A simple doc storage version number would likely be enough for future us to do fancier things.
On Tue, Feb 19, 2019 at 4:16 PM Benjamin Anderson <banjie...@apache.org> wrote: > > I don’t think adding a layer of abstraction is the right move just yet, > I think we should continue to find consensus on one answer to this question > > Agree that the theorycrafting stage is not optimal for making > abstraction decisions, but I suspect it would be worthwhile somewhere > between prototyping and releasing. Adam's proposal does seem to me the > most appealing approach on the surface, and I don't see anyone signing > up to do the work to deliver an alternative concurrently. > > -- > ba > > On Tue, Feb 19, 2019 at 1:43 PM Robert Samuel Newson <rnew...@apache.org> > wrote: > > > > Addendum: By “directory aliasing” I meant within a document (either the > actual Directory thing or something equivalent of our own making). The > directory aliasing for each database is a good way to reduce key size > without a significant cost. Though if Redwood lands in time, even this > would become an inutile obfuscation]. > > > > > On 19 Feb 2019, at 21:39, Robert Samuel Newson <rnew...@apache.org> > wrote: > > > > > > Interesting suggestion, obviously the details might get the wrong kind > of fun. > > > > > > Somewhere above I suggested this would be something we could change > over time and even use different approaches for different documents within > the same database. This is the long way of saying there are multiple ways > to do this each with advantages and none without disadvantages. > > > > > > I don’t think adding a layer of abstraction is the right move just > yet, I think we should continue to find consensus on one answer to this > question (and the related ones in other threads) for the first release. > It’s easy to say “we can change it later”, of course. We can, though it > would be a chunk of work in the context of something that already works, > I’ve rarely seen anyone sign up for that. > > > > > > I’m fine with the first proposal from Adam, where the keys are tuples > of key parts pointing at terminal values. To make it easier for the first > version, I would exclude optimisations like deduplication or the Directory > aliasing or the schema thing that I suggested and that Ilya incorporated a > variant of in a follow-up post. We’d accept that there are limits on the > sizes of documents, including the awkward-to-express one about property > depth. > > > > > > Stepping back, I’m not seeing any essential improvement over Adam’s > original proposal besides the few corrections and clarifications made by > various authors. Could we start an RFC based on Adam’s original proposal on > document body, revision tree and index storage? We could then have PR’s > against that for each additional optimisation (one person’s optimisation is > another person’s needless complication)? > > > > > > If I’ve missed some genuine advance on the original proposal in this > long thread, please call it out for me. > > > > > > B. > > > > > >> On 19 Feb 2019, at 21:15, Benjamin Anderson <banjie...@apache.org> > wrote: > > >> > > >> As is evident by the length of this thread, there's a pretty big > > >> design space to cover here, and it seems unlikely we'll have arrived > > >> at a "correct" solution even by the time this thing ships. Perhaps it > > >> would be worthwhile to treat the in-FDB representation of data as a > > >> first-class abstraction and support multiple representations > > >> simultaneously? > > >> > > >> Obviously there's no such thing as a zero-cost abstraction - and I've > > >> not thought very hard about how far up the stack the document > > >> representation would need to leak - but supporting different layouts > > >> (primarily, as Adam points out, on the document body itself) might > > >> prove interesting and useful. I'm sure there are folks interested in a > > >> column-shaped CouchDB, for example. > > >> > > >> -- > > >> b > > >> > > >> On Tue, Feb 19, 2019 at 11:39 AM Robert Newson <rnew...@apache.org> > wrote: > > >>> > > >>> Good points on revtree, I agree with you we should store that > intelligently to gain the benefits you mentioned. > > >>> > > >>> -- > > >>> Robert Samuel Newson > > >>> rnew...@apache.org > > >>> > > >>> On Tue, 19 Feb 2019, at 18:41, Adam Kocoloski wrote: > > >>>> I do not think we should store the revtree as a blob. The design > where > > >>>> each edit branch is its own KV should save on network IO and CPU > cycles > > >>>> for normal updates. We’ve performed too many heroics to keep > > >>>> couch_key_tree from stalling entire databases when trying to update > a > > >>>> single document with a wide revision tree, I would much prefer to > ignore > > >>>> other edit branches entirely when all we’re doing is extending one > of > > >>>> them. > > >>>> > > >>>> I also do not think we should store JSON documents as blobs, but > it’s a > > >>>> closer call. Some of my reasoning for preferring the exploded path > > >>>> design: > > >>>> > > >>>> - it lends itself nicely to sub-document operations, for which Jan > > >>>> crafted an RFC last year: > https://github.com/apache/couchdb/issues/1559 > > >>>> - it optimizes the creation of Mango indexes on existing databases > since > > >>>> we only need to retrieve the value(s) we want to index > > >>>> - it optimizes Mango queries that use field selectors > > >>>> - anyone who wanted to try their hand at GraphQL will find it very > > >>>> handy: https://github.com/apache/couchdb/issues/1499 > > >>>> - looking further ahead, it lets us play with smarter leaf value > types > > >>>> like Counters (yes I’m still on the CRDT bandwagon, sorry) > > >>>> > > >>>> A few comments on the thread: > > >>>> > > >>>>>>> * Most documents bodies are probably going to be smaller than > 100k. So in > > >>>>>>> the majority of case it would be one write / one read to update > and fetch > > >>>>>>> the document body. > > >>>> > > >>>> We should test, but I expect reading 50KB of data in a range query > is > > >>>> almost as efficient as reading a single 50 KB value. Similarly, > writes > > >>>> to a contiguous set of keys should be quite efficient. > > >>>> > > >>>> I am concerned about the overhead of the repeated field paths in the > > >>>> keys with the exploded path option in the absence of key prefix > > >>>> compression. That would be my main reason to acquiesce and throw > away > > >>>> all the document structure. > > >>>> > > >>>> Adam > > >>>> > > >>>>> On Feb 19, 2019, at 12:04 PM, Robert Newson <rnew...@apache.org> > wrote: > > >>>>> > > >>>>> I like the idea that we'd reuse the same pattern (but perhaps not > the same _code_) for doc bodies, revtree and attachments. > > >>>>> > > >>>>> I hope we still get to delete couch_key_tree.erl, though. > > >>>>> > > >>>>> -- > > >>>>> Robert Samuel Newson > > >>>>> rnew...@apache.org > > >>>>> > > >>>>> On Tue, 19 Feb 2019, at 17:03, Jan Lehnardt wrote: > > >>>>>> I like the idea from a “trying a simple thing first” perspective, > but > > >>>>>> Nick’s points below are especially convincing to with this for > now. > > >>>>>> > > >>>>>> Best > > >>>>>> Jan > > >>>>>> — > > >>>>>> > > >>>>>>> On 19. Feb 2019, at 17:53, Nick Vatamaniuc <vatam...@gmail.com> > wrote: > > >>>>>>> > > >>>>>>> Hi, > > >>>>>>> > > >>>>>>> Sorry for jumping in so late, I was following from the sidelines > mostly. A > > >>>>>>> lot of good discussion happening and am excited about the > possibilities > > >>>>>>> here. > > >>>>>>> > > >>>>>>> I do like the simpler "chunking" approach for a few reasons: > > >>>>>>> > > >>>>>>> * Most documents bodies are probably going to be smaller than > 100k. So in > > >>>>>>> the majority of case it would be one write / one read to update > and fetch > > >>>>>>> the document body. > > >>>>>>> > > >>>>>>> * We could reuse the chunking code for attachment handling and > possibly > > >>>>>>> revision key trees. So it's the general pattern of upload chunks > to some > > >>>>>>> prefix, and when finished flip an atomic toggle to make it > current. > > >>>>>>> > > >>>>>>> * Do the same thing with revision trees and we could re-use the > revision > > >>>>>>> tree manipulation logic. That is, the key tree in most cases > would be small > > >>>>>>> enough to fit in 100k but if they get huge, they'd get chunked. > This would > > >>>>>>> allow us to reuse all the battle tested couch_key_tree code > mostly as is. > > >>>>>>> We even have property tests for it > > >>>>>>> > https://github.com/apache/couchdb/blob/master/src/couch/test/couch_key_tree_prop_tests.erl > > >>>>>>> > > >>>>>>> * It removes the need to explain the max exploded path length > limitation to > > >>>>>>> customers. > > >>>>>>> > > >>>>>>> Cheers, > > >>>>>>> -Nick > > >>>>>>> > > >>>>>>> > > >>>>>>> On Tue, Feb 19, 2019 at 11:18 AM Robert Newson < > rnew...@apache.org> wrote: > > >>>>>>> > > >>>>>>>> Hi, > > >>>>>>>> > > >>>>>>>> An alternative storage model that we should seriously consider > is to > > >>>>>>>> follow our current approach in couch_file et al. Specifically, > that the > > >>>>>>>> document _body_ is stored as an uninterpreted binary value. > This would be > > >>>>>>>> much like the obvious plan for attachment storage; a key prefix > that > > >>>>>>>> identifies the database and document, with the final item of > that key tuple > > >>>>>>>> is an incrementing integer. Each of those keys has a binary > value of up to > > >>>>>>>> 100k. Fetching all values with that key prefix, in fdb's > natural ordering, > > >>>>>>>> will yield the full document body, which can be JSON decoded > for further > > >>>>>>>> processing. > > >>>>>>>> > > >>>>>>>> I like this idea, and I like Adam's original proposal to > explode documents > > >>>>>>>> into property paths. I have a slight preference for the > simplicity of the > > >>>>>>>> idea in the previous paragraph, not least because it's close to > what we do > > >>>>>>>> today. I also think it will be possible to migrate to > alternative storage > > >>>>>>>> models in future, and foundationdb's transaction supports means > we can do > > >>>>>>>> this migration seamlessly should we come to it. > > >>>>>>>> > > >>>>>>>> I'm very interested in knowing if anyone else is interested in > going this > > >>>>>>>> simple, or considers it a wasted opportunity relative to the > 'exploded' > > >>>>>>>> path. > > >>>>>>>> > > >>>>>>>> B. > > >>>>>>>> > > >>>>>>>> -- > > >>>>>>>> Robert Samuel Newson > > >>>>>>>> rnew...@apache.org > > >>>>>>>> > > >>>>>>>> On Mon, 4 Feb 2019, at 19:59, Robert Newson wrote: > > >>>>>>>>> I've been remiss here in not posting the data model ideas that > IBM > > >>>>>>>>> worked up while we were thinking about using FoundationDB so > I'm posting > > >>>>>>>>> it now. This is Adam' Kocoloski's original work, I am just > transcribing > > >>>>>>>>> it, and this is the context that the folks from the IBM side > came in > > >>>>>>>>> with, for full disclosure. > > >>>>>>>>> > > >>>>>>>>> Basics > > >>>>>>>>> > > >>>>>>>>> 1. All CouchDB databases are inside a Directory > > >>>>>>>>> 2. Each CouchDB database is a Directory within that Directory > > >>>>>>>>> 3. It's possible to list all subdirectories of a Directory, so > > >>>>>>>>> `_all_dbs` is the list of directories from 1. > > >>>>>>>>> 4. Each Directory representing a CouchdB database has several > Subspaces; > > >>>>>>>>> 4a. by_id/ doc subspace: actual document contents > > >>>>>>>>> 4b. by_seq/versionstamp subspace: for the _changes feed > > >>>>>>>>> 4c. index_definitions, indexes, ... > > >>>>>>>>> > > >>>>>>>>> JSON Mapping > > >>>>>>>>> > > >>>>>>>>> A hierarchical JSON object naturally maps to multiple KV pairs > in FDB: > > >>>>>>>>> > > >>>>>>>>> { > > >>>>>>>>> “_id”: “foo”, > > >>>>>>>>> “owner”: “bob”, > > >>>>>>>>> “mylist”: [1,3,5], > > >>>>>>>>> “mymap”: { > > >>>>>>>>> “blue”: “#0000FF”, > > >>>>>>>>> “red”: “#FF0000” > > >>>>>>>>> } > > >>>>>>>>> } > > >>>>>>>>> > > >>>>>>>>> maps to > > >>>>>>>>> > > >>>>>>>>> (“foo”, “owner”) = “bob” > > >>>>>>>>> (“foo”, “mylist”, 0) = 1 > > >>>>>>>>> (“foo”, “mylist”, 1) = 3 > > >>>>>>>>> (“foo”, “mylist”, 2) = 5 > > >>>>>>>>> (“foo”, “mymap”, “blue”) = “#0000FF” > > >>>>>>>>> (“foo”, “mymap”, “red”) = “#FF0000” > > >>>>>>>>> > > >>>>>>>>> NB: this means that the 100KB limit applies to individual > leafs in the > > >>>>>>>>> JSON object, not the entire doc > > >>>>>>>>> > > >>>>>>>>> Edit Conflicts > > >>>>>>>>> > > >>>>>>>>> We need to account for the presence of conflicts in various > levels of > > >>>>>>>>> the doc due to replication. > > >>>>>>>>> > > >>>>>>>>> Proposal is to create a special value indicating that the > subtree below > > >>>>>>>>> our current cursor position is in an unresolvable conflict. > Then add > > >>>>>>>>> additional KV pairs below to describe the conflicting entries. > > >>>>>>>>> > > >>>>>>>>> KV data model allows us to store these efficiently and minimize > > >>>>>>>>> duplication of data: > > >>>>>>>>> > > >>>>>>>>> A document with these two conflicts: > > >>>>>>>>> > > >>>>>>>>> { > > >>>>>>>>> “_id”: “foo”, > > >>>>>>>>> “_rev”: “1-abc”, > > >>>>>>>>> “owner”: “alice”, > > >>>>>>>>> “active”: true > > >>>>>>>>> } > > >>>>>>>>> { > > >>>>>>>>> “_id”: “foo”, > > >>>>>>>>> “_rev”: “1-def”, > > >>>>>>>>> “owner”: “bob”, > > >>>>>>>>> “active”: true > > >>>>>>>>> } > > >>>>>>>>> > > >>>>>>>>> could be stored thus: > > >>>>>>>>> > > >>>>>>>>> (“foo”, “active”) = true > > >>>>>>>>> (“foo”, “owner”) = kCONFLICT > > >>>>>>>>> (“foo”, “owner”, “1-abc”) = “alice” > > >>>>>>>>> (“foo”, “owner”, “1-def”) = “bob” > > >>>>>>>>> > > >>>>>>>>> So long as `kCONFLICT` is set at the top of the conflicting > subtree this > > >>>>>>>>> representation can handle conflicts of different data types as > well. > > >>>>>>>>> > > >>>>>>>>> Missing fields need to be handled explicitly: > > >>>>>>>>> > > >>>>>>>>> { > > >>>>>>>>> “_id”: “foo”, > > >>>>>>>>> “_rev”: “1-abc”, > > >>>>>>>>> “owner”: “alice”, > > >>>>>>>>> “active”: true > > >>>>>>>>> } > > >>>>>>>>> > > >>>>>>>>> { > > >>>>>>>>> “_id”: “foo”, > > >>>>>>>>> “_rev”: “1-def”, > > >>>>>>>>> “owner”: { > > >>>>>>>>> “name”: “bob”, > > >>>>>>>>> “email”: “ > > >>>>>>>>> b...@example.com > > >>>>>>>>> " > > >>>>>>>>> } > > >>>>>>>>> } > > >>>>>>>>> > > >>>>>>>>> could be stored thus: > > >>>>>>>>> > > >>>>>>>>> (“foo”, “active”) = kCONFLICT > > >>>>>>>>> (“foo”, “active”, “1-abc”) = true > > >>>>>>>>> (“foo”, “active”, “1-def”) = kMISSING > > >>>>>>>>> (“foo”, “owner”) = kCONFLICT > > >>>>>>>>> (“foo”, “owner”, “1-abc”) = “alice” > > >>>>>>>>> (“foo”, “owner”, “1-def”, “name”) = “bob” > > >>>>>>>>> (“foo”, “owner”, “1-def”, “email”) = ... > > >>>>>>>>> > > >>>>>>>>> Revision Metadata > > >>>>>>>>> > > >>>>>>>>> * CouchDB uses a hash history for revisions > > >>>>>>>>> ** Each edit is identified by the hash of the content of the > edit > > >>>>>>>>> including the base revision against which it was applied > > >>>>>>>>> ** Individual edit branches are bounded in length but the > number of > > >>>>>>>>> branches is potentially unbounded > > >>>>>>>>> > > >>>>>>>>> * Size limits preclude us from storing the entire key tree as > a single > > >>>>>>>>> value; in pathological situations > > >>>>>>>>> the tree could exceed 100KB (each entry is > 16 bytes) > > >>>>>>>>> > > >>>>>>>>> * Store each edit branch as a separate KV including deleted > status in a > > >>>>>>>>> special subspace > > >>>>>>>>> > > >>>>>>>>> * Structure key representation so that “winning” revision can > be > > >>>>>>>>> automatically retrieved in a limit=1 > > >>>>>>>>> key range operation > > >>>>>>>>> > > >>>>>>>>> (“foo”, “_meta”, “deleted=false”, 1, “def”) = [] > > >>>>>>>>> (“foo”, “_meta”, “deleted=false”, 4, “bif”) = > [“3-baz”,”2-bar”,”1-foo”] > > >>>>>>>>> <-- winner > > >>>>>>>>> (“foo”, “_meta”, “deleted=true”, 3, “abc”) = [“2-bar”, “1-foo”] > > >>>>>>>>> > > >>>>>>>>> Changes Feed > > >>>>>>>>> > > >>>>>>>>> * FDB supports a concept called a versionstamp — a 10 byte, > unique, > > >>>>>>>>> monotonically (but not sequentially) increasing value for each > committed > > >>>>>>>>> transaction. The first 8 bytes are the committed version of the > > >>>>>>>>> database. The last 2 bytes are monotonic in the serialization > order for > > >>>>>>>>> transactions. > > >>>>>>>>> > > >>>>>>>>> * A transaction can specify a particular index into a key > where the > > >>>>>>>>> following 10 bytes will be overwritten by the versionstamp at > commit > > >>>>>>>>> time > > >>>>>>>>> > > >>>>>>>>> * A subspace keyed on versionstamp naturally yields a _changes > feed > > >>>>>>>>> > > >>>>>>>>> by_seq subspace > > >>>>>>>>> (“versionstamp1”) = (“foo”, “1-abc”) > > >>>>>>>>> (“versionstamp4”) = (“bar”, “4-def”) > > >>>>>>>>> > > >>>>>>>>> by_id subspace > > >>>>>>>>> (“bar”, “_vsn”) = “versionstamp4” > > >>>>>>>>> ... > > >>>>>>>>> (“foo”, “_vsn”) = “versionstamp1” > > >>>>>>>>> > > >>>>>>>>> JSON Indexes > > >>>>>>>>> > > >>>>>>>>> * “Mango” JSON indexes are defined by > > >>>>>>>>> ** a list of field names, each of which may be nested, > > >>>>>>>>> ** an optional partial_filter_selector which constrains the > set of docs > > >>>>>>>>> that contribute > > >>>>>>>>> ** an optional name defined by the ddoc field (the name is > auto- > > >>>>>>>>> generated if not supplied) > > >>>>>>>>> > > >>>>>>>>> * Store index definitions in a single subspace to aid query > planning > > >>>>>>>>> ** ((person,name), title, email) = (“name-title-email”, > “{“student”: > > >>>>>>>>> true}”) > > >>>>>>>>> ** Store the values for each index in a dedicated subspace, > adding the > > >>>>>>>>> document ID as the last element in the tuple > > >>>>>>>>> *** (“rosie revere”, “engineer”, “ro...@example.com", “foo”) > = null > > >>>>>>>>> > > >>>>>>>>> B. > > >>>>>>>>> > > >>>>>>>>> -- > > >>>>>>>>> Robert Samuel Newson > > >>>>>>>>> rnew...@apache.org > > >>>>>>>>> > > >>>>>>>>> On Mon, 4 Feb 2019, at 19:13, Ilya Khlopotov wrote: > > >>>>>>>>>> > > >>>>>>>>>> I want to fix previous mistakes. I did two mistakes in > previous > > >>>>>>>>>> calculations: > > >>>>>>>>>> - I used 1Kb as base size for calculating expansion factor > (although > > >>>>>>>> we > > >>>>>>>>>> don't know exact size of original document) > > >>>>>>>>>> - The expansion factor calculation included number of > revisions (it > > >>>>>>>>>> shouldn't) > > >>>>>>>>>> > > >>>>>>>>>> I'll focus on flattened JSON docs model > > >>>>>>>>>> > > >>>>>>>>>> The following formula is used in previous calculation. > > >>>>>>>>>> > storage_size_per_document=mapping_table_size*number_of_revisions + > > >>>>>>>>>> depth*number_of_paths*number_of_revisions + > > >>>>>>>>>> number_of_paths*value_size*number_of_revisions > > >>>>>>>>>> > > >>>>>>>>>> To clarify things a little bit I want to calculate space > requirement > > >>>>>>>> for > > >>>>>>>>>> single revision this time. > > >>>>>>>>>> > mapping_table_size=field_name_size*(field_name_length+4(integer > > >>>>>>>>>> size))=100 * (20 + 4(integer size)) = 2400 bytes > > >>>>>>>>>> > storage_size_per_document_per_revision_per_replica=mapping_table_size > > >>>>>>>> + > > >>>>>>>>>> depth*number_of_paths + value_size*number_of_paths = > > >>>>>>>>>> 2400bytes + 10*1000+1000*100=112400bytes~=110 Kb > > >>>>>>>>>> > > >>>>>>>>>> We definitely can reduce requirement for mapping table by > adopting > > >>>>>>>>>> rnewson's idea of a schema. > > >>>>>>>>>> > > >>>>>>>>>> On 2019/02/04 11:08:16, Ilya Khlopotov <iil...@apache.org> > wrote: > > >>>>>>>>>>> Hi Michael, > > >>>>>>>>>>> > > >>>>>>>>>>>> For example, hears a crazy thought: > > >>>>>>>>>>>> Map every distinct occurence of a key/value instance > through a > > >>>>>>>> crypto hash > > >>>>>>>>>>>> function to get a set of hashes. > > >>>>>>>>>>>> > > >>>>>>>>>>>> These can be be precomputed by Couch without any lookups in > FDB. > > >>>>>>>> These > > >>>>>>>>>>>> will be spread all over kingdom come in FDB and not lend > > >>>>>>>> themselves to > > >>>>>>>>>>>> range search well. > > >>>>>>>>>>>> > > >>>>>>>>>>>> So what you do is index them for frequency of occurring in > the > > >>>>>>>> same set. > > >>>>>>>>>>>> In essence, you 'bucket them' statistically, and that > bucket id > > >>>>>>>> becomes a > > >>>>>>>>>>>> key prefix. A crypto hash value can be copied into more > than one > > >>>>>>>> bucket. > > >>>>>>>>>>>> The {bucket_id}/{cryptohash} becomes a {val_id} > > >>>>>>>>>>> > > >>>>>>>>>>>> When writing a document, Couch submits the list/array of > > >>>>>>>> cryptohash values > > >>>>>>>>>>>> it computed to FDB and gets back the corresponding > {val_id} (the > > >>>>>>>> id with > > >>>>>>>>>>>> the bucket prefixed). This can get somewhat expensive if > there's > > >>>>>>>> always a > > >>>>>>>>>>>> lot of app local cache misses. > > >>>>>>>>>>>> > > >>>>>>>>>>>> A document's value is then a series of {val_id} arrays up > to 100k > > >>>>>>>> per > > >>>>>>>>>>>> segment. > > >>>>>>>>>>>> > > >>>>>>>>>>>> When retrieving a document, you get the val_ids, find the > distinct > > >>>>>>>> buckets > > >>>>>>>>>>>> and min/max entries for this doc, and then parallel query > each > > >>>>>>>> bucket while > > >>>>>>>>>>>> reconstructing the document. > > >>>>>>>>>>> > > >>>>>>>>>>> Interesting idea. Let's try to think it through to see if we > can > > >>>>>>>> make it viable. > > >>>>>>>>>>> Let's go through hypothetical example. Input data for the > example: > > >>>>>>>>>>> - 1M of documents > > >>>>>>>>>>> - each document is around 10Kb > > >>>>>>>>>>> - each document consists of 1K of unique JSON paths > > >>>>>>>>>>> - each document has 100 unique JSON field names > > >>>>>>>>>>> - every scalar value is 100 bytes > > >>>>>>>>>>> - 10% of unique JSON paths for every document already stored > in > > >>>>>>>> database under different doc or different revision of the > current one > > >>>>>>>>>>> - we assume 3 independent copies for every key-value pair in > FDB > > >>>>>>>>>>> - our hash key size is 32 bytes > > >>>>>>>>>>> - let's assume we can determine if key is already on the > storage > > >>>>>>>> without doing query > > >>>>>>>>>>> - 1% of paths is in cache (unrealistic value, in real live > the > > >>>>>>>> percentage is lower) > > >>>>>>>>>>> - every JSON field name is 20 bytes > > >>>>>>>>>>> - every JSON path is 10 levels deep > > >>>>>>>>>>> - document key prefix length is 50 > > >>>>>>>>>>> - every document has 10 revisions > > >>>>>>>>>>> Let's estimate the storage requirements and size of data we > need to > > >>>>>>>> transmit. The calculations are not exact. > > >>>>>>>>>>> 1. storage_size_per_document (we cannot estimate exact > numbers since > > >>>>>>>> we don't know how FDB stores it) > > >>>>>>>>>>> - 10 * ((10Kb - (10Kb * 10%)) + (1K - (1K * 10%)) * 32 > bytes) = > > >>>>>>>> 38Kb * 10 * 3 = 1140 Kb (11x) > > >>>>>>>>>>> 2. number of independent keys to retrieve on document read > > >>>>>>>> (non-range queries) per document > > >>>>>>>>>>> - 1K - (1K * 1%) = 990 > > >>>>>>>>>>> 3. number of range queries: 0 > > >>>>>>>>>>> 4. data to transmit on read: (1K - (1K * 1%)) * (100 bytes + > 32 > > >>>>>>>> bytes) = 102 Kb (10x) > > >>>>>>>>>>> 5. read latency (we use 2ms per read based on numbers from > > >>>>>>>> https://apple.github.io/foundationdb/performance.html) > > >>>>>>>>>>> - sequential: 990*2ms = 1980ms > > >>>>>>>>>>> - range: 0 > > >>>>>>>>>>> Let's compare these numbers with initial proposal (flattened > JSON > > >>>>>>>> docs without global schema and without cache) > > >>>>>>>>>>> 1. storage_size_per_document > > >>>>>>>>>>> - mapping table size: 100 * (20 + 4(integer size)) = 2400 > bytes > > >>>>>>>>>>> - key size: (10 * (4 + 1(delimiter))) + 50 = 100 bytes > > >>>>>>>>>>> - storage_size_per_document: 2.4K*10 + 100*1K*10 + 1K*100*10 > = > > >>>>>>>> 2024K = 1976 Kb * 3 = 5930 Kb (59.3x) > > >>>>>>>>>>> 2. number of independent keys to retrieve: 0-2 (depending on > index > > >>>>>>>> structure) > > >>>>>>>>>>> 3. number of range queries: 1 (1001 of keys in result) > > >>>>>>>>>>> 4. data to transmit on read: 24K + 1000*100 + 1000*100 = > 23.6 Kb > > >>>>>>>> (2.4x) > > >>>>>>>>>>> 5. read latency (we use 2ms per read based on numbers from > > >>>>>>>> https://apple.github.io/foundationdb/performance.html and > estimate range > > >>>>>>>> read performance based on numbers from > > >>>>>>>> > https://apple.github.io/foundationdb/benchmarking.html#single-core-read-test > > >>>>>>>> ) > > >>>>>>>>>>> - range read performance: Given read performance is about > 305,000 > > >>>>>>>> reads/second and range performance 3,600,000 keys/second we > estimate range > > >>>>>>>> performance to be 11.8x compared to read performance. If read > performance > > >>>>>>>> is 2ms than range performance is 0.169ms (which is hard to > believe). > > >>>>>>>>>>> - sequential: 2 * 2 = 4ms > > >>>>>>>>>>> - range: 0.169 > > >>>>>>>>>>> > > >>>>>>>>>>> It looks like we are dealing with a tradeoff: > > >>>>>>>>>>> - Map every distinct occurrence of a key/value instance > through a > > >>>>>>>> crypto hash: > > >>>>>>>>>>> - 5.39x more disk space efficient > > >>>>>>>>>>> - 474x slower > > >>>>>>>>>>> - flattened JSON model > > >>>>>>>>>>> - 5.39x less efficient in disk space > > >>>>>>>>>>> - 474x faster > > >>>>>>>>>>> > > >>>>>>>>>>> In any case this unscientific exercise was very helpful. > Since it > > >>>>>>>> uncovered the high cost in terms of disk space. 59.3x of > original disk size > > >>>>>>>> is too much IMO. > > >>>>>>>>>>> > > >>>>>>>>>>> Are the any ways we can make Michael's model more performant? > > >>>>>>>>>>> > > >>>>>>>>>>> Also I don't quite understand few aspects of the global hash > table > > >>>>>>>> proposal: > > >>>>>>>>>>> > > >>>>>>>>>>> 1. > - Map every distinct occurence of a key/value instance > through > > >>>>>>>> a crypto hash function to get a set of hashes. > > >>>>>>>>>>> I think we are talking only about scalar values here? I.e. > > >>>>>>>> `"#/foo.bar.baz": 123` > > >>>>>>>>>>> Since I don't know how we can make it work for all possible > JSON > > >>>>>>>> paths `{"foo": {"bar": {"size": 12, "baz": 123}}}": > > >>>>>>>>>>> - foo > > >>>>>>>>>>> - foo.bar > > >>>>>>>>>>> - foo.bar.baz > > >>>>>>>>>>> > > >>>>>>>>>>> 2. how to delete documents > > >>>>>>>>>>> > > >>>>>>>>>>> Best regards, > > >>>>>>>>>>> ILYA > > >>>>>>>>>>> > > >>>>>>>>>>> > > >>>>>>>>>>> On 2019/01/30 23:33:22, Michael Fair < > mich...@daclubhouse.net> > > >>>>>>>> wrote: > > >>>>>>>>>>>> On Wed, Jan 30, 2019, 12:57 PM Adam Kocoloski < > kocol...@apache.org > > >>>>>>>> wrote: > > >>>>>>>>>>>> > > >>>>>>>>>>>>> Hi Michael, > > >>>>>>>>>>>>> > > >>>>>>>>>>>>>> The trivial fix is to use DOCID/REVISIONID as DOC_KEY. > > >>>>>>>>>>>>> > > >>>>>>>>>>>>> Yes that’s definitely one way to address storage of edit > > >>>>>>>> conflicts. I > > >>>>>>>>>>>>> think there are other, more compact representations that > we can > > >>>>>>>> explore if > > >>>>>>>>>>>>> we have this “exploded” data model where each scalar value > maps > > >>>>>>>> to an > > >>>>>>>>>>>>> individual KV pair. > > >>>>>>>>>>>> > > >>>>>>>>>>>> > > >>>>>>>>>>>> I agree, as I mentioned on the original thread, I see a > scheme, > > >>>>>>>> that > > >>>>>>>>>>>> handles both conflicts and revisions, where you only have > to store > > >>>>>>>> the most > > >>>>>>>>>>>> recent change to a field. Like you suggested, multiple > revisions > > >>>>>>>> can share > > >>>>>>>>>>>> a key. Which in my mind's eye further begs the > conflicts/revisions > > >>>>>>>>>>>> discussion along with the working within the limits > discussion > > >>>>>>>> because it > > >>>>>>>>>>>> seems to me they are all intrinsically related as a > "feature". > > >>>>>>>>>>>> > > >>>>>>>>>>>> Saying 'We'll break documents up into roughly 80k > segments', then > > >>>>>>>> trying to > > >>>>>>>>>>>> overlay some kind of field sharing scheme for > revisions/conflicts > > >>>>>>>> doesn't > > >>>>>>>>>>>> seem like it will work. > > >>>>>>>>>>>> > > >>>>>>>>>>>> I probably should have left out the trivial fix proposal as > I > > >>>>>>>> don't think > > >>>>>>>>>>>> it's a feasible solution to actually use. > > >>>>>>>>>>>> > > >>>>>>>>>>>> The comment is more regarding that I do not see how this > thread > > >>>>>>>> can escape > > >>>>>>>>>>>> including how to store/retrieve conflicts/revisions. > > >>>>>>>>>>>> > > >>>>>>>>>>>> For instance, the 'doc as individual fields' proposal lends > itself > > >>>>>>>> to value > > >>>>>>>>>>>> sharing across mutiple documents (and I don't just mean > revisions > > >>>>>>>> of the > > >>>>>>>>>>>> same doc, I mean the same key/value instance could be > shared for > > >>>>>>>> every > > >>>>>>>>>>>> document). > > >>>>>>>>>>>> However that's not really relevant if we're not considering > the > > >>>>>>>> amount of > > >>>>>>>>>>>> shared information across documents in the storage scheme. > > >>>>>>>>>>>> > > >>>>>>>>>>>> Simply storing documents in <100k segments (perhaps in some > kind of > > >>>>>>>>>>>> compressed binary representation) to deal with that FDB > limit > > >>>>>>>> seems fine. > > >>>>>>>>>>>> The only reason to consider doing something else is because > of its > > >>>>>>>> impact > > >>>>>>>>>>>> to indexing, searches, reduce functions, revisions, on-disk > size > > >>>>>>>> impact, > > >>>>>>>>>>>> etc. > > >>>>>>>>>>>> > > >>>>>>>>>>>> > > >>>>>>>>>>>> > > >>>>>>>>>>>>>> I'm assuming the process will flatten the key paths of the > > >>>>>>>> document into > > >>>>>>>>>>>>> an array and then request the value of each key as multiple > > >>>>>>>> parallel > > >>>>>>>>>>>>> queries against FDB at once > > >>>>>>>>>>>>> > > >>>>>>>>>>>>> Ah, I think this is not one of Ilya’s assumptions. He’s > trying > > >>>>>>>> to design a > > >>>>>>>>>>>>> model which allows the retrieval of a document with a > single > > >>>>>>>> range read, > > >>>>>>>>>>>>> which is a good goal in my opinion. > > >>>>>>>>>>>>> > > >>>>>>>>>>>> > > >>>>>>>>>>>> I am not sure I agree. > > >>>>>>>>>>>> > > >>>>>>>>>>>> Think of bitTorrent, a single range read should pull back > the > > >>>>>>>> structure of > > >>>>>>>>>>>> the document (the pieces to fetch), but not necessarily the > whole > > >>>>>>>> document. > > >>>>>>>>>>>> > > >>>>>>>>>>>> What if you already have a bunch of pieces in common with > other > > >>>>>>>> documents > > >>>>>>>>>>>> locally (a repeated header/footer/ or type for example); > and you > > >>>>>>>> only need > > >>>>>>>>>>>> to get a few pieces of data you don't already have? > > >>>>>>>>>>>> > > >>>>>>>>>>>> The real goal to Couch I see is to treat your document set > like the > > >>>>>>>>>>>> collection of structured information that it is. In some > respects > > >>>>>>>> like an > > >>>>>>>>>>>> extension of your application's heap space for structured > objects > > >>>>>>>> and > > >>>>>>>>>>>> efficiently querying that collection to get back subsets of > the > > >>>>>>>> data. > > >>>>>>>>>>>> > > >>>>>>>>>>>> Otherwise it seems more like a slightly upgraded file > system plus > > >>>>>>>> a fancy > > >>>>>>>>>>>> grep/find like feature... > > >>>>>>>>>>>> > > >>>>>>>>>>>> The best way I see to unlock more features/power is to a > move > > >>>>>>>> towards a > > >>>>>>>>>>>> more granular and efficient way to store and retrieve the > scalar > > >>>>>>>> values... > > >>>>>>>>>>>> > > >>>>>>>>>>>> > > >>>>>>>>>>>> > > >>>>>>>>>>>> For example, hears a crazy thought: > > >>>>>>>>>>>> Map every distinct occurence of a key/value instance > through a > > >>>>>>>> crypto hash > > >>>>>>>>>>>> function to get a set of hashes. > > >>>>>>>>>>>> > > >>>>>>>>>>>> These can be be precomputed by Couch without any lookups in > FDB. > > >>>>>>>> These > > >>>>>>>>>>>> will be spread all over kingdom come in FDB and not lend > > >>>>>>>> themselves to > > >>>>>>>>>>>> range search well. > > >>>>>>>>>>>> > > >>>>>>>>>>>> So what you do is index them for frequency of occurring in > the > > >>>>>>>> same set. > > >>>>>>>>>>>> In essence, you 'bucket them' statistically, and that > bucket id > > >>>>>>>> becomes a > > >>>>>>>>>>>> key prefix. A crypto hash value can be copied into more > than one > > >>>>>>>> bucket. > > >>>>>>>>>>>> The {bucket_id}/{cryptohash} becomes a {val_id} > > >>>>>>>>>>>> > > >>>>>>>>>>>> When writing a document, Couch submits the list/array of > > >>>>>>>> cryptohash values > > >>>>>>>>>>>> it computed to FDB and gets back the corresponding > {val_id} (the > > >>>>>>>> id with > > >>>>>>>>>>>> the bucket prefixed). This can get somewhat expensive if > there's > > >>>>>>>> always a > > >>>>>>>>>>>> lot of app local cache misses. > > >>>>>>>>>>>> > > >>>>>>>>>>>> > > >>>>>>>>>>>> A document's value is then a series of {val_id} arrays up > to 100k > > >>>>>>>> per > > >>>>>>>>>>>> segment. > > >>>>>>>>>>>> > > >>>>>>>>>>>> When retrieving a document, you get the val_ids, find the > distinct > > >>>>>>>> buckets > > >>>>>>>>>>>> and min/max entries for this doc, and then parallel query > each > > >>>>>>>> bucket while > > >>>>>>>>>>>> reconstructing the document. > > >>>>>>>>>>>> > > >>>>>>>>>>>> The values returned from the buckets query are the key/value > > >>>>>>>> strings > > >>>>>>>>>>>> required to reassemble this document. > > >>>>>>>>>>>> > > >>>>>>>>>>>> > > >>>>>>>>>>>> ---------- > > >>>>>>>>>>>> I put this forward primarily to hilite the idea that trying > to > > >>>>>>>> match the > > >>>>>>>>>>>> storage representation of documents in a straight forward > way to > > >>>>>>>> FDB keys > > >>>>>>>>>>>> to reduce query count might not be the most performance > oriented > > >>>>>>>> approach. > > >>>>>>>>>>>> > > >>>>>>>>>>>> I'd much prefer a storage approach that reduced data > duplication > > >>>>>>>> and > > >>>>>>>>>>>> enabled fast sub-document queries. > > >>>>>>>>>>>> > > >>>>>>>>>>>> > > >>>>>>>>>>>> This clearly falls in the realm of what people want the > 'use case' > > >>>>>>>> of Couch > > >>>>>>>>>>>> to be/become. By giving Couch more access to sub-document > > >>>>>>>> queries, I could > > >>>>>>>>>>>> eventually see queries as complicated as GraphQL submitted > to > > >>>>>>>> Couch and > > >>>>>>>>>>>> pulling back ad-hoc aggregated data across multiple > documents in a > > >>>>>>>> single > > >>>>>>>>>>>> application layer request. > > >>>>>>>>>>>> > > >>>>>>>>>>>> Hehe - one way to look at the database of Couch documents > is that > > >>>>>>>> they are > > >>>>>>>>>>>> all conflict revisions of the single root empty document. > What I > > >>>>>>>> mean be > > >>>>>>>>>>>> this is consider thinking of the entire document store as > one > > >>>>>>>> giant DAG of > > >>>>>>>>>>>> key/value pairs. How even separate documents are still > typically > > >>>>>>>> related to > > >>>>>>>>>>>> each other. For most applications there is a tremendous > amount of > > >>>>>>>> data > > >>>>>>>>>>>> redundancy between docs and especially between revisions of > those > > >>>>>>>> docs... > > >>>>>>>>>>>> > > >>>>>>>>>>>> > > >>>>>>>>>>>> > > >>>>>>>>>>>> And all this is a long way of saying "I think there could > be a lot > > >>>>>>>> of value > > >>>>>>>>>>>> in assuming documents are 'assembled' from multiple queries > to > > >>>>>>>> FDB, with > > >>>>>>>>>>>> local caching, instead of simply retrieved" > > >>>>>>>>>>>> > > >>>>>>>>>>>> Thanks, I hope I'm not the only outlier here thinking this > way!? > > >>>>>>>>>>>> > > >>>>>>>>>>>> Mike :-) > > >>>>>>>>>>>> > > >>>>>>>>>>> > > >>>>>>>> > > >>>>>> > > >>>> > > > > > >
