Remember our examples/hello.rs program? This is less accurate from a progress tracking point of view, but more performant. Timely dataflow is in a bit of a weird space between language library and runtime system. (note: the simple.rs program always uses one worker thread; it uses timely::example which ignores user-supplied input). For a more involved example, consider the very similar (but more explicit) examples/hello.rs, which creates and drives the dataflow separately: This example does a fair bit more, to show off more of what timely can do for you. 实时数据流是一种低延迟循环数据流计算模型，在 Naiad中引入： 一个及时的数据流系统。 项目是 Rust 中实时数据流的扩展和模块化实现。 这个项目类似于分布式数据并行计算引擎，它从笔记本电脑上的单线程扩展到分布式执行。 By contrast, the logging infrastructure demotes nanoseconds to data, part of the logged payload, and approximates batches of events with the smallest timestamp in the batch. There is an open issue on integrating Rust ownership idioms into timely dataflow. But, the task of sorting is traditionally viewed as transforming the data in a supplied slice, rather than sending the data to multiple workers and then announcing that it got sorted. Let's check out the time to print out the prime numbers up to 10,000 using one worker: The time is basically halved, from one minute to thirty seconds, which is a great result for those of us who like factoring small numbers. Let's do that now, to get a sense for how much of a difference it makes: That is about a 60x speed-up. Timely dataflow is intended to support multiple levels of abstraction, from the lowest level manual dataflow assembly, to higher level "declarative" abstractions. Its implementation is fully incrementalized, and the details are pretty cool (if mysterious). For example: Timely currently does more copies of data than it must, in the interest of appeasing Rust's ownership discipline most directly. The goal is to get a sense for dataflow with all of its warts, and to get you excited for the next section where we bring back the timestamps. The lack of system support means that the user ends up indicating the granularity, which isn't horrible but could plausibly be improved. Let's do that now, to get a sense for how much of a difference it makes: The hello.rs program above will by default use a single worker thread. There is also a series of blog posts (part 1, part 2, part 3) introducing timely dataflow in a different way, though be warned that the examples there may need tweaks to build against the current code. Right now, timely streams are of cloneable objects, and when a stream is re-used, items will be cloned.
The main goals are expressive power and high performance. There are a few broad categories, and then an ever-shifting pile of issues of various complexity.
The inspect operator takes an action for each datum, in this case printing something to the screen. :). You will need to use the -n or --processes argument to indicate how many processes you will spawn (a prefix of the host file), and each process must use the -p or --process argument to indicate their index out of this number. It also includes more exotic operators for tasks like entering and exiting loops (enter and leave), as well as generic operators whose implementations can be supplied using closures (unary and binary). As many operators produce data-parallel output (based on independent keys), it may not be that much of a burden to construct such iterators. D-INFK. There is an important part of our program up above that is imperative: This is an imperative fragment telling the inspect operator what to do. ETH Zurich 10 SELECT (P.name, P.city, P.state, A.id) FROM Auction A, Person P I haven't been using it up above to keep things simple, but adding the --release flag to cargo's arguments makes the compilation take a little longer, but the resulting program run a lot faster.
Importantly, we haven't imposed any constraints on how these operators need to run. At the very least, the first step would be "fundamentally re-imagine your program", which can be a fine thing to do, but is perhaps not something you would have to do with your traditional program. However, this is only a mess if we are concerned about the order, and in many cases we are not. There is a bunch of interesting work in scheduling timely dataflow operators, where when given the chance to schedule many operators, we might think for a moment and realize that several of them have no work to do and can be skipped. Learning about timely dataflow, trying to use it, and reporting back is helpful! There are a few classes of work that are helpful for us, and may be interesting for you. It may also be that leaving the user with control of the granularity leaves them with more control over the latency/throughput trade-off, which could be a good thing for the system to do.
If either of these problems were fixed, it would make sense to recycle the buffers to avoid random allocations, especially for small batches. This means that it doesn't quite have the stability guarantees a library might have (when you call data.sort() you don't think about "what if it fails? If you are interested in working with or helping out with timely dataflow, great!
There are several reasons not to use timely dataflow, though many of them are friction about how your problem is probably expressed, rather than fundamental technical limitations. There are two dataflow operators here, exchange and inspect, each of which is asked to do a thing in response to input data. However, many programs are correct only because some things happen before or after other things. GitHub Gist: star and fork benesch's gists by creating an account on GitHub. The data really does need to end up in one place, one single pre-existing memory allocation, and timely dataflow is not great at problems that cannot be recast as the movement of data. It may be possible to generalize this so that users can write programs without thinking about granularity of timestamp, and the system automatically coarsens when possible (essentially boxcar-ing times). "), nor does it have the surrounding infrastructure of a DryadLINQ or Spark style of experience. We then drive the computation by repeatedly introducing rounds of data, where the round itself is used as the data. Differential dataflow: A higher-level language built on timely dataflow, differential dataflow includes operators like group, join, and iterate. If you would like to write programs using timely dataflow, this is very interesting for us. These can often be easy to pick up, flesh out, and push without a large up-front obligation. Dataflow programming is fundamentally about describing your program as independent components, each of which operate in response to the availability of input data, as well as describing the connections between these components. We inspect the data and print the worker index to indicate which worker received which data, and then probe the result so that each worker can see when all of a given round of data has been processed. A library like Rayon would almost surely be better suited to the task. The timely communication layer currently discards most buffers it moves through exchange channels, because it doesn't have a sane way of rate controlling the output, nor a sane way to determine how many buffers should be cached. Said differently, you want a hostfile that looks like so. Ideally timely dataflow is meant to be an ergonomic approach to a non-trivial class of dataflow computations. Here is a reduced version of examples/hello.rs that just feeds data in to our dataflow, without paying any attention to progress made. Furthermore, although the 1,262 lines of results of output1.txt and output2.txt are not in the same order, it takes a fraction of a second to make them so, and verify that they are identical: This is probably as good a time as any to tell you about Rust's --release flag. I haven't been using it up above to keep things simple, but adding the --release flag to cargo's arguments makes the compilation take a little longer, but the resulting program run a lot faster. This is a very simple example (it's in the name), which only just suggests at how you might write dataflow programs. It has the added benefit that the logs are timely streams themselves, so you can even do some log processing on timely. It seems like the Stream type should be extendable to be parametric in the type of storage used for the data, so that we can express the fact that some types are not serializable and that this is ok.
If you like the idea of getting your hands dirty in timely dataflow, the issue tracker has a variety of issues that touch on different levels of the stack. I've collected a few examples here, but the list may grow with input and feedback. Dataflow systems are also fundamentally about breaking apart the execution of your program into independently operating parts.
This means that using very fine-grained timestamps, for example the nanosecond at which a record is processed, can swamp the progress tracking logic. Timely Dataflow. This gives the system the chance to play the iterator at the speed they feel is appropriate. If you are interested in teasing out how timely works in part by poking around at the infrastructure that records what it does, this could be a good fit! In each round, each worker introduces the same data, and then repeatedly takes dataflow steps until the probe reveals that all workers have processed all work for that epoch, at which point the computation proceeds. Although there is plenty of active research on transforming algorithms from sequential to parallel, if you aren't clear on how to express your program as a dataflow program then timely dataflow may not be a great fit. It is probably strictly more expressive and faster than whatever you are currently using, assuming you aren't yet using timely dataflow. This is probably as good a time as any to tell you about Rust's --release flag. There is more long-form text in mdbook format with examples tested against the current builds.
Not everything is obvious here, so there is the chance for a bit of design work too. // initializes and runs a timely dataflow.
This is also a fine time to point out that dataflow programming is not religion.
We could make that more explicit, and require calling a .cloned() method to get owned objects in the same way that iterators require it. This has several advantages, mostly in how it allows a computer to execute your program, but it can take a bit of thinking to re-imagine your imperative computation as a dataflow computation.
and then to launch the processes like so: The number of workers should be the same for each process. Let's write an overly simple dataflow program. There are fundamental technical limitations too, of course. Better, we might maintain the list of operators with anything to do, and do nothing for those without work to do. We could write this as a dataflow fragment if we wanted, but it is frustrating to do so, and less efficient.
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