Hi Kurt, What you said is the 1st reason. The second reason is this query need to scan the whole table. I think we can do better :-)
Best, Jiawei On Wed, Mar 11, 2020 at 10:52 AM Kurt Young <ykt...@gmail.com> wrote: > Hi Jiawai, > > Sorry I still didn't fully get your question. What's wrong with your > proposed SQL? > > > select vendorId, sum(inventory units) > > from dynamodb > > where today's time - inbound time > 15 > > group by vendorId > > My guess is that such query would only trigger calculations by new event. > So if a very old > inventory like inbounded 17 days ago, and there is no new events coming > about that inventory, > then the calculation would not be triggered and you can't sum it, right? > > Best, > Kurt > > > On Wed, Mar 11, 2020 at 10:06 AM Jiawei Wu <wujiawei5837...@gmail.com> > wrote: > >> Hi Robert, >> >> Your answer really helps. >> >> About the problem, we have 2 choices. The first one is using Flink as >> described in this email thread. The second one is using AWS Lambda >> triggered by CDC stream and compute the latest 15 days record, which is a >> walk-around solution and looks not as elegant as Flink to me. >> >> Currently we decided to choose AWS Lambda because we are familiar with >> it, and the most important, it lead to nearly no operational burden. But we >> are actively looking for the comparison between Lambda and Flink and want >> to know in which situation we prefer Flink over Lambda. Several teams in >> our company are already in a hot debate about the comparison, and the >> biggest concern is the non-function requirements about Flink, such as fault >> tolerance, recovery, etc. >> >> I also searched the internet but found there are nearly no comparisons >> between Lambda and Flink except for their market share :-( I'm wondering >> what do you think of this? Or any comments from flink community is >> appreciated. >> >> Thanks, >> J >> >> >> On Mon, Mar 9, 2020 at 8:22 PM Robert Metzger <rmetz...@apache.org> >> wrote: >> >>> Hey Jiawei, >>> >>> I'm sorry that you haven't received an answer yet. >>> >>> So you basically have a stream of dynamodb table updates (let's call id >>> CDC stream), and you would like to maintain the inventory of the last 15 >>> days for each vendor. >>> Whenever there's an update in the inventory data (a new event arrives in >>> the CDC stream), you want to produce a new event with the inventory count. >>> >>> If I'm not mistaken, you will need to keep all the inventory in Flink's >>> state to have an accurate count and to drop old records when they are >>> expired. >>> There are two options for maintaining the state: >>> - in memory (using the FsStateBackend) >>> - on disk (using the embedded RocksDBStatebackend) >>> >>> I would recommend starting with the RocksDBStateBackend. It will work as >>> long as your state fits on all your machines hard disks (we'll probably not >>> have an issue there :) ) >>> If you run into performance issues, you can consider switching to a >>> memory based backend (by then, you should have some knowledge about your >>> state size) >>> >>> For tracking the events, I would recommend you to look into Flink's >>> windowing API: >>> https://ci.apache.org/projects/flink/flink-docs-stable/dev/stream/operators/windows.html >>> / https://flink.apache.org/news/2015/12/04/Introducing-windows.html >>> Or alternatively doing an implementation with ProcessFunction: >>> https://ci.apache.org/projects/flink/flink-docs-release-1.10/dev/stream/operators/process_function.html >>> I personally would give it a try with ProcessFunction first. >>> >>> For reading the data from DynamoDB, there's an undocumented feature for >>> it in Flink. This is an example for reading from a DynamoDB stream: >>> https://github.com/apache/flink/blob/master/flink-connectors/flink-connector-kinesis/src/test/java/org/apache/flink/streaming/connectors/kinesis/examples/ConsumeFromDynamoDBStreams.java >>> Here's also some info: https://issues.apache.org/jira/browse/FLINK-4582 >>> >>> For writing to DynamoDB there is currently no official sink in Flink. It >>> should be fairly straightforward to implement a Sink using the SinkFunction >>> interface of Flink. >>> >>> I hope this answers your question. >>> >>> Best, >>> Robert >>> >>> >>> >>> >>> On Tue, Mar 3, 2020 at 3:25 AM Jiawei Wu <wujiawei5837...@gmail.com> >>> wrote: >>> >>>> Hi flink users, >>>> >>>> We have a problem and think flink may be a good solution for that. But >>>> I'm new to flink and hope can get some insights from flink community :) >>>> >>>> Here is the problem. Suppose we have a DynamoDB table which store the >>>> inventory data, the schema is like: >>>> >>>> * vendorId (primary key) >>>> * inventory name >>>> * inventory units >>>> * inbound time >>>> ... >>>> >>>> This DDB table keeps changing, since we have inventory coming and >>>> removal. *Every change will trigger a DynamoDB stream. * >>>> We need to calculate *all the inventory units that > 15 days for a >>>> specific vendor* like this: >>>> > select vendorId, sum(inventory units) >>>> > from dynamodb >>>> > where today's time - inbound time > 15 >>>> > group by vendorId >>>> We don't want to schedule a daily batch job, so we are trying to work >>>> on a micro-batch solution in Flink, and publish this data to another >>>> DynamoDB table. >>>> >>>> A draft idea is to use the total units minus <15 days units, since both >>>> of then have event trigger. But no detailed solutions yet. >>>> >>>> Could anyone help provide some insights here? >>>> >>>> Thanks, >>>> J. >>>> >>>
