by Christos Kalantzis In an article we posted in November 2011, Benchmarking Cassandra Scalability on AWS â Over a million writes per second, we showed how Cassandra (C*) scales linearly as you add more nodes to a cluster. With the advent of new EC2 instance types, we decided to revisit this test. Unlike the initial post, we were not interested in proving C*âs scalability. Instead, we were looking
Highly complex services, such as those here at Facebook, have large source code bases in order to deliver a wide range of features and functionality. Even after the machine code for one of these services is compiled, it can range from 10s to 100s of megabytes in size, which is often too large to fit in any modern CPU instruction cache. As a result, the hardware spends a considerable amount of proc
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In this blog post, Iâll look at MyRocks performance through some benchmark testing. As the MyRocks storage engine (based on the RocksDB key-value store http://rocksdb.org ) is now available as part of Percona Server for MySQL 5.7, I wanted to take a look at how it performs on a relatively high-end server and SSD storage. I wanted to check how it performs for different amounts of available memory f
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gperftools ---------- (originally Google Performance Tools) The fastest malloc weâve seen; works particularly well with threads and STL. Also: thread-friendly heap-checker, heap-profiler, and cpu-profiler. OVERVIEW --------- gperftools is a collection of a high-performance multi-threaded malloc() implementation, plus some pretty nifty performance analysis tools. gperftools is distributed under the
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Apache Impala is a modern, open-source MPP SQL engine architected from the ground up for the Hadoop data processing environment. Impala provides low latency and high concurrency for BI/analytic read-mostly queries on Hadoop, not delivered by batch frameworks such as Hive or SPARK. Impala is written from the ground up in C++ and Java. It maintains Hadoopâs flexibility by utilizing standard componen
meltdown_redis.md Meltdown fix impact on Redis performances in virtualized environments UPDATE: apparently kernel difference may have a serious impact, so I'll redo the test from scratch. Test performed with AOF enabled, fsync policy 1 second, allowing the rewrites to be triggered. Command lines used: ./redis-benchmark -q -P 32 -n 1000000 -t set,get -r 1000000 ./redis-benchmark -q -n 1000000 -t se
ddb_benchmark.md Write Performance Benchmark This document will allow anyone to verify the benchmark result of writing 2 - 3 million metrics per second into DalmatinerDB. This is a single node benchmark to keep things simple and easily comparable between time series databases that don't support clustering. We will setup 2 Haggar servers to generate metrics and fire them at a single node Dalmatiner
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It makes me smile when someone raves about how fast this website loads, because that's no accident. We put a lot of effort into making it so. It is the sort of thing that usually goes unnoticed, but when your readers are developers, there's a better chance they notice and appreciate it. I have written about this in the past, but it's worth re-examining because these ideas are always evolving. From
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