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SQL 2016 – It Just Runs Faster Announcement

SQL Server According to Bob

My development collogues and I are starting a regular blog series, outlining the vast range of scalability improvements, allowing SQL Server 2016 to run across a wide array of hardware configurations, faster and better than previous releases of SQL Server. Try SQL Server 2016 Today. Table of Contents.

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Strongly Type Those Table-Valued Parameters

SQL Performance

Table-valued parameters have been around since SQL Server 2008 and provide a useful mechanism for sending multiple rows of data to SQL Server, brought together as a single parameterized call. The DataTable can be created multiple ways in the.NET code, but a common way to create the table is something like the following: System.

Servers 55
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Finding Distinct Values Quickly

SQL Performance

It comes set to SQL Server 2008 compatibility (level 100), but we will start with a more modern setting of SQL Server 2017 (level 140): ALTER DATABASE StackOverflow2013 SET COMPATIBILITY_LEVEL = 140 ; The tests were performed on my laptop using SQL Server 2019 CU 2. The Sort only has 19 rows to process, so it consumes only 1ms or so.

Servers 86
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Compiler bug? Linker bug? Windows Kernel bug.

Randon ASCII

In this particular investigation, which spanned twenty months, we suspected hardware failure, compiler bugs, linker bugs, and other possibilities. Jumping too quickly to blaming hardware or build tools is a classic mistake, but in this case the mistake was that we weren’t thinking big enough. 9658 errors : @$ip (00408000-00415815).

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Solaris to Linux Migration 2017

Brendan Gregg

It uses a Solaris Porting Layer (SPL) to provide a Solaris-kernel interface on Linux, so that unmodified ZFS code can execute. There's also a ZFS send/recv code path that should try to use the TASK_INTERRUPTIBLE flag (as suggested by a coworker), to avoid a kernel hang (can't kill -9 the process). LTS (April 2016).

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Being Practical

Tim Kadlec

When he first gave the presentation in 2008, he was working at this massive company called Yahoo! And so it’s important to continually invest in stability, in strong infrastructure, in stable code, so that we have that strong platform to stand on as the next thing happens. That was 2008. Here at Yahoo!, and less so to others.

Google 40
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Structural Evolutions in Data

O'Reilly

” Each step has been a twist on “what if we could write code to interact with a tamper-resistant ledger in real-time?” Doubly so as hardware improved, eating away at the lower end of Hadoop-worthy work. Mental contortions led to code contortions led to frustration. And, often, to giving up.