What is PySpark and what are its top alternatives?
Top Alternatives to PySpark
- Scala
Scala is an acronym for “Scalable Language”. This means that Scala grows with you. You can play with it by typing one-line expressions and observing the results. But you can also rely on it for large mission critical systems, as many companies, including Twitter, LinkedIn, or Intel do. To some, Scala feels like a scripting language. Its syntax is concise and low ceremony; its types get out of the way because the compiler can infer them. ...
- 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. ...
- 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. ...
- Pandas
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more. ...
- 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. ...
- PyTorch
PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc. ...
- Dask
It is a versatile tool that supports a variety of workloads. It is composed of two parts: Dynamic task scheduling optimized for computation. This is similar to Airflow, Luigi, Celery, or Make, but optimized for interactive computational workloads. Big Data collections like parallel arrays, dataframes, and lists that extend common interfaces like NumPy, Pandas, or Python iterators to larger-than-memory or distributed environments. These parallel collections run on top of dynamic task schedulers. ...
- jQuery
jQuery is a cross-platform JavaScript library designed to simplify the client-side scripting of HTML. ...
PySpark alternatives & related posts
- Static typing188
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- Types138
- Concurrency95
- Actor library88
- Solve functional problems86
- Open source81
- Solve concurrency in a safer way80
- Functional44
- Fast24
- Generics23
- It makes me a better engineer18
- Syntactic sugar17
- Scalable13
- First-class functions10
- Type safety10
- Interactive REPL9
- Expressive8
- SBT7
- Case classes6
- Implicit parameters6
- Rapid and Safe Development using Functional Programming4
- JVM, OOP and Functional programming, and static typing4
- Object-oriented4
- Used by Twitter4
- Functional Proframming3
- Spark2
- Beautiful Code2
- Safety2
- Growing Community2
- DSL1
- Rich Static Types System and great Concurrency support1
- Naturally enforce high code quality1
- Akka Streams1
- Akka1
- Reactive Streams1
- Easy embedded DSLs1
- Mill build tool1
- Freedom to choose the right tools for a job0
- Slow compilation time11
- Multiple ropes and styles to hang your self7
- Too few developers available6
- Complicated subtyping4
- My coworkers using scala are racist against other stuff2
related Scala posts
I am new to Apache Spark and Scala both. I am basically a Java developer and have around 10 years of experience in Java.
I wish to work on some Machine learning or AI tech stacks. Please assist me in the tech stack and help make a clear Road Map. Any feedback is welcome.
Technologies apart from Scala and Spark are also welcome. Please note that the tools should be relevant to Machine Learning or Artificial Intelligence.
Lumosity is home to the world's largest cognitive training database, a responsibility we take seriously. For most of the company's history, our analysis of user behavior and training data has been powered by an event stream--first a simple Node.js pub/sub app, then a heavyweight Ruby app with stronger durability. Both supported decent throughput and latency, but they lacked some major features supported by existing open-source alternatives: replaying existing messages (also lacking in most message queue-based solutions), scaling out many different readers for the same stream, the ability to leverage existing solutions for reading and writing, and possibly most importantly: the ability to hire someone externally who already had expertise.
We ultimately migrated to Kafka in early- to mid-2016, citing both industry trends in companies we'd talked to with similar durability and throughput needs, the extremely strong documentation and community. We pored over Kyle Kingsbury's Jepsen post (https://aphyr.com/posts/293-jepsen-Kafka), as well as Jay Kreps' follow-up (http://blog.empathybox.com/post/62279088548/a-few-notes-on-kafka-and-jepsen), talked at length with Confluent folks and community members, and still wound up running parallel systems for quite a long time, but ultimately, we've been very, very happy. Understanding the internals and proper levers takes some commitment, but it's taken very little maintenance once configured. Since then, the Confluent Platform community has grown and grown; we've gone from doing most development using custom Scala consumers and producers to being 60/40 Kafka Streams/Connects.
We originally looked into Storm / Heron , and we'd moved on from Redis pub/sub. Heron looks great, but we already had a programming model across services that was more akin to consuming a message consumers than required a topology of bolts, etc. Heron also had just come out while we were starting to migrate things, and the community momentum and direction of Kafka felt more substantial than the older Storm. If we were to start the process over again today, we might check out Pulsar , although the ecosystem is much younger.
To find out more, read our 2017 engineering blog post about the migration!
Python
- Great libraries1.2K
- Readable code963
- Beautiful code847
- Rapid development788
- Large community691
- Open source438
- Elegant393
- Great community282
- Object oriented273
- Dynamic typing221
- Great standard library77
- Very fast60
- Functional programming55
- Easy to learn50
- Scientific computing46
- Great documentation35
- Productivity29
- Matlab alternative28
- Easy to read28
- Simple is better than complex24
- It's the way I think20
- Imperative19
- Very programmer and non-programmer friendly18
- Free18
- Machine learning support17
- Powerfull language17
- Fast and simple16
- Scripting14
- Explicit is better than implicit12
- Ease of development11
- Clear and easy and powerfull10
- Unlimited power9
- Import antigravity8
- It's lean and fun to code8
- Print "life is short, use python"7
- Python has great libraries for data processing7
- High Documented language6
- I love snakes6
- Readability counts6
- Rapid Prototyping6
- Now is better than never6
- Although practicality beats purity6
- Flat is better than nested6
- Great for tooling6
- There should be one-- and preferably only one --obvious6
- Fast coding and good for competitions6
- Web scraping5
- Lists, tuples, dictionaries5
- Great for analytics5
- Beautiful is better than ugly4
- Easy to learn and use4
- Easy to setup and run smooth4
- Multiple Inheritence4
- CG industry needs4
- Socially engaged community4
- Complex is better than complicated4
- Plotting4
- Simple and easy to learn4
- List comprehensions3
- Powerful language for AI3
- Flexible and easy3
- It is Very easy , simple and will you be love programmi3
- Many types of collections3
- If the implementation is easy to explain, it may be a g3
- If the implementation is hard to explain, it's a bad id3
- Special cases aren't special enough to break the rules3
- Pip install everything3
- No cruft3
- Generators3
- Import this3
- Batteries included2
- Securit2
- Can understand easily who are new to programming2
- Should START with this but not STICK with This2
- A-to-Z2
- Because of Netflix2
- Only one way to do it2
- Better outcome2
- Good for hacking2
- Best friend for NLP1
- Sexy af1
- Procedural programming1
- Automation friendly1
- Slow1
- Keep it simple0
- Powerful0
- Ni0
- Still divided between python 2 and python 353
- Performance impact28
- Poor syntax for anonymous functions26
- GIL22
- Package management is a mess19
- Too imperative-oriented14
- Hard to understand12
- Dynamic typing12
- Very slow12
- Indentations matter a lot8
- Not everything is expression8
- Incredibly slow7
- Explicit self parameter in methods7
- Requires C functions for dynamic modules6
- Poor DSL capabilities6
- No anonymous functions6
- Fake object-oriented programming5
- Threading5
- The "lisp style" whitespaces5
- Official documentation is unclear.5
- Hard to obfuscate5
- Circular import5
- Lack of Syntax Sugar leads to "the pyramid of doom"4
- The benevolent-dictator-for-life quit4
- Not suitable for autocomplete4
- Meta classes2
- Training wheels (forced indentation)1
related Python posts
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
Winds 2.0 is an open source Podcast/RSS reader developed by Stream with a core goal to enable a wide range of developers to contribute.
We chose JavaScript because nearly every developer knows or can, at the very least, read JavaScript. With ES6 and Node.js v10.x.x, it’s become a very capable language. Async/Await is powerful and easy to use (Async/Await vs Promises). Babel allows us to experiment with next-generation JavaScript (features that are not in the official JavaScript spec yet). Yarn allows us to consistently install packages quickly (and is filled with tons of new tricks)
We’re using JavaScript for everything – both front and backend. Most of our team is experienced with Go and Python, so Node was not an obvious choice for this app.
Sure... there will be haters who refuse to acknowledge that there is anything remotely positive about JavaScript (there are even rants on Hacker News about Node.js); however, without writing completely in JavaScript, we would not have seen the results we did.
#FrameworksFullStack #Languages
- Open-source61
- Fast and Flexible48
- One platform for every big data problem8
- Great for distributed SQL like applications8
- Easy to install and to use6
- Works well for most Datascience usecases3
- Interactive Query2
- Machine learning libratimery, Streaming in real2
- In memory Computation2
- Speed4
related Apache Spark posts
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:
- Our Algorithms Tour: https://algorithms-tour.stitchfix.com/
- Our blog: https://multithreaded.stitchfix.com/blog/
- Careers: https://multithreaded.stitchfix.com/careers/
#DataScience #DataStack #Data
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.
Pandas
- Easy data frame management21
- Extensive file format compatibility2
related Pandas posts
Server side
We decided to use Python for our backend because it is one of the industry standard languages for data analysis and machine learning. It also has a lot of support due to its large user base.
Web Server: We chose Flask because we want to keep our machine learning / data analysis and the web server in the same language. Flask is easy to use and we all have experience with it. Postman will be used for creating and testing APIs due to its convenience.
Machine Learning: We decided to go with PyTorch for machine learning since it is one of the most popular libraries. It is also known to have an easier learning curve than other popular libraries such as Tensorflow. This is important because our team lacks ML experience and learning the tool as fast as possible would increase productivity.
Data Analysis: Some common Python libraries will be used to analyze our data. These include NumPy, Pandas , and matplotlib. These tools combined will help us learn the properties and characteristics of our data. Jupyter notebook will be used to help organize the data analysis process, and improve the code readability.
Client side
UI: We decided to use React for the UI because it helps organize the data and variables of the application into components, making it very convenient to maintain our dashboard. Since React is one of the most popular front end frameworks right now, there will be a lot of support for it as well as a lot of potential new hires that are familiar with the framework. CSS 3 and HTML5 will be used for the basic styling and structure of the web app, as they are the most widely used front end languages.
State Management: We decided to use Redux to manage the state of the application since it works naturally to React. Our team also already has experience working with Redux which gave it a slight edge over the other state management libraries.
Data Visualization: We decided to use the React-based library Victory to visualize the data. They have very user friendly documentation on their official website which we find easy to learn from.
Cache
- Caching: We decided between Redis and memcached because they are two of the most popular open-source cache engines. We ultimately decided to use Redis to improve our web app performance mainly due to the extra functionalities it provides such as fine-tuning cache contents and durability.
Database
- Database: We decided to use a NoSQL database over a relational database because of its flexibility from not having a predefined schema. The user behavior analytics has to be flexible since the data we plan to store may change frequently. We decided on MongoDB because it is lightweight and we can easily host the database with MongoDB Atlas . Everyone on our team also has experience working with MongoDB.
Infrastructure
- Deployment: We decided to use Heroku over AWS, Azure, Google Cloud because it is free. Although there are advantages to the other cloud services, Heroku makes the most sense to our team because our primary goal is to build an MVP.
Other Tools
Communication Slack will be used as the primary source of communication. It provides all the features needed for basic discussions. In terms of more interactive meetings, Zoom will be used for its video calls and screen sharing capabilities.
Source Control The project will be stored on GitHub and all code changes will be done though pull requests. This will help us keep the codebase clean and make it easy to revert changes when we need to.
Should I continue learning Django or take this Spring opportunity? I have been coding in python for about 2 years. I am currently learning Django and I am enjoying it. I also have some knowledge of data science libraries (Pandas, NumPy, scikit-learn, PyTorch). I am currently enhancing my web development and software engineering skills and may shift later into data science since I came from a medical background. The issue is that I am offered now a very trustworthy 9 months program teaching Java/Spring. The graduates of this program work directly in well know tech companies. Although I have been planning to continue with my Python, the other opportunity makes me hesitant since it will put me to work in a specific roadmap with deadlines and mentors. I also found on glassdoor that Spring jobs are way more than Django. Should I apply for this program or continue my journey?
- Great ecosystem39
- One stack to rule them all11
- Great load balancer4
- Amazon aws1
- Java syntax1
related Hadoop posts
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.
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 )
- Easy to use15
- Developer Friendly11
- Easy to debug10
- Sometimes faster than TensorFlow7
- Lots of code3
- It eats poop1
related PyTorch posts
Server side
We decided to use Python for our backend because it is one of the industry standard languages for data analysis and machine learning. It also has a lot of support due to its large user base.
Web Server: We chose Flask because we want to keep our machine learning / data analysis and the web server in the same language. Flask is easy to use and we all have experience with it. Postman will be used for creating and testing APIs due to its convenience.
Machine Learning: We decided to go with PyTorch for machine learning since it is one of the most popular libraries. It is also known to have an easier learning curve than other popular libraries such as Tensorflow. This is important because our team lacks ML experience and learning the tool as fast as possible would increase productivity.
Data Analysis: Some common Python libraries will be used to analyze our data. These include NumPy, Pandas , and matplotlib. These tools combined will help us learn the properties and characteristics of our data. Jupyter notebook will be used to help organize the data analysis process, and improve the code readability.
Client side
UI: We decided to use React for the UI because it helps organize the data and variables of the application into components, making it very convenient to maintain our dashboard. Since React is one of the most popular front end frameworks right now, there will be a lot of support for it as well as a lot of potential new hires that are familiar with the framework. CSS 3 and HTML5 will be used for the basic styling and structure of the web app, as they are the most widely used front end languages.
State Management: We decided to use Redux to manage the state of the application since it works naturally to React. Our team also already has experience working with Redux which gave it a slight edge over the other state management libraries.
Data Visualization: We decided to use the React-based library Victory to visualize the data. They have very user friendly documentation on their official website which we find easy to learn from.
Cache
- Caching: We decided between Redis and memcached because they are two of the most popular open-source cache engines. We ultimately decided to use Redis to improve our web app performance mainly due to the extra functionalities it provides such as fine-tuning cache contents and durability.
Database
- Database: We decided to use a NoSQL database over a relational database because of its flexibility from not having a predefined schema. The user behavior analytics has to be flexible since the data we plan to store may change frequently. We decided on MongoDB because it is lightweight and we can easily host the database with MongoDB Atlas . Everyone on our team also has experience working with MongoDB.
Infrastructure
- Deployment: We decided to use Heroku over AWS, Azure, Google Cloud because it is free. Although there are advantages to the other cloud services, Heroku makes the most sense to our team because our primary goal is to build an MVP.
Other Tools
Communication Slack will be used as the primary source of communication. It provides all the features needed for basic discussions. In terms of more interactive meetings, Zoom will be used for its video calls and screen sharing capabilities.
Source Control The project will be stored on GitHub and all code changes will be done though pull requests. This will help us keep the codebase clean and make it easy to revert changes when we need to.
Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:
At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.
TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.
Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:
(Direct GitHub repo: https://github.com/uber/horovod)
related Dask posts
- Cross-browser1.3K
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- Easy459
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- Javascript1
- Easy Setup1
- Open Source, Simple, Easy Setup1
- It Just Works1
- Industry acceptance1
- Allows great manipulation of HTML and CSS1
- Widely Used1
- I love jQuery1
- Large size6
- Sometimes inconsistent API5
- Encourages DOM as primary data source5
- Live events is overly complex feature2
related jQuery posts
The client-side stack of Shopify Admin has been a long journey. It started with HTML templates, jQuery and Prototype. We moved to Batman.js, our in-house Single-Page-Application framework (SPA), in 2013. Then, we re-evaluated our approach and moved back to statically rendered HTML and vanilla JavaScript. As the front-end ecosystem matured, we felt that it was time to rethink our approach again. Last year, we started working on moving Shopify Admin to React and TypeScript.
Many things have changed since the days of jQuery and Batman. JavaScript execution is much faster. We can easily render our apps on the server to do less work on the client, and the resources and tooling for developers are substantially better with React than we ever had with Batman.
#FrameworksFullStack #Languages
I'm planning to create a web application and also a mobile application to provide a very good shopping experience to the end customers. Shortly, my application will be aggregate the product details from difference sources and giving a clear picture to the user that when and where to buy that product with best in Quality and cost.
I have planned to develop this in many milestones for adding N number of features and I have picked my first part to complete the core part (aggregate the product details from different sources).
As per my work experience and knowledge, I have chosen the followings stacks to this mission.
UI: I would like to develop this application using React, React Router and React Native since I'm a little bit familiar on this and also most importantly these will help on developing both web and mobile apps. In addition, I'm gonna use the stacks JavaScript, jQuery, jQuery UI, jQuery Mobile, Bootstrap wherever required.
Service: I have planned to use Java as the main business layer language as I have 7+ years of experience on this I believe I can do better work using Java than other languages. In addition, I'm thinking to use the stacks Node.js.
Database and ORM: I'm gonna pick MySQL as DB and Hibernate as ORM since I have a piece of good knowledge and also work experience on this combination.
Search Engine: I need to deal with a large amount of product data and it's in-detailed info to provide enough details to end user at the same time I need to focus on the performance area too. so I have decided to use Solr as a search engine for product search and suggestions. In addition, I'm thinking to replace Solr by Elasticsearch once explored/reviewed enough about Elasticsearch.
Host: As of now, my plan to complete the application with decent features first and deploy it in a free hosting environment like Docker and Heroku and then once it is stable then I have planned to use the AWS products Amazon S3, EC2, Amazon RDS and Amazon Route 53. I'm not sure about Microsoft Azure that what is the specialty in it than Heroku and Amazon EC2 Container Service. Anyhow, I will do explore these once again and pick the best suite one for my requirement once I reached this level.
Build and Repositories: I have decided to choose Apache Maven and Git as these are my favorites and also so popular on respectively build and repositories.
Additional Utilities :) - I would like to choose Codacy for code review as their Startup plan will be very helpful to this application. I'm already experienced with Google CheckStyle and SonarQube even I'm looking something on Codacy.
Happy Coding! Suggestions are welcome! :)
Thanks, Ganesa