Understanding Thread-Local Storage

Ewald Cress explains thread-local storage and its relationship with SQL Server workers:

So far, this is all rather abstract, and we’re just seeing numbers which may or may not be pointers, pointing to heaven knows what. Let me finish off by illuminating one trail to something we can relate to.

Out of the five local storage slots which contain something, the first one here points to 00000000`3b656040. As it turns out, this is an instance of the CCompExecCtxtBasic class, and you’ll just have to take my word for it today. Anyway, we’re hunting it for its meat, rather than for its name. Have some:

Click through for details, including a graphic.

Traipsing Through commondelete

Ewald Cress focuses on a function named commondelete to explain reference counting and an elegant use for vftables:

Notionally we can imagine a global portfolio of active memory allocations, each chunk uniquely identified by its starting address. When we want memory, we ask the global memory manager to lend us some from the unused pool, and when we’re done with it, we hand it back to that memory manager, who carefully locks its internal structures during such operations, because we should only access mutable global state in a single-threaded manner, and…. Oops. No, no, double no. That is not how SQL Server does things, right?

Okay, we know that there are actually a variety of memory allocators out there. If nothing else, this avoid the single bottleneck problem. But now the question becomes one of knowing which allocator to return a chunk of memory to after we’re done with it.

As usual, this is a deep and interesting blog post from Ewald.

Virtual Function Tables

Ewald Cress continues his descent into the bowels of SQL Server, this time looking at vftables:

The first and simpler GetData() overload doesn’t show up in a vftable, but the second does. Oddly, the vftable for the second one lives at an offset of +0x1448 into the class instance – you’re going to have to trust me on this one. So the rcx passed into either variation will actually be the same one! But if the virtual version is called, it needs to find its position relative to +0x1448 dynamically, by doing a data lookup. We can confirm that by peeking at what is saved four bytes earlier at +0x1444, and that is indeed the value zero.

Ewald explains how this is vital to multiple inheritance and this post is only guaranteed to make your brain hurt a little bit.

PFS Page Repair

Paul Randal explains why DBCC CHECKDB cannot repair Page Free Space pages:

PFS pages occur every 8088 pages in every data file and store a byte of information about itself and the following 8087 pages. The most important piece of information it stores is whether a page is allocated (in use) or not. You can read more about PFS pages and the other per-database allocation bitmaps in this blog post.

So why can’t they be repaired by DBCC CHECKDB, when all the other per-database allocation bitmaps can?

The answer is that the is-this-page-allocated-or-not information is not duplicated anywhere else in the database, and it’s impossible to reconstruct it in all cases.

In case you’re not particularly familiar with PFS pages, Paul has a blog post from 2006 describing GAM, SGAM, and PFS pages.

Understanding The Stack

Ewald Cress digs into stack frames, using a few SQL Server functions as examples:

The rdi register is used as a scratchpad to save the incoming rcx value for later – first it is used to restore the possibly clobered rcx value for the second child function call, and then it becomes the return value going into rax. However, since we don’t want to clobber its value for our caller, we first save its old value and then, at the very end, restore it. This is done through a traditional push/pop pair, which in x64 is only allowed in the function prologue and epilogue.

Similarly, ebx (the bottom half of rbx) is used to save the incoming value of the second parameter in edx. However, here we don’t use a push/pop pair, but instead save it in a slot in the shadow space. Why two different mechanisms for two different registers? Sorry, that’s one for the compiler writers to answer.

There’s a lot of detail involved here, but it’s well worth the read.

Clustered Index And Physical Storage

Wayne Sheffield busts a myth:

In several of my last few blog posts, I’ve shared several methods of getting internal information from a database by using the DBCC PAGE command and utilizing the “WITH TABLERESULTS” option to be allowed to automate this process for further processing. This post will also do this, but in this case, we’ll be using it to bust a common myth—data in a clustered index is physically stored on disk in the order of the clustered index.

Busting this myth

To bust this myth, we’ll create a database, put a table with a clustered index into this database, and then we’ll add some rows in random order. Next, we will show that the rows are stored on the pages in logical order, and then we’ll take a deeper look at the page internals to see that the rows are not stored in physical order.

Read on for the proof.

Thinking About Contexts

Ewald Cress has started a new series on system internals:

Per-processor partitioning of certain thread management functions makes perfect sense, since we’d aim to minimise the amount of global state. Thus each processor would have its own dispatcher state, its own timer list… And hang on, this is familiar territory we know from SQLOS! The only difference is that SQLOS operates on the premise of virtualising a CPU in the form of a Scheduler, whereas the OS kernel deals with physical CPUs, or at least what it honestly believes to be physical CPUs even in the face of CPU virtualisation.

This is a start to a very interesting series.

Thoughts On Cost-Based Optimizers

Joe Chang has the makings of an academic paper on the shortcomings of the current SQL Server cost optimizer model:

It might seem that given the pace of hardware change, such old model cannot possibly valid, resulting horrible execution plans. Around 1995 or so, the high-performance HDD was 7200RPM with a sequential bandwidth of 4-5MB/s. The mean rotational latency for 7200RPM is 4ms. An average seek time of 8.5ms seems reasonable, though I have not kept any documentation from this period. This would correspond to 80 IOPS per disk at queue depth 1 per HDD. So, it seems curious that the SQL Server cost model is based on the random IOPS of 4 disks, but the sequential IO of 2 HDDs.

Performance HDDs progressed first to 10K RPM around 1996, and then to 15K around 2000, with corresponding rotational latencies of 3 and 2ms respectively. Average seek time was reduced over time to 3ms with developments in powerful rare-earth magnets. The 10K HDD could support 125 IOPS and 200 IOPS for the 15K HDD. But no further progress was made on HDD rotational speed. In same time period, hard disk sequential IO phenomenally quickly exceeding 100MB/s in the fourth generation 15K HDD around 2005.

In other words, the SQL Server cost model is based on a 1350/320 = 4.2 ratio. But 15K HDDs in the mid-2000’s were 100MB/s × 128 pages/MB = 12,800 pages/sec sequential to 200 IOPS random for a ratio of 64:1. It turns out that achieving nearly the sequential IO capability of HDDs from SQL Server required a special layout strategy, as outlined in the Fast Track Data Warehouse Reference Architecture papers, which few people followed. This was due to the fixed, inflexible IO pattern of SQL Server, which required the disk configuration to match that of SQL Server instead of being able to adjust the IO pattern to match that of the storage configuration.

It’s worth taking the time to read.  I like Glenn Berry’s idea in the comments of building relative CPU/IO/memory measures and applying them rather than using the same values that were good for twenty years ago.

Virtual Function Calls

Ewald Cress is thinking about virtual function calls:

A virtual function call, on the other hand, is only resolved at runtime. The compiler literally does not know what address is going to get called, and neither does the runtime except in the heat of the moment, because that is going to depend on the type of the object instance that the function is called on. Bear with me, I’ll try and simplify.

A C++ object is just a little chunk of memory: a bunch of related instance variables if you like. All objects of the same class have the same structure in this regard. If you’re wondering about functions (a.k.a. methods), these belong to the class, or put differently, to ALL objects of that class. Once compiled, each method is a chunk of memory with a known address, containing the compiled instructions.

From there, it’s a harrowing journey through bigger layers of indirection.

Looking For Wait Types

Ewald Cress uses the debugger to search for particular waits:

In this case I was looking for PREEMPTIVE_COM_RELEASE, and sys.dm_xe_map_values tells me that on my 2014 RTM instance it has an index of 01d4 hexadecimal. Crazy as it sounds, I’m going to do a simple search through the code to look for places that magic number is used. As a two-byte (word) pattern we’ll get lots of false positives, but fortunately wait types are internally doublewords, with only one bit set in the high-order word. In other words, we’re going to look for the pattern 000101d4, 000201d4, 000401d4 and so forth up to 800001d4. Ignore the meaning of when which bit is going to be set; with only sixteen permutations, it’s quick enough to try them all.

Let’s focus on sqllang as the likely source – the below would apply to any other module too.

This post reminds me that my debugger skills aren’t very good.

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