Thanks Kenn, based upon the error message there was a small amount of code that I missed when updating the code. I'll attempt to fix this in the next few days.
On Mon, Jan 14, 2019 at 7:26 PM Kenneth Knowles <k...@apache.org> wrote: > I wanted to use this thread to ping that the change to the user-facing API > in order to wrap RestrictionTracker broke the Watch transform, which has > been sickbayed for a long time. It would be helpful for experts to weigh in > on https://issues.apache.org/jira/browse/BEAM-6352 about how the > functionality used here should be implemented. > > Kenn > > On Wed, Dec 5, 2018 at 4:45 PM Lukasz Cwik <lc...@google.com> wrote: > >> Based upon the current Java SDK API, I was able to implement Runner >> initiated checkpointing that the Java SDK honors within PR >> https://github.com/apache/beam/pull/7200. >> >> This is an exciting first step to a splitting implementation, feel free >> to take a look and comment. I have added two basic tests, execute SDF >> without splitting and execute SDF with a runner initiated checkpoint. >> >> On Fri, Nov 30, 2018 at 4:52 PM Robert Bradshaw <rober...@google.com> >> wrote: >> >>> On Fri, Nov 30, 2018 at 10:14 PM Lukasz Cwik <lc...@google.com> wrote: >>> > >>> > On Fri, Nov 30, 2018 at 1:02 PM Robert Bradshaw <rober...@google.com> >>> wrote: >>> >> >>> >> On Fri, Nov 30, 2018 at 6:38 PM Lukasz Cwik <lc...@google.com> wrote: >>> >> > >>> >> > Sorry, for some reason I thought I had answered these. >>> >> >>> >> No problem, thanks for you patience :). >>> >> >>> >> > On Fri, Nov 30, 2018 at 2:20 AM Robert Bradshaw < >>> rober...@google.com> wrote: >>> >> >> >>> >> >> I still have outstanding questions (above) about >>> >> >> >>> >> >> 1) Why we need arbitrary precision for backlog, instead of just >>> using >>> >> >> a (much simpler) double. >>> >> > >>> >> > >>> >> > Double lacks the precision for reporting backlogs for byte key >>> ranges (HBase, Bigtable, ...). Scanning a key range such as ["a", "b") and >>> with a large number of keys with a really long common prefix such as >>> "aaaaaaaaaaaaaaaaaaaaaaaaaab" and "aaaaaaaaaaaaaaaaaaaaaaaaaac", ... leads >>> to the backlog not changing even though we are making progress through the >>> key space. This also prevents splitting within such an area since the >>> double can't provide that necessary precision (without multiple rounds of >>> splitting which adds complexity). >>> >> >>> >> We'll have to support multiple rounds of splitting regardless. I can >>> >> see how this gives more information up front though. >>> > >>> > I agree that we will need to support multiple rounds of splitting from >>> the SDK side but this adds complexity from the runner side since it can >>> only increase the accuracy for a split by performing multiple rounds of >>> splitting at once. >>> > >>> >> (As an aside, I've been thinking about some ways of solving the dark >>> >> matter problem, and it might depend on knowing the actual key, using >>> >> the fact that character boundaries are likely cut-off points for >>> >> changes in density, which would get obscured by alternative >>> >> representations.) >>> > >>> > Every time I think about this issue, I can never get it to apply >>> meaningfully for unbounded sources such as a message queue like pubsub. >>> >>> Yeah, neither can I. >>> >>> > Also, having an infinitely precise backlog such as the decimal format >>> would still provide density information as the rate of change through the >>> backlog for a bounded source would change once a "cluster" was hit. >>> >>> This is getting to somewhat of a tangential topic, but the key insight >>> is that although it's easy to find the start of a cluster, to split >>> ideally one would want to know where the end of the cluster is. For >>> keyspaces, this is likely to be at binary fractions, and in particular >>> looking at the longevity of common prefixes of length n one could make >>> heuristic guesses as to where this density dropoff may be. (This also >>> requires splitting at a key, not splitting relative to a current >>> position, which has its issues...) >>> >>> >> >> 2) Whether its's worth passing backlog back to split requests, >>> rather >>> >> >> than (again) a double representing "portion of current remaining" >>> >> >> which may change over time. (The most common split request is into >>> >> >> even portions, and specifically half, which can't accurately be >>> >> >> requested from a stale backlog.) >>> >> > >>> >> > I see two scenarios here: >>> >> > * the fraction is exposed to the SDF author and then the SDF author >>> needs to map from their restriciton space to backlog and also map fractions >>> onto their restriction space meaning that they are required to write >>> mappings between three different models. >>> >> > * the fraction is not exposed to the SDF author and the framework >>> code multiplies the fraction against the backlog and provides the backlog >>> to the user (this solves the backlog skew issue but still has the limited >>> precision issue). >>> >> >>> >> Limited precision is not as much of an issue here because one can >>> >> express very small numbers to split close to the current position, and >>> >> don't need high precision for splitting further away. >>> > >>> > Agree. Would this also mean that skew when splitting at half doesn't >>> really matter? >>> >>> Lots of times keyspaces have big pockets of low density. If one hits >>> one of these ranges between when the backlog is reported and when the >>> split is requested, the skew can get quite large. Basically using a >>> fraction means that a system does not have to be as concerned about >>> stale data, and can make reasonable choices without data at all (e.g. >>> imagine upscaling from 200 to 300 workers and asking for everyone to >>> just give 33% of their work back), and when it does make choices based >>> on actual backlog the discrepancy between what was ideal at the time >>> backlog was requested and what's ideal now is shared between the >>> primary and remainder(s) rather than one side or the other absorbing >>> this entire error >>> >>> This of course gets exacerbated with multiple splits, e.g. if the >>> measured backlog was 100 and you wanted to split the work in 10 >>> pieces, asking for a split at 10 would only result in 9 splits if the >>> cursor advanced by 10 in the meantime, and if it advanced by 9 you'd >>> probably want to use fractions anyway to spread the error out and >>> produce (10, 9, 9, 9, 9, 9, 9, 9, 9, 9) rather than (10, 10, 10, 10, >>> 10, 10, 10, 10, 10, 1). >>> >>> >> I also think it's nice that the space of possible splits is always >>> >> (current position, restriction end) which a always double maps onto >>> >> despite those both being moving targets. If you phrase things in terms >>> >> of backlogs, you might ask for impossible things. I don't recall if >>> >> the passed backlog is the amount that should be retained or the amount >>> >> that should be returned, but if the latter, it'll be difficult to >>> >> accurately split near the current position. >>> > >>> > >>> > For the current proposal, it represents how much should be retained >>> but as was mentioned earlier, the semantics of returning multiple splits is >>> still up in the air. >>> > >>> >> >>> >> > I believe it is easier for an SDF author to write a two way mapping >>> from backlog to their position space then to write two different types of >>> mappings. For example, when a person is reading a file that has 100 bytes >>> to process and is asked to split at 60.3%, they have to map 60.3% onto 100 >>> bytes figuring out that they are responsible for 60.3 bytes in which they >>> round down to 60 bytes. In the scenario where the runner provides the >>> backlog, 60.3 would have been sent across and the SDF author would only >>> need to perform rounding. >>> >> >>> >> Yeah, that's something to mull on. Maybe with a set of concrete >>> examples. >>> >> >>> >> >> There are also some questions about returning multiple remainders, >>> and >>> >> >> how that relates to/overlaps with the initial splitting, but those >>> can >>> >> >> probably be deferred. >>> >> > >>> >> > >>> >> > Agree. >>> >> > >>> >> >> >>> >> >> On Wed, Nov 28, 2018 at 2:23 AM Lukasz Cwik <lc...@google.com> >>> wrote: >>> >> >> > >>> >> >> > I updated the PR addressing the last of Scott's comments and >>> also migrated to use an integral fraction as Robert had recommended by >>> using approach A for the proto representation and BigDecimal within the >>> Java SDK: >>> >> >> > A: >>> >> >> > // Represents a non-negative decimal number: unscaled_value * >>> 10^(-scale) >>> >> >> > message Decimal { >>> >> >> > // Represents the unscaled value as a big endian unlimited >>> precision non-negative integer. >>> >> >> > bytes unscaled_value = 1; >>> >> >> > // Represents the scale >>> >> >> > uint32 scale = 2; >>> >> >> > } >>> >> >> > >>> >> >> > Ismael, I would like to defer the changes to improve the >>> ByteBuddy DoFnInvoker since that is parallelizable work and have filed >>> BEAM-6142. >>> >> >> > >>> >> >> > I don't believe there are any other outstanding changes and >>> would like to get the PR merged so that people can start working on >>> implementing support for backlog reporting and splitting within the Java >>> SDK harness, improving the ByteBuddy DoFnInvoker, exposing the shared >>> runner library parts, and integrating this into ULR, Flink, Dataflow, ... >>> >> >> > >>> >> >> > On Mon, Nov 26, 2018 at 9:49 AM Lukasz Cwik <lc...@google.com> >>> wrote: >>> >> >> >> >>> >> >> >> >>> >> >> >> >>> >> >> >> On Mon, Nov 26, 2018 at 9:09 AM Ismaël Mejía <ieme...@gmail.com> >>> wrote: >>> >> >> >>> >>> >> >> >>> > Bundle finalization is unrelated to backlogs but is needed >>> since there is a class of data stores which need acknowledgement that says >>> I have successfully received your data and am now responsible for it such >>> as acking a message from a message queue. >>> >> >> >>> >>> >> >> >>> Currently ack is done by IOs as part of checkpointing. How >>> this will >>> >> >> >>> be different? Can you please clarify how should be done in >>> this case, >>> >> >> >>> or is this totally independent? >>> >> >> >> >>> >> >> >> >>> >> >> >> The flow for finalization and checkpointing is similar: >>> >> >> >> Checkpointing: >>> >> >> >> 1) Process a bundle >>> >> >> >> 2) Checkpoint bundle containing acks that need to be done >>> >> >> >> 3) When checkpoint resumes, acknowledge messages >>> >> >> >> >>> >> >> >> Finalization: >>> >> >> >> 1) Process a bundle >>> >> >> >> 2) Request bundle finalization when bundle completes >>> >> >> >> 3) SDK is asked to finalize bundle >>> >> >> >> >>> >> >> >> The difference between the two is that bundle finalization >>> always goes back to the same machine instance that processed the bundle >>> while checkpointing can be scheduled on another machine. Many message queue >>> like systems expose clients which store in memory state and can't ack from >>> another machine. You could solve the problem with checkpointing but would >>> require each machine to be able to tell another machine that it got a >>> checkpoint with acks that it is responsible for but this won't work >>> everywhere and isn't as clean. >>> >> >> >> >>> >> >> >>> >>> >> >> >>> > UnboundedPerElement/BoundedPerElement tells us during >>> pipeline construction time what type of PCollection we will be creating >>> since we may have a bounded PCollection goto an UnboundedPerElement DoFn >>> and that will produce an unbounded PCollection and similarly we could have >>> an unbounded PCollection goto a BoundedPerElement DoFn and that will >>> produce an unbounded PCollection. Restrictions.IsBounded is used during >>> pipeline execution to inform the runner whether a restriction being >>> returned is bounded or not since unbounded restrictions can return bounded >>> restrictions during splitting. So in the above example using the message >>> queue, the first 7 restrictions that only read 1250 messages would be >>> marked with the Restrictions.IsBounded interface while the last one would >>> not be. This could also be a method on restrictions such as "IsBounded >>> isBounded()" on Pcollections. >>> >> >> >>> >>> >> >> >>> Thanks for the explanation about Restrictions.IsBounded, since >>> this is >>> >> >> >>> information for the runner What is the runner expected to do >>> >> >> >>> differently when IsUnbounded? (I assume that IsBounded is the >>> default >>> >> >> >>> behavior and nothing changes). >>> >> >> >> >>> >> >> >> >>> >> >> >> Knowing whether a restriction is bounded or unbounded is >>> important, one example use case would be for the limited depth splitting >>> proposal ( >>> https://docs.google.com/document/d/1cKOB9ToasfYs1kLWQgffzvIbJx2Smy4svlodPRhFrk4/edit#heading=h.wkwslng744mv) >>> since you want to keep the unbounded restrictions at level 0 and only pass >>> the bounded restrictions to the other levels. The reasoning behind this is >>> that you don't want to end up in a state where all your unbounded >>> restrictions are at the highest level preventing you from splitting any >>> further. >>> >> >> >> >>> >> >> >>> >>> >> >> >>> > Note that this does bring up the question of whether SDKs >>> should expose coders for backlogs since ByteKeyCoder and BigEndianLongCoder >>> exist which would cover a good number of scenarios described above. This >>> coder doesn't have to be understood by the runner nor does it have to be >>> part of the portability APIs (either Runner of Fn API). WDYT? >>> >> >> >>> >>> >> >> >>> Yes we may need a Coder effectively for both sides, only thing >>> I don’t >>> >> >> >>> like is external impact in the API. I mean it is not too >>> complex, but >>> >> >> >>> adds some extras to support things that are ‘rarely’ changed. >>> >> >> >> >>> >> >> >> >>> >> >> >> Based upon Robert's suggestion above to swap to use a integral >>> floating point number and even without Robert's suggestion this won't work. >>> The idea was that a coder would help convert the byte[] backlog >>> representation to/from a type the user wants but the issue is that the >>> Runner may give any arbitrary byte[] backlog to the SDK during splitting >>> and this coder would need to be able to handle it. >>> >> >> >> >>> >> >> >>> >>> >> >> >>> > Ismael, I looked at the API around ByteKeyRangeTracker and >>> OffsetRangeTracker figured out that the classes are named as such because >>> they are trackers for the OffsetRange and ByteKeyRange classes. Some >>> options are to: >>> >> >> >>> > 1) Copy the ByteKeyRange and call it ByteKeyRestriction and >>> similarly copy OffsetRange and call it OffsetRestriction. This would allow >>> us to name the trackers ByteKeyRestrictionTracker and >>> OffsetRestrictionTracker. Note that we can't rename because that would be a >>> backwards incompatible change for existing users of >>> ByteKeyRange/OffsetRange. This would allow us to add methods relevant to >>> SDF and remove methods that aren't needed. >>> >> >> >>> > 2) Rename ByteKeyRangeTracker to >>> ByteKeyRangeRestrictionTracker and OffsetRangeTracker to >>> OffsetRangeRestrictionTracker. Not really liking this option. >>> >> >> >>> > 3) Leave things as they are. >>> >> >> >>> >>> >> >> >>> For the RangeTracker vs RestrictionTracker discussion I will >>> probably >>> >> >> >>> lean to (3) Leave things as they are) save if there is >>> important >>> >> >> >>> things to change/fix (1) which I am not aware of. >>> >> >> >> >>> >> >> >> >>> >> >> >> Sounds good to me. >>> >> >> >> >>> >> >> >>> >>> >> >> >>> On Tue, Nov 20, 2018 at 12:07 AM Lukasz Cwik <lc...@google.com> >>> wrote: >>> >> >> >>> > >>> >> >> >>> > Sorry for the late reply. >>> >> >> >>> > >>> >> >> >>> > On Thu, Nov 15, 2018 at 8:53 AM Ismaël Mejía < >>> ieme...@gmail.com> wrote: >>> >> >> >>> >> >>> >> >> >>> >> Some late comments, and my pre excuses if some questions >>> look silly, >>> >> >> >>> >> but the last documents were a lot of info that I have not >>> yet fully >>> >> >> >>> >> digested. >>> >> >> >>> >> >>> >> >> >>> >> I have some questions about the ‘new’ Backlog concept >>> following a >>> >> >> >>> >> quick look at the PR >>> >> >> >>> >> https://github.com/apache/beam/pull/6969/files >>> >> >> >>> >> >>> >> >> >>> >> 1. Is the Backlog a specific concept for each IO? Or in >>> other words: >>> >> >> >>> >> ByteKeyRestrictionTracker can be used by HBase and >>> Bigtable, but I am >>> >> >> >>> >> assuming from what I could understand that the Backlog >>> implementation >>> >> >> >>> >> will be data store specific, is this the case? or it can be >>> in some >>> >> >> >>> >> case generalized (for example for Filesystems)? >>> >> >> >>> > >>> >> >> >>> > >>> >> >> >>> > The backlog is tied heavily to the restriction tracker >>> implementation, any data store using the same restriction tracker will >>> provide the same backlog computation. For example, if HBase/Bigtable use >>> the ByteKeyRestrictionTracker then they will use the same backlog >>> calculation. Note that an implementation could subclass a restriction >>> tracker if the data store could provide additional information. For >>> example, the default backlog for a ByteKeyRestrictionTracker over >>> [startKey, endKey) is distance(currentKey, lastKey) where distance is >>> represented as byte array subtraction (which can be wildly inaccurrate as >>> the density of data is not well reflected) but if HBase/Bigtable could >>> provide the number of bytes from current key to last key, a better >>> representation could be provided. >>> >> >> >>> > >>> >> >> >>> > Other common examples of backlogs would be: >>> >> >> >>> > * files: backlog = length of file - current byte offset >>> >> >> >>> > * message queues: backlog = number of outstanding messages >>> >> >> >>> > >>> >> >> >>> >> >>> >> >> >>> >> >>> >> >> >>> >> 2. Since the backlog is a byte[] this means that it is up >>> to the user >>> >> >> >>> >> to give it a meaning depending on the situation, is this >>> correct? Also >>> >> >> >>> >> since splitRestriction has now the Backlog as an argument, >>> what do we >>> >> >> >>> >> expect the person that implements this method in a DoFn to >>> do ideally >>> >> >> >>> >> with it? Maybe a more concrete example of how things fit for >>> >> >> >>> >> File/Offset or HBase/Bigtable/ByteKey will be helpful >>> (maybe also for >>> >> >> >>> >> the BundleFinalizer concept too). >>> >> >> >>> > >>> >> >> >>> > >>> >> >> >>> > Yes, the restriction tracker/restriction/SplittableDoFn must >>> give the byte[] a meaning. This can have any meaning but we would like that >>> the backlog byte[] representation to be lexicograhically comparable (when >>> viewing the byte[] in big endian format and prefixes are smaller (e.g. 001 >>> is smaller then 0010) and preferably a linear representation. Note that all >>> restriction trackers of the same type should use the same "space" so that >>> backlogs are comparable across multiple restriction tracker instances. >>> >> >> >>> > >>> >> >> >>> > The backlog when provided to splitRestriction should be used >>> to subdivide the restriction into smaller restrictions where each would >>> have the backlog if processed (except for potentially the last). >>> >> >> >>> > >>> >> >> >>> > A concrete example would be to represent the remaining bytes >>> to process in a file as a 64 bit big endian integer, lets say that is >>> 500MiB (524288000 bytes) or 00000000 00000000 00000000 00000000 00011111 >>> 01000000 (note that the trailing zeros are optional and doesn't impact the >>> calculation). The runner could notice that processing the restriction will >>> take 10 hrs, so it asks the SDF to split at 1/16 segments by shifting the >>> bits over by 4 and asks to split using backlog 00000000 00000000 00000000 >>> 00000000 00000001 11110100. The SDK is able to convert this request back >>> into 32768000 bytes and returns 16 restrictions. Another example would be >>> for a message queue where we have 10000 messages on the queue remaining so >>> the backlog would be 00000000 00000000 00000000 00000000 00000000 00000000 >>> 00100111 00010000 when represented as a 64 bit unsigned big endian integer. >>> The runner could ask the SDK to split using a 1/8th backlog of 00000000 >>> 00000000 00000000 00000000 00000000 00000000 00000100 11100010 which the >>> SDK would break out into 8 restrictions, the first 7 responsible for >>> reading 1250 messages and stopping while the last restriction would read >>> 1250 messages and then continue to read anything else that has been >>> enqueued. >>> >> >> >>> > >>> >> >> >>> > Bundle finalization is unrelated to backlogs but is needed >>> since there is a class of data stores which need acknowledgement that says >>> I have successfully received your data and am now responsible for it such >>> as acking a message from a message queue. >>> >> >> >>> > >>> >> >> >>> >> >>> >> >> >>> >> >>> >> >> >>> >> 3. By default all Restrictions are assumed to be unbounded >>> but there >>> >> >> >>> >> is this new Restrictions.IsBounded method, can’t this >>> behavior be >>> >> >> >>> >> inferred (adapted) from the DoFn UnboundedPerElement/Bounded >>> >> >> >>> >> annotation or are these independent concepts? >>> >> >> >>> > >>> >> >> >>> > >>> >> >> >>> > UnboundedPerElement/BoundedPerElement tells us during >>> pipeline construction time what type of PCollection we will be creating >>> since we may have a bounded PCollection goto an UnboundedPerElement DoFn >>> and that will produce an unbounded PCollection and similarly we could have >>> an unbounded PCollection goto a BoundedPerElement DoFn and that will >>> produce an unbounded PCollection. Restrictions.IsBounded is used during >>> pipeline execution to inform the runner whether a restriction being >>> returned is bounded or not since unbounded restrictions can return bounded >>> restrictions during splitting. So in the above example using the message >>> queue, the first 7 restrictions that only read 1250 messages would be >>> marked with the Restrictions.IsBounded interface while the last one would >>> not be. This could also be a method on restrictions such as "IsBounded >>> isBounded()" on PCollections. >>> >> >> >>> > >>> >> >> >>> >> Extra unrelated comment: >>> >> >> >>> >> Since SDF is still @Experimental we should probably rename >>> >> >> >>> >> OffsetRangeTracker and ByteKeyRangeTracker into the >>> RestrictionTracker >>> >> >> >>> >> suffix (I don’t know why they share the RangeTracker suffix >>> for the >>> >> >> >>> >> new trackers, WDYT? >>> >> >> >>> > >>> >> >> >>> > >>> >> >> >>> > Agree, will perform in a follow-up PR. >>> >> >> >>> > >>> >> >> >>> >> >>> >> >> >>> >> On Wed, Nov 7, 2018 at 5:47 PM Lukasz Cwik < >>> lc...@google.com> wrote: >>> >> >> >>> >> > >>> >> >> >>> >> > >>> >> >> >>> >> > >>> >> >> >>> >> > On Wed, Nov 7, 2018 at 8:33 AM Robert Bradshaw < >>> rober...@google.com> wrote: >>> >> >> >>> >> >> >>> >> >> >>> >> >> I think that not returning the users specific subclass >>> should be fine. >>> >> >> >>> >> >> Does the removal of markDone imply that the consumer >>> always knows a >>> >> >> >>> >> >> "final" key to claim on any given restriction? >>> >> >> >>> >> > >>> >> >> >>> >> > >>> >> >> >>> >> > Yes, each restriction needs to support claiming a "final" >>> key that would make the restriction "done". In the BigTable/HBase case it >>> is the empty key "", for files it can be a file offset beyond the end of >>> the file. Generally, restriction trackers written by SDF authors could also >>> take an instance of an object that they can compare instance equality >>> against for a final key. Alternatively we could allow restriction trackers >>> to implement markDone() but would need the SDK have knowledge of the method >>> by having the RestrictionTracker implement interface, extend abstract base >>> class, or reflectively found so that we would be able to wrap it to provide >>> synchronization guarantees. I had toyed with the idea of using something >>> like the ProxyInvocationHandler that backs PipelineOptions to be able to >>> provide a modified version of the users instance that had the appropriate >>> synchronization guarantees but couldn't get it to work. >>> >> >> >>> >> > >>> >> >> >>> >> >> >>> >> >> >>> >> >> On Wed, Nov 7, 2018 at 1:45 AM Lukasz Cwik < >>> lc...@google.com> wrote: >>> >> >> >>> >> >> > >>> >> >> >>> >> >> > I have started to work on how to change the user >>> facing API within the Java SDK to support splitting/checkpointing[1], >>> backlog reporting[2] and bundle finalization[3]. >>> >> >> >>> >> >> > >>> >> >> >>> >> >> > I have this PR[4] which contains minimal >>> interface/type definitions to convey how the API surface would change with >>> these 4 changes: >>> >> >> >>> >> >> > 1) Exposes the ability for @SplitRestriction to take a >>> backlog suggestion on how to perform splitting and for how many >>> restrictions should be returned. >>> >> >> >>> >> >> > 2) Adds the ability for RestrictionTrackers to report >>> backlog >>> >> >> >>> >> >> > 3) Updates @ProcessElement to be required to take a >>> generic RestrictionTracker instead of the users own restriction tracker >>> type. >>> >> >> >>> >> >> > 4) Adds the ability for >>> @StartBundle/@ProcessElement/@FinishBundle to register a callback that is >>> invoked after bundle finalization. >>> >> >> >>> >> >> > >>> >> >> >>> >> >> > The details are in the javadoc comments as to how I >>> would expect the contract to play out. >>> >> >> >>> >> >> > Feel free to comment on the ML/PR around the contract >>> and after the feedback is received/digested/implemented, I would like to >>> get the changes submitted so that work can start towards providing an >>> implementation in the Java SDK, Python SDK, and Go SDK and the shared >>> runner portability library. >>> >> >> >>> >> >> > >>> >> >> >>> >> >> > I would like to call out special attention to 3 since >>> with this change it will enable us to remove the synchronization >>> requirement for users as we will wrap the underlying restriction tracker >>> allowing us to add appropriate synchronization as needed and also to watch >>> any calls that pass through the object such as the claim calls. I also >>> believe this prevents people from writing RestrictionTrackers where the >>> contract of tryClaim is subverted since markDone is outside the purview of >>> tryClaim as in ByteKeyRangeTracker[5]. >>> >> >> >>> >> >> > >>> >> >> >>> >> >> > 1: >>> https://s.apache.org/beam-checkpoint-and-split-bundles >>> >> >> >>> >> >> > 2: https://s.apache.org/beam-bundles-backlog-splitting >>> >> >> >>> >> >> > 3: https://s.apache.org/beam-finalizing-bundles >>> >> >> >>> >> >> > 4: https://github.com/apache/beam/pull/6969 >>> >> >> >>> >> >> > 5: https://github.com/apache/beam/pull/6949 >>> >>
