We execute multiple job graphs routinely because we cannot submit a single graph without it blowing up. I believe Regina spoke to this in Berlin during her talk. We instead if we are processing a database ingestion with 200 tables in it, we do a job graph per table rather than a single job graph that does all tables instead. A single job graph can be in the tens of thousands of nodes in our largest cases and we have found flink (as os 1.3/1.6.4) cannot handle graphs of that size. We’re currently testing 1.9.1 but have not retested the large graph scenario.
Billy From: Paul Lam [mailto:paullin3...@gmail.com] Sent: Wednesday, October 30, 2019 8:41 AM To: SHI Xiaogang Cc: tison; dev; user Subject: Re: [DISCUSS] Semantic and implementation of per-job mode Hi, Thanks for starting the discussion. WRT the per-job semantic, it looks natural to me that per-job means per-job-graph, because in my understanding JobGraph is the representation of a job. Could you share some use case in which a user program should contain multiple job graphs? WRT the per-program mode, I’m also in flavor of a unified cluster-side execution for user program, so +1 from my side. But I think there may be some values for the current per-job mode: we now have some common resources available on the client machine that would be read by main methods in user programs. If migrated to per-program mode, we must explicitly set the specific resources for each user program and ship them to the cluster, it would be a bit inconvenient. Also, as the job graph is compiled at the client, we can recognize the errors caused by user code before starting the cluster and easily get access to the logs. Best, Paul Lam 在 2019年10月30日，16:22，SHI Xiaogang <shixiaoga...@gmail.com<mailto:shixiaoga...@gmail.com>> 写道： Hi Thanks for bringing this. The design looks very nice to me in that 1. In the new per-job mode, we don't need to compile user programs in the client and can directly run user programs with user jars. That way, it's easier for resource isolation in multi-tenant platforms and is much safer. 2. The execution of user programs can be unified in session and per-job modes. In session mode, user jobs are submitted via a remote ClusterClient while in per-job mode user jobs are submitted via a local ClusterClient. Regards, Xiaogang tison <wander4...@gmail.com<mailto:wander4...@gmail.com>> 于2019年10月30日周三 下午3:30写道： (CC user list because I think users may have ideas on how per-job mode should look like) Hi all, In the discussion about Flink on k8s[1] we encounter a problem that opinions diverge in how so-called per-job mode works. This thread is aimed at stating a dedicated discussion about per-job semantic and how to implement it. **The AS IS per-job mode** * in standalone deployment, we bundle user jar with Flink jar, retrieve JobGraph which is the very first JobGraph from user program in classpath, and then start a Dispatcher with this JobGraph preconfigured, which launches it as "recovered" job. * in YARN deployment, we accept submission via CliFrontend, extract JobGraph which is the very first JobGraph from user program submitted, serialize the JobGraph and upload it to YARN as resource, and then when AM starts, retrieve the JobGraph as resource and start Dispatcher with this JobGraph preconfigured, follows are the same. Specifically, in order to support multiple parts job, if YARN deployment configured as "attached", it starts a SessionCluster, proceeds the progress and shutdown the cluster on job finished. **Motivation** The implementation mentioned above, however, suffers from problems. The major two of them are 1. only respect the very first JobGraph from user program 2. compile job in client side 1. Only respect the very first JobGraph from user program There is already issue about this topic[2]. As we extract JobGraph from user program by hijacking Environment#execute we actually abort any execution after the first call to #execute. Besides it surprises users many times that any logic they write in the program is possibly never executed, here the problem is that the semantic of "job" from Flink perspective. I'd like to say in current implementation "per-job" is actually "per-job-graph". However, in practices since we support jar submission it is "per-program" semantic wanted. 2. Compile job in client side Well, standalone deployment is not in the case. But in YARN deployment, we compile job and get JobGraph in client side, and then upload it to YARN. This approach, however, somehow breaks isolation. We have observed that user program contains exception handling logic which call System.exit in main method, which causes a compilation of the job exit the whole client at once. It is a critical problem if we manage multiple Flink job in a unique platform. In this case, it shut down the whole service. Besides there are many times I was asked why per-job mode doesn't run "just like" session mode but with a dedicated cluster. It might imply that current implementation mismatches users' demand. **Proposal** In order to provide a "per-program" semantic mode which acts "just like" session mode but with a dedicated cluster, I propose a workflow as below. It acts like starting a drive on cluster but is not a general driver solution as proposed here[3], the main purpose of the workflow below is for providing a "per-program" semantic mode. *From CliFrontend* 1. CliFrontend receives submission, gathers all configuration and starts a corresponding ClusterDescriptor. 2. ClusterDescriptor deploys a cluster with main class ProgramClusterEntrypoint while shipping resources including user program. 3. ProgramClusterEntrypoint#main contains logic starting components including Standalone Dispatcher, configuring user program to start a RpcClusterClient, and then invoking main method of user program. 4. RpcClusterClient acts like MiniClusterClient which is able to submit the JobGraph after leader elected so that we don't fallback to round-robin or fail submission due to no leader. 5. Whether or not deliver job result depends on user program logic, since we can already get a JobClient from execute. ProgramClusterEntrypoint exits on user program exits and all jobs submitted globally terminate. This way fits in the direction of FLIP-73 because strategy starting a RpcClusterClient can be regarded as a special Executor. After ProgramClusterEntrypoint#main starts a Cluster, it generates and passes configuration to user program so that when Executor generated, it knows to use a RpcClusterClient for submission and the address of Dispatcher. **Compatibility** In my opinion this mode can be totally an add-on to current codebase. We actually don't replace current per-job mode with so-called "per-program" mode. It happens that current per-job mode would be useless if we have such "per-program" mode so that we possibly deprecate it for preferring the other. I'm glad to discuss more into details if you're interested in, but let's say we'd better first reach a consensus on the overall design :-) Looking forward to your reply! Best, tison. [1] https://issues.apache.org/jira/browse/FLINK-9953<https://urldefense.proofpoint.com/v2/url?u=https-3A__issues.apache.org_jira_browse_FLINK-2D9953&d=DwMFoQ&c=7563p3e2zaQw0AB1wrFVgyagb2IE5rTZOYPxLxfZlX4&r=rlkM70D3djmDN7dGPzzbVKG26ShcTFDMKlX5AWucE5Q&m=fT0zUrRT-N5XEE85dO3q03TkGf3bN1V3el5frnzSQsg&s=p428wH8eWmBwyjHaE0vClbGi51CQxgjJ6Js3X9Kyr04&e=> [2] https://issues.apache.org/jira/browse/FLINK-10879<https://urldefense.proofpoint.com/v2/url?u=https-3A__issues.apache.org_jira_browse_FLINK-2D10879&d=DwMFoQ&c=7563p3e2zaQw0AB1wrFVgyagb2IE5rTZOYPxLxfZlX4&r=rlkM70D3djmDN7dGPzzbVKG26ShcTFDMKlX5AWucE5Q&m=fT0zUrRT-N5XEE85dO3q03TkGf3bN1V3el5frnzSQsg&s=mEzfvloedca1XW6pqI9LrR--IKhrkg-YmFMXRULqVSQ&e=> [3] https://docs.google.com/document/d/1dJnDOgRae9FzoBzXcsGoutGB1YjTi1iqG6d1Ht61EnY/edit#<https://urldefense.proofpoint.com/v2/url?u=https-3A__docs.google.com_document_d_1dJnDOgRae9FzoBzXcsGoutGB1YjTi1iqG6d1Ht61EnY_edit-23&d=DwMFoQ&c=7563p3e2zaQw0AB1wrFVgyagb2IE5rTZOYPxLxfZlX4&r=rlkM70D3djmDN7dGPzzbVKG26ShcTFDMKlX5AWucE5Q&m=fT0zUrRT-N5XEE85dO3q03TkGf3bN1V3el5frnzSQsg&s=XNVcSV52D3KneNkZgP7tgo9Y4uBm0jsN0RfYaelP7JM&e=> ________________________________ Your Personal Data: We may collect and process information about you that may be subject to data protection laws. 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