Essential Optimization Methods to Make Apache Spark Work Faster

This 12-page technical report provides guidelines for optimizing Apache Spark and two of its modules: Spark Core and Spark SQL. The study features the performance results of Apache Spark before and after optimization. The paper also includes four comparative diagrams and four descriptive tables, which illustrate performance differences in detail.

Technical NoSQL Comparison Report 2019: Couchbase Server v6.0, DataStax Enterprise v6.7 (Cassandra), and MongoDB v4.0

This technical report provides an in-depth analysis of the three leading NoSQL systems: Couchbase Server v6.0, MongoDB v4.0, and DataStax Enterprise v6.7 (Cassandra). The comparison evaluates the solutions across 20+ criteria, such as performance, availability, ease of installation and maintenance, data consistency, fault tolerance, replication, recovery, scalability, etc. The paper includes 26 scoring tables and 17 diagrams to support the evaluation results of the three systems.

NoSQL Performance Benchmark 2018: Couchbase Server v5.5, DataStax Enterprise v6 (Cassandra), and MongoDB v3.6

This technical report compares three popular NoSQL systems: Couchbase Server v5.5, DataStax Enterprise v6 (Cassandra), and MongoDB v3.6. The evaluation includes performance results on 3 different cluster configurations and 4 varying workloads. The paper also provides comparative tables and diagrams which illustrate throughput and latencies measured in greater detail.

DataStax Cassandra vs. Couchbase Server: Architectural Differences and Their Impact

This technical report explores the architectural differences between the two popular NoSQL databases: Cassandra 2.1.x (DataStax Enterprise) and Couchbase Server 3.x. In detail, it describes how these discrepancies impact availability, scalability, and performance. The paper provides a detailed overview of topology, data model, partitioning, replication, caching, etc.

2017 NoSQL Technical Comparison Report: Cassandra (DataStax), MongoDB, and Couchbase Server

This report provides an in-depth analysis of the leading NoSQL systems: DataStax Enterprise v5.0 (Cassandra), MongoDB v3.4, and Couchbase Server v5.0. Approaching the databases from the perspective of architecture, development, and administration, the research compares the solutions across 20+ criteria: ease of installation/configuration, scalability, availability, maintenance, data consistency, fault tolerance, replication, recovery, etc. The benchmark features a scoring framework/template for evaluating and comparing NoSQL data stores for your particular scenario—depending on the weight of each criterion.

Reference Architecture: Multi-Datacenter Cloud Foundry with Concourse and Vault

This 11-page technical paper describes how to manage secure Cloud Foundry deployments—distributed across multiple data centers. Providing a reference architecture, the guide explores how to create repeatable and secure Cloud Foundry deployments with related services using Vault, Concourse CI, and BOSH. Highlighting each of the components and pipelines involved into the workflow, the document also overviews dependencies between the elements of the architecture.

Performance Comparison of Ruby Frameworks, App Servers, Template Engines, and ORMs

This 15-page benchmark demonstrates performance results of the most popular Ruby frameworks, template engines, Rack application servers, and Ruby ORM tools. All the Ruby frameworks and tools were tested in the production mode and with disabled logging to enable equal conditions.

Performance Comparison of Ruby Frameworks: Goliath, Ruby on Rails, Sinatra, Padrino, and Espresso

This 15-page technical study compares five popular Ruby frameworks in terms of performance. The research paper includes performance results under four workloads: views (Slim), MySQL (Sequel), views and MySQL, and no views/DB. For each framework, similar test applications were built to find out how much time it takes to process 1,000 requests.

Performance of Distributed TensorFlow: A Multi-Node and Multi-GPU Configuration

This 20-page technical research embodies performance evaluation of distributed training with TensorFlow under two scenarios: a multi-node and multi-GPU infrastructure configuration. The benchmark was carried out using the Inception architecture as a neural network model and the Camelyon16 data as a training set. To test training scalability of distributed TensorFlow running on an Amazon EC2 cluster, the g2.2xlarge and g2.8xlarge instance types were employed.

Performance of Deep Learning Frameworks: Caffe, Deeplearning4j, TensorFlow, Theano, and Torch

This 15-page research compares five popular deep learning tools—Caffe, Deeplearning4j, TensorFlow, Theano, and Torch—in terms of training performance and accuracy. When testing the frameworks, a fully connected neural network architecture was used for classifying digits from the MNIST data set. The paper also provides information on how speed and accuracy are affected by changes in the network “depth” and “width” (the data is available for the Tanh and ReLU activation functions).