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Messagepack VS Apache Pig

Compare Messagepack VS Apache Pig and see what are their differences

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Messagepack logo Messagepack

An efficient binary serialization format.

Apache Pig logo Apache Pig

Pig is a high-level platform for creating MapReduce programs used with Hadoop.
  • Messagepack Landing page
    Landing page //
    2022-01-07
  • Apache Pig Landing page
    Landing page //
    2021-12-31

Messagepack features and specs

  • Efficiency
    MessagePack provides efficient binary serialization, which can significantly reduce the size of the data. This makes it faster to transmit over networks and cheaper to store, particularly for large datasets.
  • Interoperability
    MessagePack is supported by a wide variety of programming languages, making it easy to use in polyglot environments or in systems that consist of multiple services using different programming languages.
  • Simplicity
    The MessagePack format is simple to use and understand, comparable to JSON, but it offers better performance and compactness as it uses binary format instead of text.
  • Flexibility
    Supports a variety of data types including integers, floats, strings, arrays, and maps, allowing for complex data structures to be serialized without losing any information.

Possible disadvantages of Messagepack

  • Human Readability
    Because MessagePack uses a binary format, it is not human-readable. This makes debugging and logging more difficult compared to text formats like JSON.
  • Size Overhead for Small Data
    For very small payloads, the size overhead of MessagePack can be higher than JSON. This is because the headers and binary format of MessagePack can add more bytes compared to JSON’s minimal text representation.
  • Tooling and Ecosystem
    While MessagePack is widely supported, its ecosystem and tooling are not as rich as JSON’s. JSON has more extensive support in terms of libraries, tools, and online resources.
  • Complexity in Implementation
    Implementing MessagePack serialization and deserialization requires handling binary data, which can be more complex than dealing with text-based formats. This might require more effort and careful handling, especially in resource-constrained environments.

Apache Pig features and specs

  • Simplicity
    Apache Pig provides a high-level scripting language called Pig Latin that is much easier to write and understand than complex MapReduce code, enabling faster development time.
  • Abstracts Hadoop Complexity
    Pig abstracts the complexity of Hadoop, allowing developers to focus on data processing rather than worrying about the intricacies of Hadoop’s underlying mechanisms.
  • Extensibility
    Pig allows user-defined functions (UDFs) to process various types of data, giving users the flexibility to extend its functionality according to their specific requirements.
  • Optimized Query Execution
    Pig includes a rich set of optimization techniques that automatically optimize the execution of scripts, thereby improving performance without needing manual tuning.
  • Error Handling and Debugging
    The platform has an extensive error handling mechanism and provides the ability to make debugging easier through logging and stack traces, making it simpler to troubleshoot issues.

Possible disadvantages of Apache Pig

  • Performance Limitations
    While Pig simplifies writing MapReduce operations, it may not always offer the same level of performance as hand-optimized, low-level MapReduce code.
  • Limited Real-Time Processing
    Pig is primarily designed for batch processing and may not be the best choice for real-time data processing requirements.
  • Steeper Learning Curve for SQL Users
    Developers who are already familiar with SQL might find Pig Latin to be less intuitive at first, resulting in a steeper learning curve for building complex data transformations.
  • Maintenance Overhead
    As Pig scripts grow in complexity and number, maintaining and managing these scripts can become challenging, particularly in large-scale production environments.
  • Growing Obsolescence
    With the rise of more versatile and performant Big Data tools like Apache Spark and Hive, Pig’s relevance and community support have been on the decline.

Messagepack videos

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Apache Pig videos

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Pig Tutorial | Apache Pig Script | Hadoop Pig Tutorial | Edureka

More videos:

  • Review - Simple Data Analysis with Apache Pig

Category Popularity

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User comments

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Social recommendations and mentions

Based on our record, Messagepack should be more popular than Apache Pig. It has been mentiond 13 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.

Messagepack mentions (13)

  • Salt Exporter: the story behind the tool
    I also read that Salt was using MessagePack to format their messages. MessagePack is a format like JSON, but more compact. - Source: dev.to / over 1 year ago
  • What is the fastest way to encode the arbitrary struct into bytes?
    So appreciate such a detailed reply, thanks. btw, why did you choose tinylib/msgp from 4 available go-impls? Source: about 2 years ago
  • Using Arduino as input to Rust project (help needed)
    If you find you're running the serial connection at maximum speed and it's still not fast enough, try switching to a more compact binary encoding that has both Serde and Arduino implementations, like MsgPack... Though I don't remember enough about its format off the top of my head to tell you the easiest way to put an unambiguous header on each packet/message to make the protocol self-synchronizing. Source: over 2 years ago
  • Java Serialization with Protocol Buffers
    The information can be stored in a database or as files, serialized in a standard format and with a schema agreed with your Data Engineering team. Depending on your information and requirements, it can be as simple as CSV, XML or JSON, or Big Data formats such as Parquet, Avro, ORC, Arrow, or message serialization formats like Protocol Buffers, FlatBuffers, MessagePack, Thrift, or Cap'n Proto. - Source: dev.to / over 2 years ago
  • Multiplayer Networking Solutions
    MessagePack Similar to JSONs, just more compact, although not as much as the ones above. Still, it's usefull to retain some readability in your messages. Source: over 2 years ago
View more

Apache Pig mentions (2)

  • In One Minute : Hadoop
    Pig, a platform/programming language for authoring parallelizable jobs. - Source: dev.to / over 2 years ago
  • Spark is lit once again
    In the early days of the Big Data era when K8s hasn't even been born yet, the common open source go-to solution was the Hadoop stack. We have written several old-fashioned Map-Reduce jobs, scripts using Pig until we came across Spark. Since then Spark has became one of the most popular data processing engines. It is very easy to start using Lighter on YARN deployments. Just run a docker with proper configuration... - Source: dev.to / over 3 years ago

What are some alternatives?

When comparing Messagepack and Apache Pig, you can also consider the following products

Protobuf - Protocol buffers are a language-neutral, platform-neutral extensible mechanism for serializing structured data.

Looker - Looker makes it easy for analysts to create and curate custom data experiences—so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.

TOML - TOML - Tom's Obvious, Minimal Language

Jupyter - Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Ready to get started? Try it in your browser Install the Notebook.

JSON - (JavaScript Object Notation) is a lightweight data-interchange format

Presto DB - Distributed SQL Query Engine for Big Data (by Facebook)

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