Alternatives to Spring Batch logo

Alternatives to Spring Batch

Hadoop, Talend, Spring Boot, Apache Spark, and Kafka are the most popular alternatives and competitors to Spring Batch.
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What is Spring Batch and what are its top alternatives?

Spring Batch is a lightweight, comprehensive batch framework designed to facilitate the development of robust batch applications. It offers key features such as transaction management, job processing, job execution flow, and resource management. However, Spring Batch may have limitations in terms of complex job requirements and debugging capabilities.

  1. Apache Beam: Apache Beam is a unified programming model for both batch and streaming data processing. It supports multiple execution engines and offers rich features for scalable and fault-tolerant processing. Pros include flexibility in choosing execution engine, while cons may include a steeper learning curve compared to Spring Batch.
  2. Apache Storm: Apache Storm is a real-time computation system designed for processing large volumes of data with low latency. It provides fault-tolerance and scalability for continuous data processing. Pros include real-time processing capabilities, while cons may include a focus on streaming rather than batch processing.
  3. Apache Flink: Apache Flink is a powerful and scalable stream processing framework that also supports batch processing. It offers low-latency and high-throughput processing capabilities with efficient fault-tolerance mechanisms. Pros include unified batch and stream processing, while cons may include complexity for simple batch jobs.
  4. Spring Cloud Data Flow: Spring Cloud Data Flow is a cloud-native toolkit for building and deploying data microservices on modern runtime platforms. It provides a unified interface for composing and orchestrating data pipelines. Pros include cloud-native approach, while cons may include a potentially steep learning curve.
  5. Airflow: Apache Airflow is a platform to programmatically author, schedule, and monitor workflows. It allows the creation of complex workflows with dependencies and triggers. Pros include rich DAG functionalities, while cons may include a more complex setup compared to Spring Batch.
  6. Celery: Celery is a distributed task queue system for message passing between processes. It supports both real-time and batch processing tasks with flexible scheduling and monitoring capabilities. Pros include distributed task execution, while cons may include a steeper learning curve for beginners.
  7. AWS Glue: AWS Glue is a fully managed extract, transform, and load (ETL) service for processing and transforming data at scale. It offers serverless data integration with built-in automation features. Pros include serverless processing, while cons may include potential vendor lock-in.
  8. Google Cloud Dataflow: Google Cloud Dataflow is a fully managed service for executing a wide range of data processing patterns such as ETL, batch computation, and real-time analysis. It offers scalability, monitoring, and integration with other Google Cloud services. Pros include seamless integration with Google Cloud ecosystem, while cons may include potential cost considerations.
  9. Luigi: Luigi is a Python-based dependency framework for defining and running complex pipelines of batch jobs. It provides tooling for building data workflows with support for task dependencies and scheduling. Pros include simplicity for defining dependencies, while cons may include a focus on Python-based workflows.
  10. Talend Open Studio: Talend Open Studio is an open-source data integration tool for building and deploying data pipelines. It offers a visual interface for designing workflows and supports batch and real-time processing. Pros include a user-friendly visual interface, while cons may include potential limitations in advanced data processing functionalities.

Top Alternatives to Spring Batch

  • Hadoop
    Hadoop

    The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. ...

  • Talend
    Talend

    It is an open source software integration platform helps you in effortlessly turning data into business insights. It uses native code generation that lets you run your data pipelines seamlessly across all cloud providers and get optimized performance on all platforms. ...

  • Spring Boot
    Spring Boot

    Spring Boot makes it easy to create stand-alone, production-grade Spring based Applications that you can "just run". We take an opinionated view of the Spring platform and third-party libraries so you can get started with minimum fuss. Most Spring Boot applications need very little Spring configuration. ...

  • Apache Spark
    Apache Spark

    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. ...

  • Kafka
    Kafka

    Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design. ...

  • AWS Batch
    AWS Batch

    It enables developers, scientists, and engineers to easily and efficiently run hundreds of thousands of batch computing jobs on AWS. It dynamically provisions the optimal quantity and type of compute resources (e.g., CPU or memory optimized instances) based on the volume and specific resource requirements of the batch jobs submitted. ...

  • JavaScript
    JavaScript

    JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles. ...

  • Python
    Python

    Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best. ...

Spring Batch alternatives & related posts

Hadoop logo

Hadoop

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56
Open-source software for reliable, scalable, distributed computing
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PROS OF HADOOP
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    Great ecosystem
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    One stack to rule them all
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    Amazon aws
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CONS OF HADOOP
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    Shared insights
    on
    KafkaKafkaHadoopHadoop
    at

    The early data ingestion pipeline at Pinterest used Kafka as the central message transporter, with the app servers writing messages directly to Kafka, which then uploaded log files to S3.

    For databases, a custom Hadoop streamer pulled database data and wrote it to S3.

    Challenges cited for this infrastructure included high operational overhead, as well as potential data loss occurring when Kafka broker outages led to an overflow of in-memory message buffering.

    See more
    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 7 upvotes · 3M views

    Why we built Marmaray, an open source generic data ingestion and dispersal framework and library for Apache Hadoop :

    Built and designed by our Hadoop Platform team, Marmaray is a plug-in-based framework built on top of the Hadoop ecosystem. Users can add support to ingest data from any source and disperse to any sink leveraging the use of Apache Spark . The name, Marmaray, comes from a tunnel in Turkey connecting Europe and Asia. Similarly, we envisioned Marmaray within Uber as a pipeline connecting data from any source to any sink depending on customer preference:

    https://eng.uber.com/marmaray-hadoop-ingestion-open-source/

    (Direct GitHub repo: https://github.com/uber/marmaray Kafka Kafka Manager )

    See more
    Talend logo

    Talend

    152
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    A single, unified suite for all integration needs
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    PROS OF TALEND
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        Spring Boot logo

        Spring Boot

        26K
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        Create Spring-powered, production-grade applications and services with absolute minimum fuss
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        PROS OF SPRING BOOT
        • 149
          Powerful and handy
        • 134
          Easy setup
        • 128
          Java
        • 90
          Spring
        • 85
          Fast
        • 46
          Extensible
        • 37
          Lots of "off the shelf" functionalities
        • 32
          Cloud Solid
        • 26
          Caches well
        • 24
          Productive
        • 24
          Many receipes around for obscure features
        • 23
          Modular
        • 23
          Integrations with most other Java frameworks
        • 22
          Spring ecosystem is great
        • 21
          Auto-configuration
        • 21
          Fast Performance With Microservices
        • 18
          Community
        • 17
          Easy setup, Community Support, Solid for ERP apps
        • 15
          One-stop shop
        • 14
          Easy to parallelize
        • 14
          Cross-platform
        • 13
          Easy setup, good for build erp systems, well documented
        • 13
          Powerful 3rd party libraries and frameworks
        • 12
          Easy setup, Git Integration
        • 5
          It's so easier to start a project on spring
        • 4
          Kotlin
        • 1
          Microservice and Reactive Programming
        • 1
          The ability to integrate with the open source ecosystem
        CONS OF SPRING BOOT
        • 23
          Heavy weight
        • 18
          Annotation ceremony
        • 13
          Java
        • 11
          Many config files needed
        • 5
          Reactive
        • 4
          Excellent tools for cloud hosting, since 5.x
        • 1
          Java 😒😒

        related Spring Boot posts

        Praveen Mooli
        Engineering Manager at Taylor and Francis · | 19 upvotes · 4M views

        We are in the process of building a modern content platform to deliver our content through various channels. We decided to go with Microservices architecture as we wanted scale. Microservice architecture style is an approach to developing an application as a suite of small independently deployable services built around specific business capabilities. You can gain modularity, extensive parallelism and cost-effective scaling by deploying services across many distributed servers. Microservices modularity facilitates independent updates/deployments, and helps to avoid single point of failure, which can help prevent large-scale outages. We also decided to use Event Driven Architecture pattern which is a popular distributed asynchronous architecture pattern used to produce highly scalable applications. The event-driven architecture is made up of highly decoupled, single-purpose event processing components that asynchronously receive and process events.

        To build our #Backend capabilities we decided to use the following: 1. #Microservices - Java with Spring Boot , Node.js with ExpressJS and Python with Flask 2. #Eventsourcingframework - Amazon Kinesis , Amazon Kinesis Firehose , Amazon SNS , Amazon SQS, AWS Lambda 3. #Data - Amazon RDS , Amazon DynamoDB , Amazon S3 , MongoDB Atlas

        To build #Webapps we decided to use Angular 2 with RxJS

        #Devops - GitHub , Travis CI , Terraform , Docker , Serverless

        See more

        Is learning Spring and Spring Boot for web apps back-end development is still relevant in 2021? Feel free to share your views with comparison to Django/Node.js/ ExpressJS or other frameworks.

        Please share some good beginner resources to start learning about spring/spring boot framework to build the web apps.

        See more
        Apache Spark logo

        Apache Spark

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        Fast and general engine for large-scale data processing
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        PROS OF APACHE SPARK
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          Open-source
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          Fast and Flexible
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          One platform for every big data problem
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          Great for distributed SQL like applications
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          Easy to install and to use
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          Works well for most Datascience usecases
        • 2
          Interactive Query
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          Machine learning libratimery, Streaming in real
        • 2
          In memory Computation
        CONS OF APACHE SPARK
        • 4
          Speed

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        Eric Colson
        Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 6.1M views

        The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

        Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

        At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

        For more info:

        #DataScience #DataStack #Data

        See more
        Patrick Sun
        Software Engineer at Stitch Fix · | 10 upvotes · 60K views

        As a frontend engineer on the Algorithms & Analytics team at Stitch Fix, I work with data scientists to develop applications and visualizations to help our internal business partners make data-driven decisions. I envisioned a platform that would assist data scientists in the data exploration process, allowing them to visually explore and rapidly iterate through their assumptions, then share their insights with others. This would align with our team's philosophy of having engineers "deploy platforms, services, abstractions, and frameworks that allow the data scientists to conceive of, develop, and deploy their ideas with autonomy", and solve the pain of data exploration.

        The final product, code-named Dora, is built with React, Redux.js and Victory, backed by Elasticsearch to enable fast and iterative data exploration, and uses Apache Spark to move data from our Amazon S3 data warehouse into the Elasticsearch cluster.

        See more
        Kafka logo

        Kafka

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        Distributed, fault tolerant, high throughput pub-sub messaging system
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        PROS OF KAFKA
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          High-throughput
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          Distributed
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          Scalable
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          High-Performance
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          Durable
        • 38
          Publish-Subscribe
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          Simple-to-use
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          Open source
        • 12
          Written in Scala and java. Runs on JVM
        • 9
          Message broker + Streaming system
        • 4
          KSQL
        • 4
          Avro schema integration
        • 4
          Robust
        • 3
          Suport Multiple clients
        • 2
          Extremely good parallelism constructs
        • 2
          Partioned, replayable log
        • 1
          Simple publisher / multi-subscriber model
        • 1
          Fun
        • 1
          Flexible
        CONS OF KAFKA
        • 32
          Non-Java clients are second-class citizens
        • 29
          Needs Zookeeper
        • 9
          Operational difficulties
        • 5
          Terrible Packaging

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        Nick Rockwell
        SVP, Engineering at Fastly · | 46 upvotes · 4.1M views

        When I joined NYT there was already broad dissatisfaction with the LAMP (Linux Apache HTTP Server MySQL PHP) Stack and the front end framework, in particular. So, I wasn't passing judgment on it. I mean, LAMP's fine, you can do good work in LAMP. It's a little dated at this point, but it's not ... I didn't want to rip it out for its own sake, but everyone else was like, "We don't like this, it's really inflexible." And I remember from being outside the company when that was called MIT FIVE when it had launched. And been observing it from the outside, and I was like, you guys took so long to do that and you did it so carefully, and yet you're not happy with your decisions. Why is that? That was more the impetus. If we're going to do this again, how are we going to do it in a way that we're gonna get a better result?

        So we're moving quickly away from LAMP, I would say. So, right now, the new front end is React based and using Apollo. And we've been in a long, protracted, gradual rollout of the core experiences.

        React is now talking to GraphQL as a primary API. There's a Node.js back end, to the front end, which is mainly for server-side rendering, as well.

        Behind there, the main repository for the GraphQL server is a big table repository, that we call Bodega because it's a convenience store. And that reads off of a Kafka pipeline.

        See more
        Ashish Singh
        Tech Lead, Big Data Platform at Pinterest · | 38 upvotes · 3.3M views

        To provide employees with the critical need of interactive querying, we’ve worked with Presto, an open-source distributed SQL query engine, over the years. Operating Presto at Pinterest’s scale has involved resolving quite a few challenges like, supporting deeply nested and huge thrift schemas, slow/ bad worker detection and remediation, auto-scaling cluster, graceful cluster shutdown and impersonation support for ldap authenticator.

        Our infrastructure is built on top of Amazon EC2 and we leverage Amazon S3 for storing our data. This separates compute and storage layers, and allows multiple compute clusters to share the S3 data.

        We have hundreds of petabytes of data and tens of thousands of Apache Hive tables. Our Presto clusters are comprised of a fleet of 450 r4.8xl EC2 instances. Presto clusters together have over 100 TBs of memory and 14K vcpu cores. Within Pinterest, we have close to more than 1,000 monthly active users (out of total 1,600+ Pinterest employees) using Presto, who run about 400K queries on these clusters per month.

        Each query submitted to Presto cluster is logged to a Kafka topic via Singer. Singer is a logging agent built at Pinterest and we talked about it in a previous post. Each query is logged when it is submitted and when it finishes. When a Presto cluster crashes, we will have query submitted events without corresponding query finished events. These events enable us to capture the effect of cluster crashes over time.

        Each Presto cluster at Pinterest has workers on a mix of dedicated AWS EC2 instances and Kubernetes pods. Kubernetes platform provides us with the capability to add and remove workers from a Presto cluster very quickly. The best-case latency on bringing up a new worker on Kubernetes is less than a minute. However, when the Kubernetes cluster itself is out of resources and needs to scale up, it can take up to ten minutes. Some other advantages of deploying on Kubernetes platform is that our Presto deployment becomes agnostic of cloud vendor, instance types, OS, etc.

        #BigData #AWS #DataScience #DataEngineering

        See more
        AWS Batch logo

        AWS Batch

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        Fully Managed Batch Processing at Any Scale
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        PROS OF AWS BATCH
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          Containerized
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          Scalable
        CONS OF AWS BATCH
        • 3
          More overhead than lambda
        • 1
          Image management

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        JavaScript logo

        JavaScript

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        PROS OF JAVASCRIPT
        • 1.7K
          Can be used on frontend/backend
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          It's everywhere
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          Lots of great frameworks
        • 898
          Fast
        • 745
          Light weight
        • 425
          Flexible
        • 392
          You can't get a device today that doesn't run js
        • 286
          Non-blocking i/o
        • 237
          Ubiquitousness
        • 191
          Expressive
        • 55
          Extended functionality to web pages
        • 49
          Relatively easy language
        • 46
          Executed on the client side
        • 30
          Relatively fast to the end user
        • 25
          Pure Javascript
        • 21
          Functional programming
        • 15
          Async
        • 13
          Full-stack
        • 12
          Setup is easy
        • 12
          Future Language of The Web
        • 12
          Its everywhere
        • 11
          Because I love functions
        • 11
          JavaScript is the New PHP
        • 10
          Like it or not, JS is part of the web standard
        • 9
          Expansive community
        • 9
          Everyone use it
        • 9
          Can be used in backend, frontend and DB
        • 9
          Easy
        • 8
          Most Popular Language in the World
        • 8
          Powerful
        • 8
          Can be used both as frontend and backend as well
        • 8
          For the good parts
        • 8
          No need to use PHP
        • 8
          Easy to hire developers
        • 7
          Agile, packages simple to use
        • 7
          Love-hate relationship
        • 7
          Photoshop has 3 JS runtimes built in
        • 7
          Evolution of C
        • 7
          It's fun
        • 7
          Hard not to use
        • 7
          Versitile
        • 7
          Its fun and fast
        • 7
          Nice
        • 7
          Popularized Class-Less Architecture & Lambdas
        • 7
          Supports lambdas and closures
        • 6
          It let's me use Babel & Typescript
        • 6
          Can be used on frontend/backend/Mobile/create PRO Ui
        • 6
          1.6K Can be used on frontend/backend
        • 6
          Client side JS uses the visitors CPU to save Server Res
        • 6
          Easy to make something
        • 5
          Clojurescript
        • 5
          Promise relationship
        • 5
          Stockholm Syndrome
        • 5
          Function expressions are useful for callbacks
        • 5
          Scope manipulation
        • 5
          Everywhere
        • 5
          Client processing
        • 5
          What to add
        • 4
          Because it is so simple and lightweight
        • 4
          Only Programming language on browser
        • 1
          Test
        • 1
          Hard to learn
        • 1
          Test2
        • 1
          Not the best
        • 1
          Easy to understand
        • 1
          Subskill #4
        • 1
          Easy to learn
        • 0
          Hard 彤
        CONS OF JAVASCRIPT
        • 22
          A constant moving target, too much churn
        • 20
          Horribly inconsistent
        • 15
          Javascript is the New PHP
        • 9
          No ability to monitor memory utilitization
        • 8
          Shows Zero output in case of ANY error
        • 7
          Thinks strange results are better than errors
        • 6
          Can be ugly
        • 3
          No GitHub
        • 2
          Slow
        • 0
          HORRIBLE DOCUMENTS, faulty code, repo has bugs

        related JavaScript posts

        Zach Holman

        Oof. I have truly hated JavaScript for a long time. Like, for over twenty years now. Like, since the Clinton administration. It's always been a nightmare to deal with all of the aspects of that silly language.

        But wowza, things have changed. Tooling is just way, way better. I'm primarily web-oriented, and using React and Apollo together the past few years really opened my eyes to building rich apps. And I deeply apologize for using the phrase rich apps; I don't think I've ever said such Enterprisey words before.

        But yeah, things are different now. I still love Rails, and still use it for a lot of apps I build. But it's that silly rich apps phrase that's the problem. Users have way more comprehensive expectations than they did even five years ago, and the JS community does a good job at building tools and tech that tackle the problems of making heavy, complicated UI and frontend work.

        Obviously there's a lot of things happening here, so just saying "JavaScript isn't terrible" might encompass a huge amount of libraries and frameworks. But if you're like me, yeah, give things another shot- I'm somehow not hating on JavaScript anymore and... gulp... I kinda love it.

        See more
        Conor Myhrvold
        Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 12.6M views

        How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

        Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

        Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

        https://eng.uber.com/distributed-tracing/

        (GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

        Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

        See more
        Python logo

        Python

        244.7K
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        A clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.
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        PROS OF PYTHON
        • 1.2K
          Great libraries
        • 962
          Readable code
        • 847
          Beautiful code
        • 788
          Rapid development
        • 690
          Large community
        • 438
          Open source
        • 393
          Elegant
        • 282
          Great community
        • 272
          Object oriented
        • 220
          Dynamic typing
        • 77
          Great standard library
        • 60
          Very fast
        • 55
          Functional programming
        • 49
          Easy to learn
        • 45
          Scientific computing
        • 35
          Great documentation
        • 29
          Productivity
        • 28
          Easy to read
        • 28
          Matlab alternative
        • 24
          Simple is better than complex
        • 20
          It's the way I think
        • 19
          Imperative
        • 18
          Free
        • 18
          Very programmer and non-programmer friendly
        • 17
          Powerfull language
        • 17
          Machine learning support
        • 16
          Fast and simple
        • 14
          Scripting
        • 12
          Explicit is better than implicit
        • 11
          Ease of development
        • 10
          Clear and easy and powerfull
        • 9
          Unlimited power
        • 8
          It's lean and fun to code
        • 8
          Import antigravity
        • 7
          Print "life is short, use python"
        • 7
          Python has great libraries for data processing
        • 6
          Although practicality beats purity
        • 6
          Now is better than never
        • 6
          Great for tooling
        • 6
          Readability counts
        • 6
          Rapid Prototyping
        • 6
          I love snakes
        • 6
          Flat is better than nested
        • 6
          Fast coding and good for competitions
        • 6
          There should be one-- and preferably only one --obvious
        • 6
          High Documented language
        • 5
          Great for analytics
        • 5
          Lists, tuples, dictionaries
        • 4
          Easy to learn and use
        • 4
          Simple and easy to learn
        • 4
          Easy to setup and run smooth
        • 4
          Web scraping
        • 4
          CG industry needs
        • 4
          Socially engaged community
        • 4
          Complex is better than complicated
        • 4
          Multiple Inheritence
        • 4
          Beautiful is better than ugly
        • 4
          Plotting
        • 3
          Many types of collections
        • 3
          Flexible and easy
        • 3
          It is Very easy , simple and will you be love programmi
        • 3
          If the implementation is hard to explain, it's a bad id
        • 3
          Special cases aren't special enough to break the rules
        • 3
          Pip install everything
        • 3
          List comprehensions
        • 3
          No cruft
        • 3
          Generators
        • 3
          Import this
        • 3
          If the implementation is easy to explain, it may be a g
        • 2
          Can understand easily who are new to programming
        • 2
          Batteries included
        • 2
          Securit
        • 2
          Good for hacking
        • 2
          Better outcome
        • 2
          Only one way to do it
        • 2
          Because of Netflix
        • 2
          A-to-Z
        • 2
          Should START with this but not STICK with This
        • 2
          Powerful language for AI
        • 1
          Automation friendly
        • 1
          Sexy af
        • 1
          Slow
        • 1
          Procedural programming
        • 0
          Ni
        • 0
          Powerful
        • 0
          Keep it simple
        CONS OF PYTHON
        • 53
          Still divided between python 2 and python 3
        • 28
          Performance impact
        • 26
          Poor syntax for anonymous functions
        • 22
          GIL
        • 19
          Package management is a mess
        • 14
          Too imperative-oriented
        • 12
          Hard to understand
        • 12
          Dynamic typing
        • 12
          Very slow
        • 8
          Indentations matter a lot
        • 8
          Not everything is expression
        • 7
          Incredibly slow
        • 7
          Explicit self parameter in methods
        • 6
          Requires C functions for dynamic modules
        • 6
          Poor DSL capabilities
        • 6
          No anonymous functions
        • 5
          Fake object-oriented programming
        • 5
          Threading
        • 5
          The "lisp style" whitespaces
        • 5
          Official documentation is unclear.
        • 5
          Hard to obfuscate
        • 5
          Circular import
        • 4
          Lack of Syntax Sugar leads to "the pyramid of doom"
        • 4
          The benevolent-dictator-for-life quit
        • 4
          Not suitable for autocomplete
        • 2
          Meta classes
        • 1
          Training wheels (forced indentation)

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