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Spark Streaming VS dispy

Compare Spark Streaming VS dispy and see what are their differences

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Spark Streaming logo Spark Streaming

Spark Streaming makes it easy to build scalable and fault-tolerant streaming applications.

dispy logo dispy

dispy is a Python framework for parallel execution of computations by distributing them across...
  • Spark Streaming Landing page
    Landing page //
    2022-01-10
  • dispy Landing page
    Landing page //
    2023-04-23

Spark Streaming features and specs

  • Scalability
    Spark Streaming is highly scalable and can handle large volumes of data by distributing the workload across a cluster of machines. It leverages Apache Spark's capabilities to scale out easily and efficiently.
  • Integration
    It integrates seamlessly with other components of the Spark ecosystem, such as Spark SQL, MLlib, and GraphX, allowing for comprehensive data processing pipelines.
  • Fault Tolerance
    Spark Streaming provides fault tolerance by using Spark's micro-batching approach, which allows the system to recover data in case of a failure.
  • Ease of Use
    Spark Streaming provides high-level APIs in Java, Scala, and Python, making it relatively easy to develop and deploy streaming applications quickly.
  • Unified Platform
    It provides a unified platform for both batch and streaming data processing, allowing reuse of code and resources across different types of workloads.

Possible disadvantages of Spark Streaming

  • Latency
    Spark Streaming operates on a micro-batch processing model, which introduces latency compared to real-time processing. This may not be suitable for applications requiring immediate responses.
  • Complexity
    While it integrates well with other Spark components, building complex streaming applications can still be challenging and may require expertise in distributed systems and stream processing concepts.
  • Resource Management
    Efficiently managing cluster resources and tuning the system can be difficult, especially when dealing with variable workload and ensuring optimal performance.
  • Backpressure Handling
    Handling backpressure effectively can be a challenge in Spark Streaming, requiring careful management to prevent resource saturation or data loss.
  • Limited Windowing Support
    Compared to some stream processing frameworks, Spark Streaming has more limited options for complex windowing operations, which can restrict some advanced use cases.

dispy features and specs

  • Ease of Use
    Dispy provides a simple and intuitive API for distributing computations across multiple processors or nodes, making it accessible even for those with moderate technical expertise.
  • Scalability
    It supports both computation parallelization on a single multi-core machine and distribution across a cluster of nodes, allowing for scalable computing.
  • Fault Tolerance
    Dispy includes built-in fault-tolerance features like automatic re-execution of failed tasks, improving reliability in distributed computing environments.
  • Python Integration
    Being a Python library, dispy fits well into the Python ecosystem and can easily integrate with other Python libraries and tools.
  • Open Source
    As an open-source project, dispy is free to use and modify, fostering community contribution and collaboration.

Possible disadvantages of dispy

  • Limited Documentation
    The documentation for dispy can be sparse or lacking in detailed examples, which may pose a challenge for new users trying to implement advanced features.
  • Performance Overhead
    The abstraction layer introduced by dispy might introduce some performance overhead, which can be a drawback in performance-critical applications.
  • Dependency on Python
    As it is a Python-based framework, dispy depends on Python and may not be ideal for integrating with other languages or non-Python components.
  • Community and Support
    As a project hosted on SourceForge, dispy may not have as large a community or as active development as some other distributed computing frameworks, potentially impacting the availability of support and updates.
  • Complexity in Setup
    Setting up a distributed environment with dispy might require additional configuration and setup, which can be complex for users unfamiliar with distributed computing concepts.

Analysis of dispy

Overall verdict

  • Dispy is considered a good choice for users who need a straightforward and effective way to distribute computational tasks. Its Python integration makes it accessible for developers familiar with the language and who need to implement asynchronous computations quickly.

Why this product is good

  • Dispy, available on SourceForge, is a distributed and parallel computing framework primarily written in Python. It allows developers and researchers to easily distribute computation-intensive tasks across multiple processors or computers. This is particularly beneficial for those in need of harnessing more computational power without diving deep into complex parallel computing concepts. Dispy provides simplicity and flexibility with fault-tolerance and dynamic allocation of resources, which makes it appealing for projects requiring scalability and efficiency.

Recommended for

    Dispy is recommended for data scientists, researchers, and developers dealing with computationally heavy tasks that can be parallelized, especially those already using Python. It is ideal for environments where ease of setup and execution is prioritized, and where complex distributed computing systems may not be feasible due to resource constraints.

Spark Streaming videos

+ Add

Spark Streaming Vs Kafka Streams || Which is The Best for Stream Processing?

More videos:

  • Tutorial - Spark Streaming Vs Structured Streaming Comparison | Big Data Hadoop Tutorial

dispy videos

No dispy videos yet. You could help us improve this page by suggesting one.

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Category Popularity

0-100% (relative to Spark Streaming and dispy)

Spark Streaming

dispy

Stream Processing
78 78%
Stream Processing
22% 22
Big Data
75 75%
Big Data
25% 25
Data Management
86 86%
Data Management
14% 14
Databases
0 0%
Databases
100% 100

User comments

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Social recommendations and mentions

Based on our record, Spark Streaming seems to be more popular. It has been mentiond 5 times since March 2021. 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.

Spark Streaming mentions (5)

  • RisingWave Turns Four: Our Journey Beyond Democratizing Stream Processing
    The last decade saw the rise of open-source frameworks like Apache Flink, Spark Streaming, and Apache Samza. These offered more flexibility but still demanded significant engineering muscle to run effectively at scale. Companies using them often needed specialized stream processing engineers just to manage internal state, tune performance, and handle the day-to-day operational challenges. The barrier to entry... - Source: dev.to / 6 months ago
  • Streaming Data Alchemy: Apache Kafka Streams Meet Spring Boot
    Apache Spark Streaming: Offers micro-batch processing, suitable for high-throughput scenarios that can tolerate slightly higher latency. https://spark.apache.org/streaming/. - Source: dev.to / about 1 year ago
  • Choosing Between a Streaming Database and a Stream Processing Framework in Python
    Other stream processing engines (such as Flink and Spark Streaming) provide SQL interfaces too, but the key difference is a streaming database has its storage. Stream processing engines require a dedicated database to store input and output data. On the other hand, streaming databases utilize cloud-native storage to maintain materialized views and states, allowing data replication and independent storage scaling. - Source: dev.to / over 1 year ago
  • Machine Learning Pipelines with Spark: Introductory Guide (Part 1)
    Spark Streaming: The component for real-time data processing and analytics. - Source: dev.to / almost 3 years ago
  • Spark for beginners - and you
    Is a big data framework and currently one of the most popular tools for big data analytics. It contains libraries for data analysis, machine learning, graph analysis and streaming live data. In general Spark is faster than Hadoop, as it does not write intermediate results to disk. It is not a data storage system. We can use Spark on top of HDFS or read data from other sources like Amazon S3. It is the designed... - Source: dev.to / almost 4 years ago

dispy mentions (0)

We have not tracked any mentions of dispy yet. Tracking of dispy recommendations started around Mar 2021.

What are some alternatives?

When comparing Spark Streaming and dispy, you can also consider the following products

Confluent - Confluent offers a real-time data platform built around Apache Kafka.

asyncoro - asyncoro is a Python framework for developing concurrent, distributed programs with asynchronous...

Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.

Disco MapReduce - Disco is a lightweight, open-source framework for distributed computing based on the MapReduce...

Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.

Node.js - Node.js is a platform built on Chrome's JavaScript runtime for easily building fast, scalable network applications

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