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  1. Stackups
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  4. Databases
  5. Apache Spark vs SQLite

Apache Spark vs SQLite

OverviewDecisionsComparisonAlternatives

Overview

SQLite
SQLite
Stacks19.9K
Followers15.2K
Votes535
Apache Spark
Apache Spark
Stacks3.1K
Followers3.5K
Votes140
GitHub Stars42.2K
Forks28.9K

Apache Spark vs SQLite: What are the differences?

Introduction

Apache Spark and SQLite are both commonly used data processing tools, but they have significant differences in their capabilities and intended use cases. Understanding these differences is crucial when selecting the appropriate tool for a specific task.

  1. Scalability: Apache Spark is designed for large-scale data processing and is well-suited for big data analytics tasks. It can handle massive amounts of data efficiently by distributing the computing workload across multiple nodes. On the other hand, SQLite is a lightweight, file-based database engine primarily intended for small to medium-sized applications and does not support distributed processing.

  2. Processing Paradigm: Spark provides a high-level, distributed computing framework that allows for parallel processing using the Resilient Distributed Dataset (RDD) abstraction or the newer DataFrame API. It supports not only batch processing but also interactive querying and stream processing. SQLite, on the other hand, follows a traditional client-server model and is more focused on transaction-based applications.

  3. Language Support: Spark is polyglot in nature, offering APIs in popular languages like Scala, Java, Python, and R, providing more flexibility to developers. SQLite, on the other hand, has a primary focus on providing an SQL syntax and does not offer as wide a range of language support.

  4. Data Source Support: Spark has built-in support for a variety of data sources, including Hadoop Distributed File System (HDFS), Apache Cassandra, Amazon S3, and more. It can seamlessly integrate with these data systems and process data stored in various formats (e.g., CSV, Parquet, Avro). SQLite, on the other hand, is mainly designed to work with local databases and does not have built-in compatibility with distributed file systems.

  5. Performance: Spark's distributed processing capabilities give it an advantage over SQLite when dealing with large datasets and complex computations. It can leverage distributed memory and disk storage, as well as parallel processing, to achieve faster processing times. SQLite, being a file-based local database, may not perform as well when handling large-scale data or more resource-intensive operations.

  6. Deployment and Management: Spark is typically deployed on a cluster of machines, allowing for easy scalability and fault tolerance. It includes a standalone cluster manager or can be integrated with other cluster management systems like Apache Mesos or Hadoop YARN. SQLite, on the other hand, is normally used as an embedded database within applications and does not require additional deployment or management infrastructure.

In summary, Apache Spark and SQLite have key differences in terms of scalability, processing paradigm, language support, data source compatibility, performance, and deployment. Understanding these differences is crucial when choosing the appropriate tool for specific data processing requirements.

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Advice on SQLite, Apache Spark

Dimelo
Dimelo

Nov 5, 2020

Needs adviceonSQLiteSQLiteMySQLMySQLPostgreSQLPostgreSQL

I need to add a DBMS to my stack, but I don't know which. I'm tempted to learn SQLite since it would be useful to me with its focus on local access without concurrency. However, doing so feels like I would be defeating the purpose of trying to expand my skill set since it seems like most enterprise applications have the opposite requirements.

To be able to apply what I learn to more projects, what should I try to learn? MySQL? PostgreSQL? Something else? Is there a comfortable middle ground between high applicability and ease of use?

670k views670k
Comments
Stephen
Stephen

Senior DevOps Engineer at Vital Beats

Nov 9, 2020

Review

A question you might want to think about is "What kind of experience do I want to gain, by using a DBMS?". If your aim is to have experience with SQL and any related libraries and frameworks for your language of choice (python, I think?), then it kind of doesn't matter too much which you pick so much. As others have said, SQLite would offer you the ability to very easily get started, and would give you a reasonably standard (if a little basic) SQL dialect to work with.

If your aim is actually to have a bit of "operational" experience, in terms of things like what command line tools might be available as standard for the DBMS, understanding how the DBMS handles multiple databases, when to use multiple schemas vs multiple databases, some basic privilege management etc. Then I would recommend PostgreSQL. SQLite's simplicity actually avoids most of these experiences, which is not helpful to you if that is what you hope to learn. MySQL has a few "quirks" to how it manages things like multiple databases, which may lead you to making less good decisions if you tried to take your experience over to different DBMS, especially in bigger enterprise roles. PostgreSQL is kind of a happy middle ground here, with the ability to start PostgreSQL servers via docker or docker-compose making the actual day-to-day management pretty easy, while still giving you experience of the kinds of considerations I have listed above.

At Vital Beats we make use of PostgreSQL, largely because it offers us a happy balance between good management and backup of data, and good standard command line tools, which is essential for us where we are deploying our solutions within Kubernetes / docker, and so more graphical tools are not always appropriate for us. PostgreSQL is also pretty universally supported in terms of language libraries and frameworks, without having to make compromises on how we want to store and layout our data.

316k views316k
Comments
Nilesh
Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceonElasticsearchElasticsearchKafkaKafka

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.

576k views576k
Comments

Detailed Comparison

SQLite
SQLite
Apache Spark
Apache Spark

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

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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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
Statistics
GitHub Stars
-
GitHub Stars
42.2K
GitHub Forks
-
GitHub Forks
28.9K
Stacks
19.9K
Stacks
3.1K
Followers
15.2K
Followers
3.5K
Votes
535
Votes
140
Pros & Cons
Pros
  • 163
    Lightweight
  • 135
    Portable
  • 122
    Simple
  • 81
    Sql
  • 29
    Preinstalled on iOS and Android
Cons
  • 2
    Not for multi-process of multithreaded apps
  • 1
    Needs different binaries for each platform
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

What are some alternatives to SQLite, Apache Spark?

MongoDB

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

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.

PostgreSQL

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.

Microsoft SQL Server

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

Cassandra

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

InfluxDB

InfluxDB

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.

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