Analysis updated 2026-07-08 · repo last pushed 2015-06-14
Build an ad platform backend that stores and retrieves user profiles at massive scale with sub-millisecond latency.
Create a recommendation engine that processes data directly inside storage to avoid network round trips.
Power a real-time analytics pipeline that needs consistent, highly available data across multiple data centers.
Store temporary high-speed data in memory with the option to persist to SSD for durability.
| yingfeng/pistachio | asutosh936/job-finder-app | babisha-dev/core-java-programs | |
|---|---|---|---|
| Stars | — | 0 | 0 |
| Language | Java | Java | Java |
| Last pushed | 2015-06-14 | — | 2026-07-04 |
| Maintenance | Dormant | — | Active |
| Setup difficulty | hard | moderate | easy |
| Complexity | 5/5 | 2/5 | 1/5 |
| Audience | ops devops | developer | developer |
Figures from each repo's GitHub metadata at analysis time.
Requires deploying and configuring a distributed multi-server cluster with SSDs across multiple data centers for serious production use.
Pistachio is a storage system designed for applications that need to read and write enormous amounts of data very quickly without ever losing track of what's accurate and up-to-date. Think of it as a giant, highly reliable digital filing cabinet that can be accessed by hundreds of servers simultaneously. It was built to handle serious scale, the README notes it has been used at Yahoo to store over 10 billion user profiles, serving millions of reads per second across eight data centers worldwide. The core promise is that data stays consistent and available even when things go wrong. You can set up the system so that every piece of data is copied across multiple servers. As long as at least one copy survives, your data remains accessible. This means that if a server crashes or a network fails, the application keeps running as if nothing happened. What makes this project interesting is how it thinks about the relationship between data and the work being done with that data. Normally, an application fetches data from storage, processes it somewhere else, then sends the results back, a back-and-forth that creates delays. Pistachio flips this around by letting you push the processing logic directly into the storage system itself. By cutting out the round trips, it can make data-heavy computations dramatically faster. The README claims up to 100x speed improvements with this approach. It also offers flexibility in how data is physically stored, depending on your needs. For temporary data that just needs to be fast, it can live entirely in memory. For workloads with lots of random reads and writes, it can use solid-state drives through pluggable storage engines. This makes it adaptable to different trade-offs between speed, durability, and cost. The project would appeal to teams building large-scale data systems, think ad platforms, recommendation engines, or analytics pipelines, where milliseconds matter and the volume of data is too large for a single machine. It's a Java-based system and is open source under the Apache 2.0 license.
Pistachio is a fast, reliable storage system built for massive scale. It lets you run processing logic directly inside the storage layer to eliminate network delays and speed up data-heavy computations by up to 100x.
Mainly Java. The stack also includes Java.
Dormant — no commits in 2+ years (last push 2015-06-14).
Use freely for any purpose, including commercial use, with no major restrictions. You must include the license notice and state any changes made.
Setup difficulty is rated hard, with roughly 1day+ to a first successful run.
Mainly ops devops.
This repo across BitVibe Labs
Verify against the repo before relying on details.