Fabian, Thank you for the reply. Yes I do watch via the ui, is there another way to see progress through the steps?
I think I just figured it out, the hangup is in the sort phase (ID 4) where 2 slots take all the time. Looking in the UI most slots get less than 500MB of data to sort, these two have 6.7GB and 7.3GB each, together its about 272M records and these will run for hours at this point. Looks like I need to figure out a different partitioning/sort strategy. I never noticed before because when I run the system at ~1400 slots I don't use the UI anymore as its gets unresponsive. 400 Slots is painfully slow, but still works. The getEnv output is very cool! Also very big, I've tried to summarize it here in more of a yaml format as its on a different network. Note the parallelism was just set to 10 as I didn't know if that effected output. Hopefully I didn't flub a copy paste step, it looks good to me. This flow used to be far fewer steps, but as it wasn't scaling I broke it out into all the distinct pieces so I could see where it failed. Source and sink are both Hive tables. I wonder if the inputformat is expected to give more info to seed some of these stat values? nodes id: 6 type: source pact: Data Source contents: at CreateInput(ExecutionEnvironment.java:533) parallelism: 10 global_properties: name: partitioning v: RANDOM_PARTITIONED name: Partitioning Order value: none name: Uniqueness value: not unique local_properties: name: Order value: none name: Grouping value: not grouped name: Uniqueness value: not unique estimates: name: Est. Output Size value: unknown name: Est Cardinality value: unknown costs: name: Network value: 0 name: Disk I/O value 0 name: CPU value: 0 name: Cumulative Network value: 0 name: Cumulative Disk I/O value: 0 name: Cumulative CPU value: 0 compiler_hints: name: Output Size (bytes) value: none name: Output Cardinality value: none name: Avg. Output Record Size (bytes) value: none name: Filter Factor value: none id: 5 type: pact pact: FlatMap contents: FlatMap at main() parallelism: 10 predecessors: id: 6, ship_strategy: Forward, exchange_mode: PIPELINED driver_strategy: FlatMap global_properties: name: partitioning v: RANDOM_PARTITIONED name: Partitioning Order value: none name: Uniqueness value: not unique local_properties: name: Order value: none name: Grouping value: not grouped name: Uniqueness value: not unique estimates: name: Est. Output Size value: unknown name: Est Cardinality value: unknown costs: name: Network value: 0 name: Disk I/O value 0 name: CPU value: 0 name: Cumulative Network value: 0 name: Cumulative Disk I/O value: 0 name: Cumulative CPU value: 0 compiler_hints: name: Output Size (bytes) value: none name: Output Cardinality value: none name: Avg. Output Record Size (bytes) value: none name: Filter Factor value: none id: 4 type: pact pact: Sort-Partition contents: Sort at main() parallelism: 10 predecessors: id: 5, ship_strategy: Hash Partition on [0,2] local_strategy: Sort on [0:ASC,2:ASC,1:ASC], exchange_mode: PIPELINED driver_strategy: No-Op global_properties: name: partitioning v: HASH_PARTITIONED name: Partitioned on value: [0,2] name: Partitioning Order value: none name: Uniqueness value: not unique local_properties: name: Order value: [0:ASC,2:ASC,1:ASC] name: Grouping value: [0,2,1] name: Uniqueness value: not unique estimates: name: Est. Output Size value: unknown name: Est Cardinality value: unknown costs: name: Network value: 0 name: Disk I/O value 0 name: CPU value: 0 name: Cumulative Network value: unknown name: Cumulative Disk I/O value: unknown name: Cumulative CPU value: unknown compiler_hints: name: Output Size (bytes) value: none name: Output Cardinality value: none name: Avg. Output Record Size (bytes) value: none name: Filter Factor value: none id: 3 type: pact pact: GroupReduce contents: GroupReduce at first(SortedGrouping.java:210) parallelism: 10 predecessors: id: 4, ship_strategy: Forward, exchange_mode: PIPELINED driver_strategy: Sorted Group Reduce global_properties: name: partitioning v: RANDOM_PARTITIONED name: Partitioning Order value: none name: Uniqueness value: not unique local_properties: name: Order value: none name: Grouping value: not grouped name: Uniqueness value: not unique estimates: name: Est. Output Size value: unknown name: Est Cardinality value: unknown costs: name: Network value: 0 name: Disk I/O value 0 name: CPU value: 0 name: Cumulative Network value: unknown name: Cumulative Disk I/O value: unknown name: Cumulative CPU value: unknown compiler_hints: name: Output Size (bytes) value: none name: Output Cardinality value: none name: Avg. Output Record Size (bytes) value: none name: Filter Factor value: none id: 2 type: pact pact: Map contents: Map at () parallelism: 10 predecessors: id: 3, ship_strategy: Forward, exchange_mode: PIPELINED driver_strategy: Map global_properties: name: partitioning v: RANDOM_PARTITIONED name: Partitioning Order value: none name: Uniqueness value: not unique local_properties: name: Order value: none name: Grouping value: not grouped name: Uniqueness value: not unique estimates: name: Est. Output Size value: unknown name: Est Cardinality value: unknown costs: name: Network value: 0 name: Disk I/O value 0 name: CPU value: 0 name: Cumulative Network value: unknown name: Cumulative Disk I/O value: unknown name: Cumulative CPU value: unknown compiler_hints: name: Output Size (bytes) value: none name: Output Cardinality value: none name: Avg. Output Record Size (bytes) value: none name: Filter Factor value: none id: 1 type: pact pact: Map contents: map at main() parallelism: 10 predecessors: id: 2, ship_strategy: Forward, exchange_mode: PIPELINED driver_strategy: Map global_properties: name: partitioning v: RANDOM_PARTITIONED name: Partitioning Order value: none name: Uniqueness value: not unique local_properties: name: Order value: none name: Grouping value: not grouped name: Uniqueness value: not unique estimates: name: Est. Output Size value: unknown name: Est Cardinality value: unknown costs: name: Network value: 0 name: Disk I/O value 0 name: CPU value: 0 name: Cumulative Network value: unknown name: Cumulative Disk I/O value: unknown name: Cumulative CPU value: unknown compiler_hints: name: Output Size (bytes) value: none name: Output Cardinality value: none name: Avg. Output Record Size (bytes) value: none name: Filter Factor value: none id: 0 type: sink pact: Data Sink contents: org.apache.flink.api.java.jadoop.mapreduce.HadoopOutputFormat parallelism: 10 predecessors: id: 1, ship_strategy: Forward, exchange_mode: PIPELINED driver_strategy: Map global_properties: name: partitioning v: RANDOM_PARTITIONED name: Partitioning Order value: none name: Uniqueness value: not unique local_properties: name: Order value: none name: Grouping value: not grouped name: Uniqueness value: not unique estimates: name: Est. Output Size value: unknown name: Est Cardinality value: unknown costs: name: Network value: 0 name: Disk I/O value 0 name: CPU value: 0 name: Cumulative Network value: unknown name: Cumulative Disk I/O value: unknown name: Cumulative CPU value: unknown compiler_hints: name: Output Size (bytes) value: none name: Output Cardinality value: none name: Avg. Output Record Size (bytes) value: none name: Filter Factor value: none On Tue, Dec 5, 2017 at 5:36 PM, Fabian Hueske <fhue...@gmail.com> wrote: > Hi, > > Flink's operators are designed to work in memory as long as possible and > spill to disk once the memory budget is exceeded. > Moreover, Flink aims to run programs in a pipelined fashion, such that > multiple operators can process data at the same time. > This behavior can make it a bit tricky to analyze the runtime behavior and > progress of operators. > > It would be interesting to have a look at the execution plan for the > program that you are running. > The plan can be obtained from the ExecutionEnvironment by calling > env.getExecutionPlan() instead of env.execute(). > > I would also like to know how you track the progress of the program. > Are you looking at the record counts displayed in the WebUI? > > Best, > Fabian > > > > 2017-12-05 22:03 GMT+01:00 Garrett Barton <garrett.bar...@gmail.com>: > >> I have been moving some old MR and hive workflows into Flink because I'm >> enjoying the api's and the ease of development is wonderful. Things have >> largely worked great until I tried to really scale some of the jobs >> recently. >> >> I have for example one etl job that reads in about 12B records at a time >> and does a sort, some simple transformations, validation, a re-partition >> and then output to a hive table. >> When I built it with the sample set, ~200M, it worked great, took maybe a >> minute and blew threw it. >> >> What I have observed is there is some kind of saturation reached >> depending on number of slots, number of nodes and the overall size of data >> to move. When I run the 12B set, the first 1B go through in under 1 >> minute, really really fast. But its an extremely sharp drop off after >> that, the next 1B might take 15 minutes, and then if I wait for the next >> 1B, its well over an hour. >> >> What I cant find is any obvious indicators or things to look at, >> everything just grinds to a halt, I don't think the job would ever actually >> complete. >> >> Is there something in the design of flink in batch mode that is perhaps >> memory bound? Adding more nodes/tasks does not fix it, just gets me a >> little further along. I'm already running around ~1,400 slots at this >> point, I'd postulate needing 10,000+ to potentially make the job run, but >> thats too much of my cluster gone, and I have yet to get flink to be stable >> past 1,500. >> >> Any idea's on where to look, or what to debug? GUI is also very >> cumbersome to use at this slot count too, so other measurement ideas are >> welcome too! >> >> Thank you all. >> > >