Remove 2015 Remove Cache Remove Servers
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Mastering Disk Space Management with MongoDB® Storage Engines

Scalegrid

However, it is limited by the available free memory amount, and all data is lost when the server stops. It uses a filesystem cache and write-ahead log for crash recovery. MongoDB makes use of both the filesystem cache and the WiredTiger internal cache. released in December 2015. How Large is Your Database, Really?

Storage 130
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USENIX SREcon APAC 2022: Computing Performance: What's on the Horizon

Brendan Gregg

My personal opinion is that I don't see a widespread need for more capacity given horizontal scaling and servers that can already exceed 1 Tbyte of DRAM; bandwidth is also helpful, but I'd be concerned about the increased latency for adding a hop to more memory.

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The Return of the Frame Pointers

Brendan Gregg

Only in extreme circumstances does the cost (in processor time and I-cache footprint) translate to a tangible benefit - circumstances which usually resort to hand-coded assembly anyway. It shouldn't be 10%, unless it's cache effects. Back-end servers. Don't blame the straw, in this case, don't blame the frame pointers.

Java 137
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Is Intel Doomed in the Server CPU Space?

SQL Performance

A close monitoring of the hardware enthusiast community, including many of the most respected hardware analysts and reviewers paints an even more dire picture about Intel in the server processor space. Despite all of this, Intel is not going to lose their entire server processor business any time soon. So, what has changed my mind?

Servers 46
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How I Used Brotli to Get Even Smaller CSS and JavaScript Files at CDN Scale

CSS - Tricks

In 2015, Google published a blog post announcing Brotli and released its source code on GitHub. It took a few months for major CDN players to support Brotli, but meanwhile it was seeing widespread adoption in tools, services, browsers and servers. Maybe that’s why Pied Piper had to continue rigging its servers for more power.

Cache 80
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Using Parallel Query with Amazon Aurora for MySQL

Percona

I will compare AWS Aurora with MySQL (Percona Server) 5.6 Aurora Parallel Query response time (for queries which can not use indexes) can be 5x-10x better compared to the non-parallel fully cached operations. 84.1 | | version_comment | Percona Server (GPL), Release 84.1, MySQL on ec2.

Cache 47
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USENIX SREcon APAC 2022: Computing Performance: What's on the Horizon

Brendan Gregg

My personal opinion is that I don't see a widespread need for more capacity given horizontal scaling and servers that can already exceed 1 Tbyte of DRAM; bandwidth is also helpful, but I'd be concerned about the increased latency for adding a hop to more memory.