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Apache Pig VS runc

Compare Apache Pig VS runc and see what are their differences

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Apache Pig logo Apache Pig

Pig is a high-level platform for creating MapReduce programs used with Hadoop.

runc logo runc

CLI tool for spawning and running containers according to the OCI specification - opencontainers/runc
  • Apache Pig Landing page
    Landing page //
    2021-12-31
  • runc Landing page
    Landing page //
    2023-08-21

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.

runc features and specs

  • Standardization
    runc is part of the Open Containers Initiative (OCI), promoting standardization across container runtimes. This ensures interoperability and broad community support.
  • Lightweight
    As a lightweight and fast CLI tool, runc provides a minimal runtime for environments where resource efficiency is critical.
  • Security
    runc adheres to principles of secure software development and incorporates Linux kernel features like namespaces and cgroups to enhance security.
  • Broad Adoption
    As the reference implementation for OCI, runc is widely adopted and tested in production environments, ensuring reliability.
  • Flexibility
    runc offers the flexibility to handle low-level container configurations, making it suitable for advanced users needing granular control.

Possible disadvantages of runc

  • Complexity for Beginners
    The low-level nature of runc can be daunting for beginners who might prefer higher-level tools like Docker that abstract away complexities.
  • Minimalist Design
    While its simplicity is an advantage, runc lacks some of the advanced features and orchestration capabilities found in other container platforms.
  • Manual Configurations
    Users need to manually handle configurations, which can be error-prone and time-consuming compared to automated solutions.
  • Ecosystem Integration
    runc does not provide direct integration with tools and platforms by default, requiring additional setup for comprehensive ecosystem support.
  • Limited Features
    Compared to complete container platforms, runc offers fewer built-in features, requiring supplementary tools to achieve similar functionalities.

Analysis of Apache Pig

Overall verdict

  • Apache Pig is a valuable tool for data professionals working within a Hadoop environment, especially those who prefer or require a language more accessible than Java. However, its utility might be overshadowed by newer technologies such as Apache Spark, which offers more extensive functionality and faster processing speeds.

Why this product is good

  • Apache Pig is a high-level platform for creating programs that run on Apache Hadoop. It simplifies the processing of large data sets by providing a scripting language known as Pig Latin, which is easier to use compared to Java MapReduce. Pig is designed to handle both structured and unstructured data and is particularly effective for tasks involving data manipulation, transformation, and analysis. Its ability to optimize code execution through pig-specific optimizations and automatic transformations makes it a powerful tool for those familiar with Hadoop ecosystems.

Recommended for

    Apache Pig is recommended for data engineers and analysts who are working in Apache Hadoop environments and need to perform ETL (Extract, Transform, Load) operations on large datasets. It is also suitable for teams looking to leverage existing Hadoop infrastructures without delving into complex Java MapReduce programming or when migrating legacy processing scripts based on Pig Latin.

Apache Pig videos

+ Add

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

More videos:

  • Review - Simple Data Analysis with Apache Pig

runc videos

+ Add

2/21/19 RunC Vulnerability Gives Root Access on Container Systems| AT&T ThreatTraq

More videos:

  • Review - Demo MONEY,TIME - RunC

Category Popularity

0-100% (relative to Apache Pig and runc)

Apache Pig

runc

Data Dashboard
100 100%
Data Dashboard
0% 0
Web Servers
0 0%
Web Servers
100% 100
Database Tools
100 100%
Database Tools
0% 0
Web And Application Servers
0 0%
Web And Application Servers
100% 100

User comments

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Social recommendations and mentions

Based on our record, runc should be more popular than Apache Pig. It has been mentiond 11 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.

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

runc mentions (11)

  • Setup multi node kubernetes cluster using kubeadm
    For kubeadm , kubetlet , kubectl should same version package in this lab I used v1.31 to have 1.31.7 References: Https://kubernetes.io/docs/reference/networking/ports-and-protocols/ Https://kubernetes.io/docs/setup/production-environment/tools/kubeadm/install-kubeadm/ Https://github.com/opencontainers/runc/releases/... - Source: dev.to / 3 months ago
  • Comparing 3 Docker container runtimes - Runc, gVisor and Kata Containers
    Previously I wrote about the multiple variants of Docker and also the dependencies behind the Docker daemon. One of the dependencies was the container runtime called runc. That is what creates the usual containers we are all familiar with. When you use Docker, this is the default runtime, which is understandable since it was started by Docker, Inc. - Source: dev.to / 8 months ago
  • You run containers, not dockers - Discussing Docker variants, components and versioning
    Now we have dockerd which uses containerd, but containerd will not create containers directly. It needs a runtime and the default runtime is runc, but that can be changed. Containerd actually doesn't have to know the parameters of the runtime. There is a shim process between containerd and runc, so containerd knows the parameters of the shim, and the shim knows the parameters of runc or other runtimes. - Source: dev.to / 8 months ago
  • US Cybersecurity: The Urgent Need for Memory Safety in Software Products
    It's interesting that, in light of things like this, you still see large software companies adding support for new components written in non-memory safe languages (e.g. C) As an example Red Hat OpenShift added support for crun(https://github.com/containers/crun), which is written in C as an alternative to runc, which is written in Go( - Source: Hacker News / over 1 year ago
  • Why did the Krustlet project die?
    Yeah, runtimeClass lets you specify which CRI plugin you want based on what you have available. Here's an example from the containerd documentation - you could have one node that can run containers under standard runc, gvisor, kata containers, or WASM. Without runtimeClass, you'd need either some form of custom solution or four differently configured nodes to run those different runtimes. That's how krustlet did... Source: over 2 years ago
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What are some alternatives?

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

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.

Docker Hub - Docker Hub is a cloud-based registry service

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.

Apache Thrift - An interface definition language and communication protocol for creating cross-language services.

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

SkyDNS - DNS service discovery for etcd. Contribute to skynetservices/skydns development by creating an account on GitHub.

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