Wow, thanks for kicking this off Jan. Lots of good ideas in that list. I have a few additional ideas:
# Containers and Package Management Deploying an Erlang-based system can still be an unfriendly exercise. Rather than redouble our efforts to play nice with all variants of distro-specific package managers out there, let’s do the following: a) deliver an official Snap package (thanks Michael!) for those who still want Linux packages, and b) plug our Docker image into the popular container orchestration frameworks. I want to see one-touch cluster(able) deployments in Kubernetes, DC/OS Universe, and Docker Compose / DAB. Promote these options as preferred ways to get up and running with CouchDB. # Tombstone Curation You touched on this with some of your thoughts on Replication, but I’d like to investigate ways to excise tombstones safely from existing databases. We know that documents with wide revision trees become very unwieldy, and that the best practices around conflict management do nothing to help address this. Rather than ask the user to go in and manually purge records, can we compute when it’s safe to automatically prune a deleted edit branch? # Database Archival Clustering is great and all, but sometimes one just wants to get old data out of the database and into some cheaper storage. Many IoT historian use cases fall into this bucket. This work could take a lot of different forms, from simple whole-database archival to more subtle policy-based archiving within a database. # Integrations - Object Storage, Kafka, Spark Most of the pluggable storage engines you mentioned will not be happy about storing large attachments. We started a bit of work to optionally offload those attachments into an object store like S3 or Swift and keep just the metadata in CouchDB; I’d like to see that through. I’d also like to establish a stronger linkage with a few of our Apache brethren. Enabling a _changes feed to be published in Kafka (and a Kafka topic to be loaded into a database) will help Couch play in more sophisticated data processing pipelines. On the Spark side we’ve already written code that can be used to expose CouchDB as an external datasource, but there are still some significant optimizations that we can apply (in the vein of the cluster-aware clients mentioned below). Adam > On Sep 27, 2016, at 5:56 AM, Jan Lehnardt <[email protected]> wrote: > > Hi all, > > apologies in advance, this is going to be a long email. > > > I’ve been holding this back intentionally in order to be able to focus on > shipping 2.0, but now that that’s out, I feel we should talk about what’s > next. > > This email is separated into areas of work that I think CouchDB could improve > on, some with very concrete plans, some with rather vague ideas. I’ve been > collecting these over the past year or <strike>two</strike>five, so it’s > fairly wide, but I’m sure I’m missing things that other people find > important, so please add to this list. > > After the initial discussion here, I’ll move all of the individual issues to > JIRA, so we can go down our usual process. > > This is basically my wish list, and I’d like this to become everyone’s wish > list, so please add what I’ve been missing. :) — Note, this isn’t a > free-for-all, only suggest things that you are prepared to see through being > shipped, from design, implementation to docs. > > I don’t have a specific order for these in mind, although I have a rough idea > of what we should be doing first. Putting all of this on a roadmap is going > to be a fun future exercise for us, though :) > > One last note: this doesn’t include anything on documentation or testing. I > fully expect to step our game from here on out. This list is for the > technical aspects of the project. > > * * * > > These are the areas of work I’ve roughly come up with that my suggestions fit > into: > > - API > - Storage > - Query > - Replication > - Cluster > - Fauxton > - Releases > - Performance > - Internals > - Builds > - Features > > (I’m not claiming these are any good, but it’s what I’ve got) > > > Let’s go. > > > * * * > > # API > > ## HTTP2 > > I think this is an obvious first next step. Our HTTP Layer needs work, our > existing HTTP server library is not getting HTTP2 support, it’s time to > attack this head-first. I’m imagining a Cowboy[1]-based HTTP layer that calls > into a unified internals layer and everything will be rose-golden. HTTP2 > support for Cowboy is still in progress. Maybe we can help them along, or we > focus on the internals refactor first and drop Cowboy in later (not sure how > feasible this approach is, but we’ll figure this out. > > In my head, we focus on this and call the result 3.0 in 6-12 months. That > doesn’t mean we *only* do this, but this will be the focus (more on this > later). > > There are a few fun considerations, mainly of the “avoid Python > 2/3-chasm”-type. Do we re-implement the 2.0 API with all its idiosyncrasies, > or do we take the opportunity to clean things up while we are at it? If yes, > how and how long do we support the then old API? Do we manage this via > different ports? If yes, how can this me made to work for hosting services > like Cloudant? Etc. etc. > > [1] https://github.com/ninenines/cowboy > > > ## Sub-Document Operations > > Currently a doc update needs the whole doc body sent to the server. There are > some obvious performance improvements possible. For the longest time, I > wanted to see if we can model sub-document operations via JSON Pointers[2]. > These would roughly allow pointing to a JSON value via a URL. > > For example in this doc: > > { > "_id": "123abc", > "_rev": "zyx987", > "contact": { > "name": "", > "address": { > "street": "Long Street", > "nr": 123 > "zip": "12345" > } > } > > An update to the zip code could look like this: > > curl -X POST $SERVER/db/123abc/_jsonpointer/contact/address/zip?rev=zyx987 -d > '54321' > > GET/DELETE accordingly. We could shortcut the `_jsonpointer` to just `_` if > we like the short magic. > > JSONPointer can deal with nested objects and lists and works fairly well for > this type of stuff, and it is rather simple to implement (even I could do it: > https://github.com/janl/erl-jsonpointer/blob/master/src/jsonpointer.erl — > This idea is literally 5 years old, it looks like, no need to use my code if > there is anything better). > > This is just a raw idea, and I’m happy to solve this any other way, if > somebody has a good approach. > > [2] https://tools.ietf.org/html/rfc6901 > > > ## HTTP PATCH / JSON Diff > > Another stab at a similar problem are HTTP PATCH with JSON Diff, but with the > inherent problems of JSON normalisation, I’m leaning towards the JSONPointer > variant as simpler, but I’d be open for this as well, if someone comes up > with a good approach. > > > ## GraphQL[3] > > It’s rather new, but getting good traction[4]. This would be a nice addition > to our API. Somebody might already be hacking on this ;) > > [3]: http://graphql.org > [4]: http://githubengineering.com/the-github-graphql-api/ > > > ## Mango for Document Validation > > The only place where we absolutely require writing JS is validate_doc_update > functions. Some security behaviour can only be enforced there. With their > inherent performance problems, I’d like to get doc validations out of the > path of the query server and would love to find a way to validate document > updates through Mango. > > > ## Redesign Security System > > Our security system is slowly grown and not coherently designed. We should > start over. I have many ideas and opinions, but they are out of scope for > this. I think everybody here agrees that we can do better. This *very likely* > will *not* include per-document ACLs as per the often stated issues with that > approach in our data model. > > * * * > > > # Replication > > This is our flagship feature of course, and there are a few things we can do > better. > > > ## Mobile-optimised extension or new version of the protocol > > The original protocol design didn’t take mobile devices into account and > through PouchDB et.al. we are now learning that there are number of downsides > to our protocol. We’ve helped a lot with introducing _bulk_get/_revs, but > that’s more a bandaid than a considered strategy ;) > > That new version could also be HTTP2-only, to take advantage of the new > connection semantics there. > > > ## Easy way to skip deletes on sync > > This one is self-explanatory, mobile clients usually don’t need to sync > deletes from a year ago first. Mango filters might already get us there, > maybe we can do better. > > > ## Sync a rolling subset > > Say you always want to keep the last 90 days of email on a mobile device with > optionally back-loading older documents on user-request. It is something I > could see getting a lot of traction. > > Today, this can be built on 1.x with clever use of _purge, but that’s hardly > a good experience. I don’t know if it can be done in a cluster. > > > ## Selective Sync > > There might be other criteria than “last 90 days”, so the more general > solution to this problem class would be arbitrary (e.g. client-directed) > selective sync, but this might be really hard as opposed to just very hard of > the “last 90 days” one, so happy to punt on this first. But filters are > generally not the answer, especially with large data sets. Maybe proper sync > from views _changes is the answer. > > > ## A _db_updates powered _replicator DB > > Running thousands+ of replications on a server is not really resource > friendly today, we should teach the replicator to only run replication on > active databases via _db_updates. Somebody might already be looking into this > one. > > * * * > > > # Storage > > > ## Pluggable Storage Engines > > Paul Davis already showed some work on allowing multiple different storage > backends. I’d like to see this land. > > ## Different Storage Backends > > These don’t all have to be supported by the main project, but I’d really like > to see some experimentation with different backends like > LevelDB[5]/RocksDB[6], InnoDB[7], SQLite[8] a native-erlang one that is > optimised for space usage and not performance (I don’t want to budge on > safety). Similarly, it’d be fun to see if there is a compression format that > we can use as a storage backend directly, so we get full-DB compression as > opposed to just per-doc compression. > > [5]: http://leveldb.org > [6]: http://rocksdb.org > [7]: https://en.wikipedia.org/wiki/InnoDB > [8]: https://www.sqlite.org > > * * * > > > # Query > > ## Teach Mango JOINs and result sorting > > It’s the natural path for query languages. We should make these happen. Once > we have the basics, we might even be able to find a way to compile basic SQL > into Mango, it’s going to be glorious :) > > > ## “No-JavaScript”-mode > > I’ve hinted at this above, but I’d really like a way for users to use CouchDB > productively without having to write a line of JavaScript. My main motivation > is the poor performance characteristics of the Query Server (hello CGI[9]?). > But even with one that is improved, it will always faster to do any, say > filtering or validation operations in native Erlang. I don’t know if we can > expand Mango to cover all this, and I’m not really concerned about the > specifics, as long as we get there. > > Of course, for pro-users, the JS-variant will still be around. > > [9]: https://en.wikipedia.org/wiki/Common_Gateway_Interface > > > ## Query Server V2 > > We need to revamp the Query Server. It is hardcoded to an out-of-date version > of SpiderMonkey and we are stuck with C-bindings that barely anyone dares to > look at, let alone iterate on. > > I believe the way forward is re-vamping the query server protocol to use > streaming IO instead of blocking batches like we do now, and use JS-native > implementation of the JS-side instead of C-bindings. > > I’m partial to doing this straight in Node, because there is a ton of support > for things we need already, and I believe we’ve solved the isolation issues > required for secure MapReduce, but I’m happy to use any other thing as well, > if it helps. > > Other benefits would be support for emerging JS features that devs will want > to use. > > And we can have two modes: standalone QS like now, and embedded QS where, > say, V8 is compiled into the Erlang VM. Not everybody will want to run this, > but it’ll be neat for those who do. > > > * * * > > > # Cluster > > ## Rebalancing > > With this we will be able to grow clusters one by one instead of hitting a > wall when eventually each shard lives on a single machine. E.g. when you add > a node to the cluster, all other nodes share 1/Nth of their data with the new > node, and everything can keep going. Same for removing a node and shrinking > the cluster. > > Couchbase has this and it is really nice. > > > ## Setup > > Even without rebalancing, we need a nice Fauxton UI to manage the cluster, so > far we only have a simple setup procedure (which is great don’t get me > wrong), but users will want to do more elaborate cluster management and we > should make that easy with a slick UI. > > > ## Cluster-Aware Clients > > This might end up being not a good idea, but I’d like some experimentation > here. Say you’d have a CouchDB client that could be hooked into the cluster > topology so it’d know which nodes to query for which data, then we can save a > proxy-hop, and build clients that have lower-latency access to CouchDB. > Again, this is something that Couchbase does and I think is worth exploring. > > > > * * * > > > # Fauxton > > Fauxton is great, but it could be better too, I think. I’m mostly concerned > about number of clicks/taps required for more specialised actions (like > setting the group_level of a reduce query, it’s like 15 or so). More cluster > info would also be nice, and maybe a specialised dashboard for db-per-user > setups. > > > * * * > > > # Releases > > > ## Six-Week Release Trains > > We need to get back to frequent releases and I propose to go back to our > six-week-release train plans from three years ago. Whatever lands within a > release train time frame goes out. The nature of the change dictates the > version number increment as per semver, and we just ship a new version every > six weeks, even if it only includes a single bug fix. We should automate most > of this infrastructure, so actual releases are cheap. We are reasonably close > with this, but we need some more folks to step up on using and maintaining > our CI systems. > > > ## One major feature per major version > > I also propose to keep the scope of future major versions small, so we don’t > have to wait another 3-5 years for 3.0. In particular, I think we should > focus on a single major feature per major version and get that shipped within > 6-12 months tops. If anything needs more time, it needs to be broken up. Of > course we continue to add features and fix things while this happens, but as > a project, there is *one* major feature we push. For example, for 3.0 I see > our push be behind HTTP2 support. There is a lot of subsequent work required > to make that happen, so it’ll be a worthwhile 3.0, but we can ship it in 6-12 > months (hopefully). > > Best case scenario, we have CouchDB 4.0 coming out 12 months from now with > two new major features. That would be amazing. > > > * * * > > > # Performance > > ## Perf Team > > We need a team to comprehensive look at CouchDB performance. There is a lot > of low-hanging fruit like Robert Kowalski showed a while back, we should get > back into this. I’m mostly inspired by SQLite who’ve done a release a while > back that only focussed on 1-2% performance improvements, but got like 20-30 > of those and made the thing a lot faster across the board. I can’t remember > where I read about this, but I’ll update this once I find the link. > > > ## Benchmark Suite > > We need a benchmark suite that tests a variety of different work loads. The > goal here is to run different versions of CouchDB against the same suite on > the same hardware, to see where are going. I’m imagining a > http://arewefastyet.com style dashboard where we can track this, and even run > this on Pull Requests and not allow them if they significantly impact > performance. > > > ## Synthetic Load Suite > > This one is for end users. I’d like to be able to say: My app produces mostly > 10-20kb-sized docs, but millions of those in a single database, or across > 1000s of databases, with these views etc. and then run this on target > hardware so I’d know, e.g. how many nodes I need for a cluster with my > estimated workload. I know this can only be done in approximation, but I > think this could make a big difference in CouchDB adoption and feed back into > Perf Team mentioned above. > > * * * > > > # Internals > > ## Consolidate Repositories > > With 2.0 we started to experiment with radically small modules for our > components and I think we’ve come to the conclusion that some consolidation > is better for us going forward. Obvious candidates for separate repos are > docs, Fauxton etc. but also some of the Erlang modules that other projects > reasonably would use. > > > ## Elixir > > I’d like it very much if we elevate Elixir as a prime target language for > writing CouchDB internals. I believe this would get us an influx of new > developers that we badly need to get all the things I’m listing here done. > Somebody might be looking into the technical aspects of this already, but we > need to decide as a project if we are okay with that. > > > ## GitHub Issues > > I hope we can transition to GitHub Issues soon. > > * * * > > > # Builds > > I’d like automated builds for source, Docker et.al., rpm, deb, brew, ports, > Mac Binary, etc with proper release channels for people to subscribe to, all > powered by CI for nightly builds, so people can test in-development versions > easily. > > I’d also like builds that include popular community plugins like Geo or > Fulltext Search. > > > > * * * > > > # Features > > ## Better Support for db-per-user > > I don’t know what this will look like, but this is a pattern, and we need to > support it better. > > One approach could be “virtual dbs” that are backed by a single database, but > that’s usually at odds with views, so we could make this an XOR and disable > views on these dbs. Since this usually powers client-heavy apps, querying > usually happens there anyway. > > Another approach would be better / easier cross-db aggregation or querying. > There are a few approaches, but nothing really slick. > > > ## Schema Extraction > > I have half an (old) patch that extracts top level fields from a document and > stores them with a hash in an “attachment” to the database header. So we only > end up storing doc values and the schema hash. First of all this trades > storage for CPU time (I haven’t measured anything yet), but more > interestingly, we could use that schema data to do smart things like > auto-generating a validation function / mango expression based on the data > that is already in the database. And other fun things like easier schema > migration operations that are native in CouchDB and thus a lot faster than > external ones. For the curious ones, I’ve got the idea from V8’s property > access optimisation strategy[10]. > > [10]: https://github.com/v8/v8/wiki/Design%20Elements#fast-property-access > > * * * > > Alright, that’s it for now. Can’t wait for your feedback! > > Best > Jan > -- > Professional Support for Apache CouchDB: > https://neighbourhood.ie/couchdb-support/ >
