BlackRay is a relational database system designed to offer performance features commonly associated with search engines. It offers SQL support and sophisticated operational and management features. Load-balancing and operational stability by means of N+1 redundance are included. BlackRay is called a "Data Engine" since it combines traditional, relational database features and SQL with the power and flexibility of search engines. It is a true hybrid, offering transaction support, data-versioned snapshots, and sophisticated function-based indices. Wildcards, phonetic, and fuzzy logic searches are supported, as well. BlackRay supports a subset of the SQL92 standard and provides JDBC/ODBC/native driver options via the PostgreSQL protocol, in addition to an API based query option. The project is released under the GPLv2, with some drivers available under BSD-style licenses. Commercial support contracts are available as well.
Pylot is a tool for testing the performance and scalability of Web services. It runs HTTP load tests, which are useful for capacity planning, benchmarking, analysis, and system tuning. It generates concurrent loads, verifies server responses, and produces reports with metrics. Tests suites are executed and monitored from a GUI or the shell.
gprog is a basic GUI pipe meter that shows the percentage complete as data moves through a Unix pipe. It is very fast because it uses a dual process design with a cache oblivious algorithm for self-tuning. Also, the presentation is largely decoupled from the transfer, so that the GUI won't slow down the transfer.
sec-wall is a feature-packed security proxy that supports SSL/TLS, WS-Security, HTTP Auth Basic/Digest, extensible authentication schemes based on custom HTTP headers and XPath expressions, powerful URL matching/rewriting, and an optional header enrichment. It's a security wall with which you can conveniently fence otherwise defenseless backend servers.
Django-live-profiler is a low-overhead data access and code profiler for Django-based applications. Profiling Web applications on a development environment often produces misleading results due to different patterns in the data, different patterns in user behavior, and differences in infrastructure. All existing DB access profiling solutions for Django seem to focus on a single request, but in the real world certain queries might be negligible in a single request while still putting a considerable strain the database across all requests. Django-live-profiler aims to solve these issues by collecting database usage data from a live application.