Ayende @ Rahien

Hi!
My name is Oren Eini
Founder of Hibernating Rhinos LTD and RavenDB.
You can reach me by email or phone:

ayende@ayende.com

+972 52-548-6969

, @ Q j

Posts: 6,726 | Comments: 48,713

filter by tags archive

Reviewing FASTERSummary

time to read 4 min | 695 words

FASTER is an interesting project, with some unique approaches to solving their tasks that I haven’t encountered before. When I initially read the paper about a year or so ago, I was impressed with what they were doing, even I didn’t quite grasp exactly what was going on. After reading the code, this is now much clearer. I don’t remember where I read it, but I remember reading a Googler talking about the difference between Microsoft and Google with regards to publishing technical papers. Google would only publish something after it has been in production for a while (and probably ready to sunset Smile) while Microsoft would publish papers about software that hasn’t been deployed yet.

The reason I mention this is that FASTER isn’t suitable for production. Not by a long shot. I’m talking about issues such as swallowing errors, writing to the console as an error handling approach, calling sleep(), lack of logging / tracing / visibility into what is going on in the system. In short, FASTER looks like it was produced to support the paper. It is proof of concept / research code, not something that can take and use.

You can see it clearly in the way that the system is designed to be operated. You have dedicated threads that process requests as fast as they possibly can. However, there is no concept of working in any kind of operational environment, you can’t start using FASTER from an ASP.Net MVC app, for example. They models are just too different. I can think of a few ways to build a server using the FASTER model, but they are all pretty awkward and very specialized. This lead to the next issue with the project, it is highly specialized solution.

It isn’t meant for general consumption. In fact, as I can figure out, this is perfect if you have a relatively small working set that you do a lot of operations on. The examples I have seen given are related to tracking ads, which is a great example. If you want to store impressions on an ad, the active ads are going to pretty small, but you are going to have a lot of impressions on them. For other stuff, I don’t see a lot of usage scenarios.

FASTER is limited in the following ways:

  • Get / Set / Update only – no way to query
  • No support for atomic operations on more than a single key
  • Can support fixed length values only
  • Crash means data loss (of the most recent 14.4 GB, usually)
  • The API is awkward to use, and you need to write a bit of (non trivial) code for each key/val you want to store.
  • No support for compaction of data beyond dropping the oldest entries

Some of these issues can be mitigated. For example, compaction can be implemented, and you can force data to be written to disk faster if you want to, but those aren’t in the box and require careful handling.

Now that I have gone over the code, I’m not quite sure what was the point there, to be honest. In terms of performance, you can get about 25% of the achieved performance by just using ConcurrentDictionary in .NET, I’m pretty sure that you can do better by literally just using a concurrent hash map in C++. This isn’t something that you can use as a primary data store, after all, so I wonder why not just keep all the data in memory and be done with it.

I liked the mutable / read only portions of the log, that is certainly a really nice way to do it, and I’m sure that the epoch idea simplified things during the implementation with the ability to not worry about concurrent accesses. However, the code is complex and I’m pretty sure that it is not going to be fun to debug / work with in real world scenarios.

To sum it up, interesting codebase and approaches, but I would caution from using it for real. The perf numbers are to salivate over,  but the manner in which the benchmark was written means that it is not really applicable for any real world scenario.

Reviewing FASTERWhen the data hits the disk

time to read 5 min | 817 words

So far, I ignored anything in FASTER about how the data actually hits the disk. Based on my reading of the code and the paper, here is what I think that is going on. FASTER works in segments and in conjunction with its allocator. When you create a new instance, you have to define what would be the log size. From looking at the code, they seem to be favoring 16GB as the default size of the log. This is passed to PersistentMemoryMalloc, which uses pages of 32MB each to manage the memory. Out of these 16GB, 14.4GB are considered to be mutable and 1.6 GB is considered to be read only.

On startup, this class allocates two pages (64MB). Whenever FASTER needs more memory, such as to store a new record, it will eventually call to this method:

image

Again we have num_slots that actually means size in bytes, but I’ll leave my pet peeves aside.

You can see that this allocates from tail of the page use Reserve, which does an atomic operation. If we run out of space in the 32MB page, the caller need to call NewPage() to handle the new allocation. This plays together with the buffer management and the epoch. In particular, here is how a new page is allocated in the normal case. Assuming we just started and we consumed 64MB of memory, the new entry will allocate the 3rd page. This will also move the section of read only memory when there is a new page allocated and the number of active pages is beyond 14.4 GB.

image

What this means, in practice, is that FASTER typically has a 14.4GB range in which all operations are working on purely mutable memory. That means that two impressions on the same ad will end up being very close to simply Interlocked.Increment on the relevant value. This is the key for the performance that FASTER exhibits.

What happens once we start going beyond the 14.4 GB? FASTER will begin to move data to the read only section. In this case, it means that the any new modifications to the data will create a new copy of it in the mutable section.

At the same time, PageAlignedShiftReadOnlyAddress() will also starts an async process of writing these readonly pages to disk. Here is how it works:

image

If the read_only_address was updated, FASTER calls to BumpCurrentEpoch() and will execute OnPagesMarkedReadOnly() when the Epoch moves beyond this range (this works because then it is guaranteed that no one may mutate this memory).  That method looks like this:

image

The notion of read_only_address and safe_readonly_only_address is discussed in the paper quite nicely, by the way. AsyncFlushPages() writes the data to disk, as you would expect and updates various in memory structures.

Note that just having the data written to disk doesn’t remove the in memory copy. It is still available in memory until it is pushed back from the log by new data. Now that we understand how the data goes to the log and then to the disk, I want to figure out how the data is written in to the disk itself. Actual writes are handled here:

image

What you can see is that the destination offset is used to divide the data on disk to sections. Each section is 1GB in size. In other words, the way FASTER works is to write the data in 1 GB segments that are sequential over time.

This also plays into the expiration policy that FASTER employs. Because it uses a logs based system, it accumulate data over time and will need to handle that. The current way it deals with the problem is to just delete old files, this gets rid of the data in roughly time based order, which is suitable for the use case that the paper talks about. Another alternative is to read the old files, and move the still valid entries to the start. That doesn’t seem to be implemented and I think it will be pretty hard to do and likely consume a lot of resources.

I’ll probably have another summary post about FASTER, but that is pretty much it. I’ve ignored other parts (recovery, several state machines used to handle internal state, etc), but they aren’t important to grokking what it is actually doing. It is an interesting codebase, but it feels… incomplete. But I’ll reserve such thoughts to the summary post.

Reviewing FASTERReading data from disk

time to read 6 min | 1178 words

One of the things that FASTER claims is that it is suitable for larger than memory datasets with its hybrid log approach. I briefly went over that code during my review, but I want to dedicate this post to discussing how FASTER work with the disk.

FASTER is using async I/O on Linux & Windows to do its I/O, which present its own challenges, in particular, how do you handle an operation that is going to need to hit the disk ( to read old data). Another thing that I would like to discover is how does it save the data to disk. We’ll start from the reading data part.

Reading in FASTER looks like this:

image

You pass a context, a callback and serial number. I’m not sure what the serial is about, I think it is about checkpoints, but not sure. You’ll be called with the results once the operation executed.

Here is the core of the Read method:

image

FASTER first checks the in memory details, and if it isn’t there it is either not found or on disk. This is actually really interesting, because it implies that FASTER keep track of all of the data items purely in memory. Let’s go to InternalRead and figure out how that works. We already looked into most of that in the previous post FindEntry is called to find the entry by it’s hash. FASTER keep all the hashes in memory, even while it is writing entries to disk. This way, it can easily answer if an entry exists or not. Note that as I read things, if FASTER has more than a certain number of values, hash collision rate is going to reach high percentage, requiring often expensive I/O to figure out whatever the value exists.

If the value is in memory, your ReadContext.GetAtomic() method will be called. Here is one such implementation:

image

Where the value was defined as:

image

If the value has already been moved to the read only section, it will use the Get() method, instead, as an optimization. If the value is not on the mutable section or the read only section, it is on the disk, in which case we have this code:

image 

The go_async() method just records the status of the operation when we started the async process, it doesn’t actually invoke anything async. That is done in the caller code, Read().

image

This, in turn, gets us to this piece of code:

image

Note that the code is full of handling of the current phase of the thread. I’m ignoring all of these for now, they aren’t important.

The next thing to look at is the async I/O request, which gets us to:

image

We first register the pending I/O, then actually starts to process the async call. Except that not really. AsyncGetFromDisk() isn’t actually doing I/O.  Instead, it seems to be focused on limiting the number of concurrent I/O operations that are going on.

In this case, if there are more than 120 pending I/Os, it will call io_getevents() in Linux and GetQueuedCompletionStatus() and try to process any completed I/O immediately.

image

ProtectAndDrain is more interesting. It asks the epoch to complete any pending operations. Such actions are the creation of a new segment file or the moving of a segment from memory to disk.

I find it interesting that FASTER chose to stall the thread until all pending I/Os are completed. Given their model, it would make more sense to push such operation into the thread ops and resume process other requests. I guess that they expect this to be either a rare case or using this for throttling overall system load. You might also have noticed the num_records arguments, this is used in the hlog method:

image

That was confusing, I expected this to be the number of records (as in, how many records are read from disk) but this is the number of bytes to read.

The C++ memory model make async code a bit complex. In particular, if you’ll look at the first code snippet in this post, you’ll see that we pass a stack allocated struct to the Read() method. Because this method can complete in an async manner, what FASTER will do is to perform a deep copy of the data. Combine that with lambda’s capturing state, and I’m pretty sure that this code will cause a very subtle memory corruption in rare cases.

image

What I think will happen is that we capture by ref the stack variable and in 99% of the cases, we’ll run this in sync mode, meaning that there will be no issues. Only if the value needs to be read from disk will you get an issue. Because that function will already return but you still have the callback (and the captured reference now pointing to something completely different) still active. I think that a C++ developer would recognize this, and the fact that C++ require you to be explicit about your captures make this a lot easier to deal with.

It is important to note that there is no good way to actually handle the async nature of certain operations here. Any code that actually handle the operation need to go in the callback.

Another aspect to remember is that just reading from the disk doesn’t mean that you got the right value. You might have gotten a hash collision:

image

In other words, if you have a way to generate hash collisions, as soon as the value hits the disk, you are going to be facing with making N disk I/O requests to find if you have the value or not.  Denial of service attacks against hash tables are well known and represent a significant risk of to services.

Next on my list if seeing how FASTER is actually writing the data to disk, but that will be in a separate post.

Reviewing FASTERThe hash structure

time to read 6 min | 1086 words

Continuing my stroll through the FASTER codebase, I want to try to get to the core stuff, not just the periphery. This is a bit hard, because there is a lot of code here and it seems to be hard to find where the real action is happening. I decided to find how FASTER is handling Get and Put operations.   There is something called InternalHashTable inside FasterKv, which seems promising,  so I’m going to follow up on that. Interestingly enough, it shows up as:

image

So there are some funky stuff here to consider too, but we’ll deal with that later, for now, I want to understand what it is doing with the InternalHashTable. Inside that class, there is the notion of bukcets:

image

And a bucket is:

image

This starts to get interesting for me, so let’s dig deeper into HashBucketEntry, where they use the same union trick I talked about in my previous posts, this allow to easily define an atomic version of it with very little effort.

image

There is also the overflow entry definition, which looks like this:

image

I got to say, I’m really liking this approach for handling data packing as well as multi threading concerns. It also plays very well with the actual cache line architecture of modern CPUs.

There is also the KeyHash struct, whose heart is this:

image

So far, this lines us very neatly to how the FASTER paper talks about things. Given a KeyHash, here is how you get it’s bucket:

image

This simply index into the buckets_ array by taking the (size_ –1) bits from the hash itself. That tells me that the FASTER structure is very sensitive to the choice of hash function that will be used. This SO answer has some amazing detail on analysis of the output of hash functions with different outputs, which can give you some idea about why this matters so much. This post is an in depth discussion of this, as well of why the choice of hash function matters. This is enough for me to stop everything and try to find what kind of hash function is actually being used here.

The user get to define their own hash function, and if they do so badly (for example, use the identity function since they have an 8 bytes value and they need an 8 bytes hash) that is going to be fun. The function that they seem to be using in their examples is this one:

image

A bit of searching on the interweb didn’t narrow it down to something specific, it may be something that they wrote specifically for that. Given that the paper doesn’t mention this, it doesn’t seem to be something special.

Given that 40343 is a prime, it seems like a pretty common pattern of multiple by a prime with each 16 bits portion of the key. The idea is that the multiplication by prime will spread the bits around. No idea how high the quality of this hash function is, since actual analysis would take at least a few hours. At least at a glance,  it doesn’t seem to be awful, but I do wonder whatever something like FNV-1. In fact, this is very similar, but with different prime and offset and with addition instead of XOR.

The actual InternalHashTable class doesn’t actually do something, there are a few functions around checkpointing and recovery, but they aren’t very interesting at this point. I’m going back to actually working with the has table and looked at how reads work. They end up in the InternalRead method which does the majority of the work inside the FindEntry function. The first thing that happens there is:

image

The first line is there to handle growing the hash table on the fly and will be ignored for now. As you can see, this basically just gives me the relevant bucket. Here is the next stage, once we have a bucket, we need to find the relevant entry that matches what we are looking for. Here is the code:

image

You can see that there are a bunch of stuff going on here. First, we run over the known entries in the bucket, trying to find an entry with the same tag. You can see the tentative usage, which is used to sync up between concurrent readers and writers. There may be more items in the bucket than we have space for, so there is also the concept of overflow. This is basically a linked list of 7 items at a time and likely to be pretty fast (frequently already in the higher tiers of the cache for commonly accessed data).

Now that we have the entry, let’s see what is done with it. I’m currently reading through the InternalRead code. Just finding the matching hash doesn’t really help us, we may have hash collisions, this is handled here:

image

This is the first time that we actually run into the hybrid log (hlog here). But the basic idea is pretty obvious. Either the key match, or we have a pointer to the previous entry. I’m not seeing any handling of complete mismatch, though. I’m pretty sure that this is a bug (the behavior is different in the C# version).

This is enough for now, from here the InternalRead code is starting to do things with the hlog, state machine, etc. I’m going to handle that in a separate post.

Reviewing FASTERWorking with the file system

time to read 6 min | 1026 words

In my last post I dove into the Epoch implementation. The Epoch is explained very nicely in the paper, and the code follows the paper pretty closely. The code make sense, but I still lack the proper… feeling for how it is actually being used. The Epoch allows you to register code that will be executed when the epoch is updated, which is the key to how FASTER is making progress, but while I can see that this is being called from the allocators, I haven’t really grokked it yet. I’m going to back to the faster.h file and see what I can glean from there.

Because of the template utilization, it is kinda hard to figure out what exactly is going on, I’m going to look at some of the examples and try to figure out what it is doing there. Here is one instance of this class:

image

AdId and NumClicks are just two ways to provide operations on 8 bytes keys and values. I like these examples because they provide good use case to talk about FASTER usage.

This code leads me to the FileSystemDisk, which is defined as:

image

In the FileSystemFile, we have this code:

image

This is pretty simple, but I was quite amused by this, because this is C# API sneaking up again in the C++ code. There is also this:

image

I’m not sure what this bundle is, though. I run into this code in the class:

image

This is… not nice, in my eyes. Based on this code, whoever allocated this instance also allocated a buffer large enough to include more data there. This is fairly common, since you want to work with such data together, but I find it ugly / risky because it means that there are multiple locations that needs to be aware of it. I would like it better if they just passed the pointer explicitly. That would avoid this snippet:

image

Which I find pretty annoying to read. What is stranger is that to use this, you have to write (bundle_t has been typedef for the FileSystemSegmentBundle):

image

I get what is going on here, but I just find it really awkward to handle. There are multiple places where you need knowledge of this allocation pattern and I don’t believe that the benefit of placing all of the data together is that important. For that matter, given the importance of not using new explicitly in modern C++, I’m sure that there are other ways to achieve the same goal that would be more natural.

Going through the code, we now have:

image

I decided to make this post about the file system usage, because there is a lot of pretty complex code here that I would like to understand. I finally figured out what the S is, this is the segment size:

image

This means that the earlier definition of FasterKv basically defined Segment Size of 1 GB in size. I’m not sure what these segments are, though. I’m pretty sure that this is how they manage time base expiration, but I’m not certain. Following upward from the creation of a new segment, we have WriteAsync, like so:

image

You can see that the segment number is basically just the file id, and if the file does not already exists, we call OpenSegment on it. Afterward, we call WriteAsync on that specific file. I’ll look into how that work in a bit, this isn’t that interesting at the moment. Right now I want to dig into OpenSegment. I removed some error handling here, but the gist of it is clear.

image

The actual code also handles threading and errors, which I omitted. You can see that it creates the new files, copying them from the existing value. Then it creates a context that holds the old files and pass it to BumpCurrentEpoch.

When FASTER is sure that no one else is looking at this epoch, it will call the callback and delete / dispose the old files. This is a nice way to ensure consistency. LMDB does something similar with its transactions’ table. So now we know that whenever we write at a 1GB boundary, FASTER will generate a new epoch.

What about the actual writing? Here is what this looks like (the Linux impl):

image

On Linux, this ends up being:

image

This is then checked in TryComplete:

image

This is called FasterKv.CompletePending(), which seems to be called occasionally by FASTER. On Windows, this is using async I/O and callbacks to handle this.

Okay, this is already long enough, but I got a handle on how FASTER is writing to disk, even though I don’t know yet what it is doing with that. I also saw an actual use of Epoch that made sense (clearing old data once no one is looking at that).

Reviewing FASTERDigging into the C++ impl

time to read 6 min | 1147 words

After going over the paper and the managed implementation, I’m ready to start with the C++ implementation. I have higher hopes for this code. As I started browsing the C++ code, it occurred to me that the way the C#’s implementation handles dynamic code generation is pretty much how templates in C++ work. I wonder if this was the trigger for that.

The C++ code reads a lot more naturally to me. There are some nice tricks that are used there that are a joy to read. For example, take a look at Address manipulation:

image

The colon syntax are a way to express bitfields in C. But the real fun part for me is the idea of control_. What is this for? Well, it runs out that in addition to Address, the also defined AtomicAddress, whose implementation need to provide atomic operation on address. This is implemented in the following manner:

image

I find this a really elegant way to handle this requirement.

Another amusing observation is that almost all the code in FASTER is in .h files, because of the heavy use of templates. I wonder how that affects compilation speed and how that would play in larger projects.

It is in faster.h that we start to get into the interesting bits. I first run into this guy:

image

This maps pretty closely to what I have actually seen the C# code does, but in C++ it is a much more natural approach that dynamic compilation on the fly as it did in C#.

Next we have the constructor, which looks like this:

image

The epoch_ field is auto initialized by the compiler and is not shown here. This indicates that FASTER can handle up to 2.1 billion entries in total, which seems to be a strange limit for a data store that is expected to handle hundreds of thousands of operations per second. I’m going to jump around the codebase a bit, because I want to follow exactly what is going on when initializing this class. The first place to look is the epoch. The idea of epoch is described in the paper, so I’m not going to repeat it. The code defines a struct that is 64 bytes in size (cache line sized, to avoid false sharing), this is used to store a thread specific value and is used to maintain most of the invariants of the epoch.

image

When switching between epochs, there are actions that needs to be run, here is what this looks like in the code.

image

I must say, this really mess up with my mind, because we have C#’s naming conventions (TryPop, TryPush) in C++ code. It’s like the code couldn’t decide what shape it wanted to be in either language.

The number of threads that can take part is limited by this value:

image

Right now, this is set to 96, which means that if you need more threads than that, you’ll get a runtime error. This fits nicely with the model FASTER uses of long running threads, but I don’t see how it can play well with actually accepting commands from network / other location.

As part of it’s constructor, this method is called, which actually does the real work of setting up the epoch.

image

I’m not really sure at this point why it is allocating two additional entries beyond the specified size.

When a thread start running FATER code, it needs to register itself with the Epoch, this is done in the Protect() call.

image

Going into the Thread class reveals a simple table of values that are used to give ids to the threads that asked to get an id. This is done in this function:

image

It took me a couple of times of reading the first two lines to understand what is going on here. This is an awesome way to handle a circular buffer scanning. It is very clear and saves a bunch of code ( at the cost of doing mod operation, which can be usually be masked if the value is known at compile time and is a power of 2, which in this case it is not). I’m probably going to use this the next time I need to implement scanning through a ring buffer.

Then we have computing the earliest safe epoch:

image

The first of these methods is elegant, it does a simple read from the table, reading potentially stale values. This doesn’t matter, because the worst thing that can happen is that we’ll keep a previous epoch for longer than it is required.

The second one reads wrong to me, but I’ll have to dig deeper into the C++ memory model more deeply for this. The problem is that this seems like it is relying on the CPU to update its state somehow. But I don’t see any instruction that would force it to. I think that the set to safe_to_reclaim_epoch (which is std::atomic<uint64_t>) will use memory_order_seq_cst for the operation, but I’m not sure how that would impact reads from the table_.

Also, I want you to pay attention to the variable names here. Private member fields:

image

Public member fields:

image

And then we have SpinWaitForSafeToReclaim that uses:

  • safe_to_reclaim_epoch – public member field
  • safe_to_reclaim_epoch_ – method argument

I’m not sure if this a common practice in C++, but this is really confusing to me. This is enough for now, I’m going to keep going thought the C++ code in my next post. There hasn’t been anything really interesting so far, just digging into the code and getting a feel as to how it is put together.

Reviewing FASTERLet’s check these numbers

time to read 3 min | 595 words

Before heading to the C++ implementation, I thought I would take the time to just walk through the FASTER codebase as it is performing a simple operation. The access violation error that I previously run into has been fixed, so I could run the benchmark. Here is my configuration:

image

I got the following results, when running on a single thread.

Total 142,603,719 ops done  in 30 secs.

This is about 4.7 million operations per second, which is certainly nice. I then decided to compare this to ConcurrentDictionary to see what kind of performance that would give me. I made the following changes, which I’ll admit are pretty brute force way to go about it. But note that this is probably significantly less efficient then I could probably write it. Nevertheless, using ConcurrentDictionary with a single thread in the same benchmark gives me:

Total 84,729,062 ops done  in 30 secs.

There isn’t much call for using this on a single thread, though. My machine has 20 cores, so let’s see what happens when I give FASTER its head, shall we?

2,330,054,219 ops done  in 30.021 secs.

That is impressive, with about 77,668,473 operations per second. On the other hand, this is what happened when I run with 20 threads and ConcurrentDictionary:

671,071,685 ops done  in 30.024 secs.

This gives us “only” 22,369,056 operations per second.

It is clear that FASTER is much better, right? The problem is that it isn’t much faster enough. What do I mean by this? I used idiomatic C# for the ConcurrentDictionary usage and got with 1/4 of FASTER’s perf. The FATER codebase is doing native calls and unsafe manipulation, dedicated allocation, etc. I expect to get better perf at that point, but “merely” 400% improvement isn’t enough for the kind of effort that was put into this. I run the concurrent dictionary in a sampling profiler, with 20 threads, and I got the following results.

image

On the other hand, using FASTER for the same scenario gives:

image

This is really interesting. You can see that the FASTER option spends all its time in either: InternalUpsert or inside the RunYcsb method (which is actually the store.Read() method that was inlined).

What is more interesting is that there are no additional calls there. The InternalUpsert call is 219 lines of code, and the code uses [MethodImpl(MethodImplOptions.AggressiveInlining)] quite aggressively (pun intended). On the other hand, the ConcurrentDictionary implementation has to make virtual method calls on each call.

There are several ways to handle this, including using generic struct that can eliminate most of the virtual calls. This is effectively what FASTER is doing, without the generics. FASTER also benefits from pre-allocating everything upfront. If you’ll look at the profiler results, you can see that these are the major source of “slowness” in the benchmark.

Given the nature of the benchmark, I feel that it is unfair to compare FASTER to persistent data stores and it should be compared more closely to a concurrent hash map. Given that this is effectively what FASTER is doing in this showcase benchmark, that seems a lot more fair. I checked the literature and we have this paper talking about concurrent hash maps where we see (Figure 2.a) numbers that near to 300 millions ops/sec for pure writes and 600 millions ops/sec for reads.

Reviewing FASTERLet’s start with managed code

time to read 7 min | 1295 words

Last post commented on the FASTER paper, now I’m getting to reading the code. This is a pleasant change of scenery for me, since FASTER is written in C# (there is a C++ port that I’ll also be going through later). As someone who spend most of their time in C#, that make it much easier to go through the code. On the other hand, this was clearly written by someone who spent most of their time in C++. This means that naming conventions and the general approach to writing the code sometimes directly contradict C# conventions. Some of that really bugs me.

The first thing I did was to try to run the benchmark on my own machine, to get relative numbers. It died with an AccessViolationException, which was a disappointment. I’m going to ignore that and just read through the code. One thing that I did noticed when reading through the benchmark code is that this piece:

image

This maps closely to some of the things they mentioned in the paper, where each thread refresh every 256 ops and complete pending operations every 65536 ops. The reason I call this out is that having this done in what effectively is a client code is a bad idea in term of design. The benchmark code is operating under continuous stream of operations and can afford to amortize such things. However, real code need to deal with client code that isn’t well behaving and you can’t assume that you’ll be operating in this kind of environment.

Based on the semantics discussed in the paper and what the code is showing me, I would assume that the general model for actually using this is to spawn off a bunch of threads and then listen to some data source. For example, a socket. On each network read, the thread would apply a batch of operations. Note that this means that you have to deal with threads that may be arbitrarily delayed. I would expect that under such a scenario, you’ll see a very different performance profile. You won’t have all threads working in tandem and staying close to one another.

When getting to the code itself, I started with the native portions, trying to figure out what FASTER is doing with the lowest level of the system. It is mostly advanced file operations (things like telling the OS to don’t defrag files, allow to allocate disk space without zeroing it first, etc). As far as I can see, this isn’t actually being called from the current code, but I assume that they at least tested out this approach. Another native code they have is related to calling __rdtsc(), which they use in the HiResTimer class.

This can be replaced completely by the .NET Stopwatch class and I believe that dropping the adv-file-ops and readtsc native projects is possible and straightforward, allowing for a fully managed FASTER impl. Note that it is still using a lot of interop calls, it seems, but at least so far, I think that most of them are not necessary. To be fair, given the way the code is structured, a lot of the native code is about I/O with pointers, which until the Great Spanification in .NET Core was PITA to deal with.

In general, by the way, the code reads more as a proof of concept than a production codebase. I notice this in particular with the general approach for errors handling. Here are two examples:

image

This is from the native code, from which it is a PITA to return errors. However, writing to the console from a library is not an error reporting strategy. What really bugged me was this code, from the MallocFixedPageSize code:

image

If there is any error in the process of pinning memory, just ignore it? Leave the memory pinned?

Here is another example that made me cringe:

image

Moving on to the CodeGen directory, it gets interesting. The paper talks about dynamic code generation, but I didn’t expect to see such wide usage of this. In particular, large sections of the code (13 files, to be exact, over 6000 lines of code) are dynamically loaded, transformed and compiled on the fly when you create the hashtable.

I understand the reasoning for this, you want to get the JIT to compile the best possible code that it can for the specific operations you execute. However, this make it pretty hard to follow what is going on there. In particular, code generating code make it harder to follow what end up actually going on. There are also better ways to do it. Either through generic struct parameters to specialize the code or only generating the dedicated connecting methods as needed and not recompiling large portions on the fly.

The Device directory isn’t really interesting. Mostly pretty trivial I/O operations, so I’m not going to discuss it in depth.

Next, getting to the Epoch, which was really interesting to read in the paper. The actual implementation raise a few questions. The Epoch value in an 32 bits integer, that means that it will wrap fairly quickly. It looks like the Epoch is bumped every time you need a new page. Given the operations rate that are reported, I would expect it to happen on a regular basis (this is also required for the system to progress properly and sync up). My reading is that wrapping of the Epoch counter will result in Bad Things Going On.

There there is this:

image

Size, in this case, is always set to 128. The size of Entry is 64 bytes, this means that this call will allocate 8,320 bytes in Gen0, immediately pin it and never let it go. This is going to result in memory fragmentation. It would be better to allocate this on the Large Object Heap and avoid the issue. In fact, the same issue can be seen in the memory allocation, where the code does:

image

In This case, PageSize is 65,536, however, and given any value except a byte, this will automatically go to the Large Object Heap anyway. I’m going to be a bit ungenerous here, but I’m not sure if this was intentional or not.

I’m firmly convinced that this is pure POC code, here is the snippet that finally did it for me, from the BumpCurrentEpoch() code.

image

Note that I don’t mind the Thread.Sleep() here, it make sense in context, but the Console.WriteLine cinched the deal for me. This is not something that you can take a use, but something to look at as you implement this properly.

I have to say that I find it very hard to go through the code, mostly because it’s in C# and there are certain expectations that I have from the code which are routinely violated. I think that I’m going to stop reading the C# codebase and switch over to the C++ implementation. I expect this to be more idiomatic, both because it is the second time this was written and because the people writing the code are clearly more using to writing in C++.

Reviewing FASTERReading the paper

time to read 5 min | 822 words

imageThe FASTER is a fast key-value store from Microsoft. It’s paper was published a while ago and now that the source is available, I thought that I would take a peek. You might say that I have a professional interest in such a project Smile.

I decided to read the paper first before reading the code, because I want to figure out what isn’t in the paper. The implementation details beyond the abstract are what I find most interesting, to be honest. But if I’m going to read the paper, I’m also going to add some comments on it. Please note that the comments are probably not going to make much sense unless you read the paper.

The Epoch Basic section explains how they use a global epoch counter with a thread local value and a shared table that marks what epochs are visible to each thread. They use this to provide synchronization points, I assume (don’t know yet). This resonates very strongly with how LMDB’s global transaction table operates.

I like the concept of the drain list which is executed whenever an epoch become safe. I would assume that they use that to let the clients know that their operation was accepted and what was its state.

I wasn’t able to figure out what they use the tag for in the hash bucket entry. I think that the way it works is that you have K hash buckets and then use the first K bits to find the appropriate bucket, then scan for a match on the last 15 bits. I’m not really sure how that work with collisions, though. I assume that this will be clearer when I get to the code. I like the two phase scan approach to ensure that you have no conflicts when updating an entry.

The paper keeps repeating the speed numbers of 160 millions ops/sec and talking about 20 millions ops / sec as being “merely”. Just based on my reading so far, I don’t see how this can happen. What is interesting to me is what is the definition of ops. Is it something like incrementing the same (small) set of counters? If that is the case, than the perf numbers both make more sense and are of less interest. Typically when talking about ops / sec in such scenarios we talk about inserts / updates to individual documents / rows / objects. Again, I don’t know yet, but that is my thinking so far.

One thing that I find sad is that this isn’t a durable store. A failure in the middle of operations would cause some data to be completely lost. It seems like they have support for checkpoints, so you don’t lose everything. However, I’m not sure how often that happens and the benchmarks they are talking about were run without it. Interestingly enough, the benchmarks were run without garbage collection. I haven’t gotten to the discussion on that yet, so I’m not exactly what that means Another missing feature here is that there is no support for atomicity. You cannot ensure that two separate operations will run as a single atomic step.

The benchmark machine is 28 cores with 256GB RAM and 3.2 TB NVMe drive. This is a really nice machine, but from the get go I can tell that this is not going to be a fair comparison to  most other storage engines. Faster is explicitly designed to work mostly in memory and with high degree of parallelism. This is great, but it gives us some important features (atomic batches and durability, also known as transactions). The data size they tested are:

  • 250 million records with 8 bytes keys & 8 bytes values – Just under 4GB in total.
  • 250 million records with 8 bytes keys & 100 bytes values – Just under 32GB in total.

I’m not sure why they think that this is going to provide larger than memory setup. In particular, they mention a memory budget of 2GB, but I assume that this is just internal configuration. There is also going to be quite a lot of memory cached in the OS’ page cache, for example, and I don’t see anything controlling that. Maybe I’ll when I’ll go through the code, though.

Okay, the garbage collection they refer to is related to how they compact the log. They use an interesting approach where they just discard it at a some point, and any pointer to the removed section is considered to be deleted automatically. That is likely to be very efficient, assuming that you don’t care about older data.

All in all, I feel that I have a vague understanding on how Faster works and a lot better grasp on what it does and how it is utilized. I’m looking forward to diving into the code.

Reading the NSA’s codebase: LemonGraph review–Part VII–Summary

time to read 2 min | 344 words

As the final post in this series, I decided to see how I can create a complex query. Given the NSA’s statement, I decided to see if I can use LemonGraph to find a dog of interest. In particular, given our graph, I wanted to find start with a particular dog and find another dog that likes this dog that also like a dog that dislike the original.

As a reminder, here is the graph:

image

And starting from Arava, I want to find a dog that likes Arava that also likes a dog that isn’t liked by Arava.

The LemonGraph query language isn’t very expressive, or at least I’m not familiar enough with it to make it work properly. I decided on the following query:

n(type="dog", value="arava")->
               @e(type="likes", value="yes")->
n(type="dog")->
               @e(type="likes", value="yes")->
n(type="dog")->
               @e(type="likes", value="no")->
@N(type="dog", value="arava")

This is a bit of a brute force method to do this. It encodes the path directly. There are a few minor things that might not be obvious here. The @ prefix means don’t return this to the user and the N() indicates that we shouldn’t filter already seen values. I can certainly see how this can be useful for certain things Smile. I wonder if LemonGraph has a better way to express such a query.

This is the first time I actually reviewed this kind of codebase, where some things are done in C and some in Python. It was quite interesting to see the interaction between them. The codebase itself is really interesting, but I found it really hard to follow at times. The love affair with tuples and dynamic behavior made the code non trivial and likely is going to cause maintenance issues down the line. It is also quite obvious that this is intended for internal consumption, with very little time or effort spent on “productization”. By that I meant things like better query errors and more obvious thing to do.

It has an interesting approach to solving the problem of graph queries and I’ve learned quite a few things from it.

FUTURE POSTS

No future posts left, oh my!

RECENT SERIES

  1. Challenge (54):
    28 Sep 2018 - The loop that leaks–Answer
  2. Graphs in RavenDB (4):
    21 Sep 2018 - Graph modeling vs. document modeling
  3. Reviewing FASTER (9):
    06 Sep 2018 - Summary
  4. RavenDB 4.1 features (12):
    22 Aug 2018 - MongoDB & CosmosDB Migration Wizards
  5. Reading the NSA’s codebase (7):
    13 Aug 2018 - LemonGraph review–Part VII–Summary
View all series

Syndication

Main feed Feed Stats
Comments feed   Comments Feed Stats