Remove Architecture Remove Benchmarking Remove Design Remove Latency
article thumbnail

Why applying chaos engineering to data-intensive applications matters

Dynatrace

Stream processing One approach to such a challenging scenario is stream processing, a computing paradigm and software architectural style for data-intensive software systems that emerged to cope with requirements for near real-time processing of massive amounts of data. We designed experimental scenarios inspired by chaos engineering.

article thumbnail

Supercomputing Predictions: Custom CPUs, CXL3.0, and Petalith Architectures

Adrian Cockcroft

Here’s some predictions I’m making: Jack Dongarra’s efforts to highlight the low efficiency of the HPCG benchmark as an issue will influence the next generation of supercomputer architectures to optimize for sparse matrix computations. Next generation architectures will use CXL3.0 petaflops, which is 0.8% of peak capacity.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

How To Scale a Single-Host PostgreSQL Database With Citus

Percona

Leveraging pgbench , which is a benchmarking utility that comes bundled with PostgreSQL, I will put the cluster through its paces by executing a series of DML operations. And now, execute the benchmark: -- execute the following on the coordinator node pgbench -c 20 -j 3 -T 60 -P 3 pgbench The results are not pretty.

Database 102
article thumbnail

Redis vs Memcached in 2024

Scalegrid

Key Takeaways Redis offers complex data structures and additional features for versatile data handling, while Memcached excels in simplicity with a fast, multi-threaded architecture for basic caching needs. Redis Data Types and Structures The design of Redis’s data structures emphasizes versatility.

Cache 130
article thumbnail

SLOG: serializable, low-latency, geo-replicated transactions

The Morning Paper

SLOG: serializable, low-latency, geo-replicated transactions Ren et al., That’s where SLOG (Serializable LOw-latency, Geo-replicated transactions) comes in. Data is replicated across regions, but for every data item one of these regions is designated as its home replica. VLDB’19. Is my data at home?

Latency 70
article thumbnail

The evolution of single-core bandwidth in multicore processors

John McCalpin

I have a lot of historical data using my ReadOnly benchmark (as described in some of the earliest entries in this blog [link] A read-only access pattern removes the need to understand and explain the many complexities associated with the “streaming stores” typically used in the STREAM benchmark (e.g., Stay tuned!

article thumbnail

Choosing a cloud DBMS: architectures and tradeoffs

The Morning Paper

Choosing a cloud DBMS: architectures and tradeoffs Tan et al., use the TPC-H benchmark to assess Redshift, Redshift Spectrum, Athena, Presto, Hive, and Vertica to find out what works best and the trade-offs involved. The design space. in the TPC-H Benchmark Standard for details of the queries). VLDB’19.