Comparing Lithops with other distributed computing frameworks#

In a nutshell, Lithops differs from other distributed computing frameworks in that Lithops leverages serverless functions to compute massively parallel computations.

In addition, Lithops provides a simple and easy-to-use interface to access and process data stored in Object Storage from your serverless functions.

Moreover, Lithops abstract design allows seamlessly portability between clouds and FaaS services, avoiding vendor lock-in.


PyWren is Lithops’ “father” project. PyWren was only designed to run in AWS Lambda with a Conda environment and only supported Python 2.7. In 2018, Lithops’ creators forked PyWren and adapted it to IBM Cloud Functions, which, in contrast, uses a Docker runtime. The authors also explored new usages for PyWren, like processing Big Data from Object Storage. Then, on September 2020, IBM PyWren authors decided that the project had evolved enough to no longer be considered a simple fork of PyWren for IBM cloud and became Lithops. With this change, the project would no longer be tied to the old PyWren model and could move to more modern features such as mulit-cloud support or the transparent multiprocessing interface.

You can read more about PyWren IBM Cloud at the Middleware’18 industry paper Serverless Data Analytics in the IBM Cloud.

Ray and Dask#

In comparison with Lithops, both Ray and Dask leverage a cluster of nodes for distributed computing, while Lithops mainly leverages serverless functions. This restraint makes Ray much less flexible than Lithops in terms of scalability.

Although Dask and Ray can scale and adapt the resources to the amount of computation needed, they don’t scale to zero since they must keep a “head node” or “master” that controls the cluster and must be kept up.

In any case, the capacity and scalability of Ray or Dask in IaaS using virtual machines is not comparable to that of serverless functions.


Much like Ray or Dask, PySpark is a distributed computing framework that uses cluster technologies. PySpark provides Python bindings for Spark. Spark is designed to work with a fixed-size node cluster, and it is typically used to process data from on-prem HDFS and analyze it using SparkSQL and Spark DataFrame.

Serverless Framework#

Serverless Framework is a tool to develop serverless applications (mainly NodeJS) and deploy them seemlessly on AWS, GCP or Azure.

Although both Serverless Framework and Lithops use serverless functions, their objective is completely different: Serverless Framework aims to provide an easy-to-use tool to develop applications related to web services, like HTTP APIs, while Lithops aims to develop applications related to highly parallel scientific computation and Big Data processing.