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Apache Spark VS Typesense

Compare Apache Spark VS Typesense and see what are their differences

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Apache Spark logo Apache Spark

Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

Typesense logo Typesense

Typo tolerant, delightfully simple, open source search 🔍
  • Apache Spark Landing page
    Landing page //
    2021-12-31
  • Typesense Landing page
    Landing page //
    2022-11-07

Apache Spark features and specs

  • Speed
    Apache Spark processes data in-memory, significantly increasing the processing speed of data tasks compared to traditional disk-based engines.
  • Ease of Use
    Spark offers high-level APIs in Java, Scala, Python, and R, making it accessible to a broad range of developers and data scientists.
  • Advanced Analytics
    Spark supports advanced analytics, including machine learning, graph processing, and real-time streaming, which can be executed in the same application.
  • Scalability
    Spark can handle both small- and large-scale data processing tasks, scaling seamlessly from a single machine to thousands of servers.
  • Support for Various Data Sources
    Spark can integrate with a wide variety of data sources, including HDFS, Apache HBase, Apache Hive, Cassandra, and many others.
  • Active Community
    Spark has a vibrant and active community, providing a wealth of extensions, tools, and support options.

Possible disadvantages of Apache Spark

  • Memory Consumption
    Spark's in-memory processing can be resource-intensive, requiring substantial amounts of RAM, which can drive up costs for large-scale deployments.
  • Complexity in Configuration
    To optimize performance, Spark requires careful configuration and tuning, which can be complex and time-consuming.
  • Learning Curve
    Despite its ease of use, mastering the full range of Spark's features and best practices can take considerable time and effort.
  • Latency for Small Data
    For smaller datasets or low-latency requirements, Spark might not be the most efficient choice, as other technologies could offer better performance.
  • Integration Overhead
    Though Spark integrates with many systems, incorporating it into an existing data infrastructure can introduce additional overhead and complexity.
  • Community Support Variability
    While the community is active, the support and quality of third-party libraries and tools can be inconsistent, leading to potential challenges in implementation.

Typesense features and specs

  • High Performance
    Typesense offers highly optimized search capabilities with fast response times, ensuring quick retrieval of search results even with large datasets.
  • Easy to Set Up
    Typesense is user-friendly and can be quickly set up using a simple configuration, making it accessible for developers who need a straightforward search solution.
  • Real-Time Indexing
    Typesense supports real-time indexing, meaning new data or updates to existing data are searchable almost immediately without significant delay.
  • Open Source
    Being an open-source solution, Typesense provides transparency, community support, and the possibility for customization to meet specific needs.
  • Typo Tolerance
    Typesense’s built-in typo tolerance allows for forgiving spell-check and correction, enhancing user experience by returning relevant results despite minor typing errors.
  • Faceted Search
    The platform supports faceted search, which lets users narrow down search results through various categories, improving relevancy and user navigation.

Possible disadvantages of Typesense

  • Limited Advanced Features
    Compared to some competitors, Typesense offers fewer advanced search features like natural language processing or machine learning-based relevance tuning.
  • Community Support
    Being relatively newer, Typesense has a smaller user base and community support compared to established search engines like ElasticSearch or Solr.
  • Documentation
    Some users may find Typesense’s documentation to be less comprehensive, potentially leading to a steeper learning curve for complex use-cases.
  • Scalability
    While Typesense is scalable, enterprise-level users managing extremely large datasets might find it less robust compared to established solutions that have been battle-tested in large-scale environments.
  • Ecosystem Integration
    The integration ecosystem is still developing, which means fewer out-of-the-box integrations with other popular tools and platforms compared to older search engines.

Apache Spark videos

+ Add

Weekly Apache Spark live Code Review -- look at StringIndexer multi-col (Scala) & Python testing

More videos:

  • Review - What's New in Apache Spark 3.0.0
  • Review - Apache Spark for Data Engineering and Analysis - Overview

Typesense videos

+ Add

Getting started with Typesense

Category Popularity

0-100% (relative to Apache Spark and Typesense)

Apache Spark

Typesense

Databases
100 100%
Databases
0% 0
Custom Search Engine
0 0%
Custom Search Engine
100% 100
Big Data
100 100%
Big Data
0% 0
Custom Search
0 0%
Custom Search
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Apache Spark and Typesense

Apache Spark Reviews

15 data science tools to consider using in 2021
Apache Spark is an open source data processing and analytics engine that can handle large amounts of data -- upward of several petabytes, according to proponents. Spark's ability to rapidly process data has fueled significant growth in the use of the platform since it was created in 2009, helping to make the Spark project one of the largest open source communities among big...
Source: searchbusinessanalytics.techtarget.com
Top 15 Kafka Alternatives Popular In 2021
Apache Spark is a well-known, general-purpose, open-source analytics engine for large-scale, core data processing. It is known for its high-performance quality for data processing – batch and streaming with the help of its DAG scheduler, query optimizer, and engine. Data streams are processed in real-time and hence it is quite fast and efficient. Its machine learning...
Source: www.spec-india.com
5 Best-Performing Tools that Build Real-Time Data Pipeline
Apache Spark is an open-source and flexible in-memory framework which serves as an alternative to map-reduce for handling batch, real-time analytics and data processing workloads. It provides native bindings for the Java, Scala, Python, and R programming languages, and supports SQL, streaming data, machine learning and graph processing. From its beginning in the AMPLab at...
Source: www.analyticsinsight.net

Typesense Reviews

Best Elasticsearch alternatives for search
A plug for yours truly! At Relevance AI, we’re building an Elasticsearch alternative that is very different to alternatives like Algolia and Typesense. Relevance AI search is an instant search API that understands “semantics”.
Source: relevance.ai
5 Open-Source Search Engines For your Website
Typesense is a fast, typo-tolerant search engine for building delightful search experiences. It claims that it is an Easier-to-Use ElasticSearch Alternative & an Open Source Algolia Alternative.
Source: vishnuch.tech
Recommendations for Poor Man's ElasticSearch on AWS?
Oh hey! I'm one of the co-founders of Typesense. Delighted to stumble on a mention of Typesense on Indiehackers. Long time lurker, first time poster :)
Source: www.indiehackers.com

Social recommendations and mentions

Apache Spark might be a bit more popular than Typesense. We know about 70 links to it since March 2021 and only 58 links to Typesense. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Apache Spark mentions (70)

  • Every Database Will Support Iceberg — Here's Why
    Apache Iceberg defines a table format that separates how data is stored from how data is queried. Any engine that implements the Iceberg integration — Spark, Flink, Trino, DuckDB, Snowflake, RisingWave — can read and/or write Iceberg data directly. - Source: dev.to / 13 days ago
  • How to Reduce Big Data Analytics Costs by 90% with Karpenter and Spark
    Apache Spark powers large-scale data analytics and machine learning, but as workloads grow exponentially, traditional static resource allocation leads to 30–50% resource waste due to idle Executors and suboptimal instance selection. - Source: dev.to / 15 days ago
  • Unveiling the Apache License 2.0: A Deep Dive into Open Source Freedom
    One of the key attributes of Apache License 2.0 is its flexible nature. Permitting use in both proprietary and open source environments, it has become the go-to choice for innovative projects ranging from the Apache HTTP Server to large-scale initiatives like Apache Spark and Hadoop. This flexibility is not solely legal; it is also philosophical. The license is designed to encourage transparency and maintain a... - Source: dev.to / about 2 months ago
  • The Application of Java Programming In Data Analysis and Artificial Intelligence
    [1] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach. Pearson, 2020. [2] F. Chollet, Deep Learning with Python. Manning Publications, 2018. [3] C. C. Aggarwal, Data Mining: The Textbook. Springer, 2015. [4] J. Dean and S. Ghemawat, "MapReduce: Simplified Data Processing on Large Clusters," Communications of the ACM, vol. 51, no. 1, pp. 107-113, 2008. [5] Apache Software Foundation, "Apache... - Source: dev.to / about 2 months ago
  • Automating Enhanced Due Diligence in Regulated Applications
    If you're designing an event-based pipeline, you can use a data streaming tool like Kafka to process data as it's collected by the pipeline. For a setup that already has data stored, you can use tools like Apache Spark to batch process and clean it before moving ahead with the pipeline. - Source: dev.to / 3 months ago
View more

Typesense mentions (58)

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What are some alternatives?

When comparing Apache Spark and Typesense, you can also consider the following products

Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.

Algolia - Algolia's Search API makes it easy to deliver a great search experience in your apps & websites. Algolia Search provides hosted full-text, numerical, faceted and geolocalized search.

Hadoop - Open-source software for reliable, scalable, distributed computing

Meilisearch - Ultra relevant, instant, and typo-tolerant full-text search API

Apache Hive - Apache Hive data warehouse software facilitates querying and managing large datasets residing in distributed storage.

ElasticSearch - Elasticsearch is an open source, distributed, RESTful search engine.

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