nosqlbench VS Folly

Not directly related, but https://github.com/nosqlbench/nosqlbench is very flexible benchmark tool for Cassandra and other distributed systems

For performance testing, we also run common benchmarks: the popular YCSB (Yahoo! Cloud Serving Benchmark), NoSQLBench, LinkBench, SysBench, TPC-H, and TPC-C. We also run C Bench, our own Tarantool API benchmark. Its primitive operations are written in C, and scripts are described in Lua.

We used NoSQLBench to perform moderate load benchmarks. It comes with a convenient Docker image that we could use straight away to run stress jobs in our k8s cluster.

Jonathan Shook created NoSQLBench to be a cross-platform performance testing tool that is easier to use than cassandra-stress and (much) more powerful than YCSB; in fact, its scripting layer is powerful enough to support things that no other testing tool can enable, with particular emphasis on modeling complex workloads with fidelity, as well as simulating realistic scenarios such as load spikes. As its name suggests, NoSQLBench is not Cassandra-specific and encourages participation from all who want to contribute; today there are clients for Cassandra, CockroachDB, JDBC, and MongoDB, as well as non-database products Kafka and Pulsar. If you’re serious about performance testing in 2021, you should check out NoSQLBench. You can get started at GitHub. Other useful links: releases, discord, docs.

https://github.com/facebook/folly/commit/b1391e1c57be71c1e2a...

https://github.com/facebook/folly/pull/2153

To set a HP on Linux, Folly just does a relaxed load of the src pointer, release store of the HP, compiler-only barrier, and acquire load. (This prevents the compiler from reordering the 2nd load before the store, right? But to my understanding does not prevent a hypothetical CPU reordering of the 2nd load before the store, which seems potentially problematic!)

Then on the GC/reclaim side of things, after protected object pointers are stored, it does a more expensive barrier[0] before acquire-loading the HPs.

I'll admit, I am not confident I understand why this works. I mean, even on x86, loads can be reordered before earlier program-order stores. So it seems like the 2nd check on the protection side could be ineffective. (The non-Linux portable version just uses an atomic_thread_fence SeqCst on both sides, which seems more obviously correct.) And if they don't need the 2nd load on Linux, I'm unclear on why they do it.

[0]: https://github.com/facebook/folly/blob/main/folly/synchroniz...

(This uses either mprotect to force a TLB flush in process-relevant CPUs, or the newer Linux membarrier syscall if available.)

folly provides functions to resize std::string & std::vector without initialization [0].

[0] https://github.com/facebook/folly/blob/3c8829785e3ce86cb821c...

My implementation was quite similar to Boost's spsc_queue and Facebook's folly/ProducerConsumerQueue.h.

> How is that relevant?

Roaring bitmaps and similar data structures get their speed from decoding together consecutive groups of elements, so if you do sequential decoding or decode a large fraction of the list you get excellent performance.

EF instead excels at random skipping, so if you visit a small fraction of the list you generally get better performance. This is why it works so well for inverted indexes, as generally the queries are very selective (otherwise why do you need an index?) and if you have good intersection algorithms you can skip a large fraction of documents.

I didn't follow the rest of your comment, select is what EF is good at, every other data structure needs a lot more scanning once you land on the right chunk. With BMI2 you can also use the PDEP instruction to accelerate the final select on a 64-bit block: https://github.com/facebook/folly/blob/main/folly/experiment...

C++ with folly's SCOPE_EXIT {} construct:

https://github.com/facebook/folly/blob/main/folly/ScopeGuard...

Seems like github issues taking a long time to get any response: https://github.com/facebook/folly

Can't speak for abseil and tbb, but in folly there are a few solutions for the common problem of sharing state between a writer that updates it very infrequently and concurrent readers that read it very frequently (typical use case is configs).

The most performant solutions are RCU (https://github.com/facebook/folly/blob/main/folly/synchroniz...) and hazard pointers (https://github.com/facebook/folly/blob/main/folly/synchroniz...), but they're not quite as easy to use as a shared_ptr [1].

Then there is simil-shared_ptr implemented with thread-local counters (https://github.com/facebook/folly/blob/main/folly/experiment...).

If you absolutely need a std::shared_ptr (which can be the case if you're working with pre-existing interfaces) there is CoreCachedSharedPtr (https://github.com/facebook/folly/blob/main/folly/concurrenc...), which uses an aliasing trick to transparently maintain per-core reference counts, and scales linearly, but it works only when acquiring the shared_ptr, any subsequent copies of that would still cause contention if passed around in threads.

[1] Google has a proposal to make a smart pointer based on RCU/hazptr, but I'm not a fan of it because generally RCU/hazptr guards need to be released in the same thread that acquired them, and hiding them in a freely movable object looks like a recipe for disaster to me, especially if paired with coroutines https://www.open-std.org/jtc1/sc22/wg21/docs/papers/2020/p05...

Not sure if it's still the case but about 6 years ago Facebook's folly C++ library was something I'd point to for my junior engineers to get a sense of "good" C++ https://github.com/facebook/folly