111 developers stacks; compared to PostGIS, which is listed in 53 company stacks and 14 developer stacks.

Advice on PostGIS and Apache Spark
Nilesh Akhade
Technical Architect at Self Employed · | 5 upvotes · 180.9K 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
Elasticsearch

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 · 89K views
Recommends
Apache 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
  • 24
    De facto GIS in SQL
  • 5
    Good Documentation
  • 58
    Open-source
  • 48
    Fast and Flexible
  • 7
    One platform for every big data problem
  • 6
    Easy to install and to use
  • 6
    Great for distributed SQL like applications
  • 3
    Works well for most Datascience usecases
  • 2
    Machine learning libratimery, Streaming in real
  • 2
    In memory Computation
  • 0
    Interactive Query

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Cons of PostGIS
Cons of Apache Spark
    Be the first to leave a con
    • 3
      Speed

    Sign up to add or upvote consMake informed product decisions

    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.

    Need advice about which tool to choose?Ask the StackShare community!

    What companies use PostGIS?
    What companies use Apache Spark?
    See which teams inside your own company are using PostGIS or Apache Spark.
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    What tools integrate with PostGIS?
    What tools integrate with Apache Spark?

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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
    How developers use PostGIS and Apache Spark
    Wei Chen uses
    Apache Spark

    Spark is good at parallel data processing management. We wrote a neat program to handle the TBs data we get everyday.

    Kalibrr uses
    PostGIS

    PostGIS makes it easy (and fast) to do geographic queries, such as nearest-neighbor and bounding box queries.

    Sail Tactics uses
    PostGIS

    Backend for weather forecast data that Geoserver queries to build updated weather maps

    Ralic Lo uses
    Apache Spark

    Used Spark Dataframe API on Spark-R for big data analysis.

    Kalibrr uses
    Apache Spark

    We use Apache Spark in computing our recommendations.

    Dotmetrics uses
    Apache Spark

    Big data analytics and nightly transformation jobs.

    brenoinojosa uses
    Apache Spark

    Data retrieval and analysis of Cassandra.

    Mathias Vonende uses
    PostGIS

    Storage for geo data.

    DNT uses
    PostGIS

    Geospatial queries