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MapD vs Apache Spark: What are the differences?

What is MapD? Open source GPU-Powered Database. Interactively query and visualize massive datasets with the parallel power of GPUs.

What is 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.

MapD can be classified as a tool in the "Databases" category, while Apache Spark is grouped under "Big Data Tools".

Some of the features offered by MapD are:

  • SQL
  • GPU-powered
  • column store

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

"Super fast, and the approach taken" is the top reason why over 2 developers like MapD, while over 45 developers mention "Open-source" as the leading cause for choosing Apache Spark.

MapD and Apache Spark are both open source tools. Apache Spark with 22.5K GitHub stars and 19.4K forks on GitHub appears to be more popular than MapD with 1.91K GitHub stars and 271 GitHub forks.

Advice on MapD and Apache Spark
Nilesh Akhade
Technical Architect at Self Employed · | 5 upvotes · 366.6K 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
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 · 238.1K views
Recommends
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 MapD
Pros of Apache Spark
  • 3
    Super fast, and the approach taken
  • 1
    Hehe
  • 60
    Open-source
  • 48
    Fast and Flexible
  • 8
    Great for distributed SQL like applications
  • 8
    One platform for every big data problem
  • 6
    Easy to install and to use
  • 3
    Works well for most Datascience usecases
  • 2
    Interactive Query
  • 2
    In memory Computation
  • 2
    Machine learning libratimery, Streaming in real

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

    Sign up to add or upvote consMake informed product decisions

    - No public GitHub repository available -

    What is MapD?

    Interactively query and visualize massive datasets with the parallel power of GPUs.

    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 companies use MapD?
    What companies use Apache Spark?
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    What tools integrate with MapD?
    What tools integrate with Apache Spark?

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    Mar 24 2021 at 12:57PM

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    What are some alternatives to MapD and Apache Spark?
    Tableau
    Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click.
    Clickhouse
    It allows analysis of data that is updated in real time. It offers instant results in most cases: the data is processed faster than it takes to create a query.
    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.
    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.
    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.
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