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