> 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 :-) > >>>>>>>>>>>> > >>>>>>>>>>> > >>>>>>>> > >>>>>> > >>>> > > >
