+1 Pratyaksh Sharma <pratyaks...@gmail.com> 于2021年7月30日周五 上午1:47写道：
> Guess we should rebrand Hudi on README.md file as well - > https://github.com/apache/hudi#readme? > > This page still mentions the following - > > "Apache Hudi (pronounced Hoodie) stands for Hadoop Upserts Deletes and > Incrementals. Hudi manages the storage of large analytical datasets on > DFS (Cloud stores, HDFS or any Hadoop FileSystem compatible storage)." > > On Sat, Jul 24, 2021 at 6:31 AM Vinoth Chandar <vin...@apache.org> wrote: > >> Thanks Vino! Got a bunch of emoticons on the PR as well. >> >> Will land this monday, giving it more time over the weekend as well. >> >> >> On Wed, Jul 21, 2021 at 7:36 PM vino yang <yanghua1...@gmail.com> wrote: >> >> > Thanks vc >> > >> > Very good blog, in-depth and forward-looking. Learned! >> > >> > Best, >> > Vino >> > >> > Vinoth Chandar <vin...@apache.org> 于2021年7月22日周四 上午3:58写道： >> > >> > > Expanding to users@ as well. >> > > >> > > Hi all, >> > > >> > > Since this discussion, I started to pen down a coherent strategy and >> > convey >> > > these ideas via a blog post. >> > > I have also done my own research, talked to (ex)colleagues I respect >> to >> > get >> > > their take and refine it. >> > > >> > > Here's a blog that hopefully explains this vision. >> > > >> > > https://github.com/apache/hudi/pull/3322 >> > > >> > > Look forward to your feedback on the PR. We are hoping to land this >> early >> > > next week, if everyone is aligned. >> > > >> > > Thanks >> > > Vinoth >> > > >> > > On Wed, Apr 21, 2021 at 9:01 PM wei li <lw309637...@gmail.com> wrote: >> > > >> > > > +1 , Cannot agree more. >> > > > *aux metadata* and metatable, can make hudi have large preformance >> > > > optimization on query end. >> > > > Can continuous develop. >> > > > cache service may the necessary component in cloud native >> environment. >> > > > >> > > > On 2021/04/13 05:29:55, Vinoth Chandar <vin...@apache.org> wrote: >> > > > > Hello all, >> > > > > >> > > > > Reading one more article today, positioning Hudi, as just a table >> > > format, >> > > > > made me wonder, if we have done enough justice in explaining what >> we >> > > have >> > > > > built together here. >> > > > > I tend to think of Hudi as the data lake platform, which has the >> > > > following >> > > > > components, of which - one if a table format, one is a >> transactional >> > > > > storage layer. >> > > > > But the whole stack we have is definitely worth more than the sum >> of >> > > all >> > > > > the parts IMO (speaking from my own experience from the past 10+ >> > years >> > > of >> > > > > open source software dev). >> > > > > >> > > > > Here's what we have built so far. >> > > > > >> > > > > a) *table format* : something that stores table schema, a metadata >> > > table >> > > > > that stores file listing today, and being extended to store column >> > > ranges >> > > > > and more in the future (RFC-27) >> > > > > b) *aux metadata* : bloom filters, external record level indexes >> > today, >> > > > > bitmaps/interval trees and other advanced on-disk data structures >> > > > tomorrow >> > > > > c) *concurrency control* : we always supported MVCC based log >> based >> > > > > concurrency (serialize writes into a time ordered log), and we now >> > also >> > > > > have OCC for batch merge workloads with 0.8.0. We will have >> > multi-table >> > > > and >> > > > > fully non-blocking writers soon (see future work section of >> RFC-22) >> > > > > d) *updates/deletes* : this is the bread-and-butter use-case for >> > Hudi, >> > > > but >> > > > > we support primary/unique key constraints and we could add foreign >> > keys >> > > > as >> > > > > an extension, once our transactions can span tables. >> > > > > e) *table services*: a hudi pipeline today is self-managing - >> sizes >> > > > files, >> > > > > cleans, compacts, clusters data, bootstraps existing data - all >> these >> > > > > actions working off each other without blocking one another. (for >> > most >> > > > > parts). >> > > > > f) *data services*: we also have higher level functionality with >> > > > > deltastreamer sources (scalable DFS listing source, Kafka, Pulsar >> is >> > > > > coming, ...and more), incremental ETL support, de-duplication, >> commit >> > > > > callbacks, pre-commit validations are coming, error tables have >> been >> > > > > proposed. I could also envision us building towards streaming >> egress, >> > > > data >> > > > > monitoring. >> > > > > >> > > > > I also think we should build the following (subject to separate >> > > > > DISCUSS/RFCs) >> > > > > >> > > > > g) *caching service*: Hudi specific caching service that can hold >> > > mutable >> > > > > data and serve oft-queried data across engines. >> > > > > h) t*imeline metaserver:* We already run a metaserver in spark >> > > > > writer/drivers, backed by rocksDB & even Hudi's metadata table. >> Let's >> > > > turn >> > > > > it into a scalable, sharded metastore, that all engines can use to >> > > obtain >> > > > > any metadata. >> > > > > >> > > > > To this end, I propose we rebrand to "*Data Lake Platform*" as >> > opposed >> > > to >> > > > > "ingests & manages storage of large analytical datasets over DFS >> > (hdfs >> > > or >> > > > > cloud stores)." and convey the scope of our vision, >> > > > > given we have already been building towards that. It would also >> > provide >> > > > new >> > > > > contributors a good lens to look at the project from. >> > > > > >> > > > > (This is very similar to for e.g, the evolution of Kafka from a >> > pub-sub >> > > > > system, to an event streaming platform - with addition of >> > > > > MirrorMaker/Connect etc. ) >> > > > > >> > > > > Please share your thoughts! >> > > > > >> > > > > Thanks >> > > > > Vinoth >> > > > > >> > > > >> > > >> > >> >
