At some point I changed the number of unique JSON paths and probably forgot to
update other conditions.
The ` - each document is around 10Kb` is not used in the calculations so can be
ignored.
On 2019/02/04 17:46:20, Adam Kocoloski <kocol...@apache.org> wrote:
> Ugh! We definitely cannot have a model where a 10K JSON document is exploded
> into 2MB worth of KV data. I’ve tried several times to follow the math here
> but I’m failing. I can’t even get past this first bit:
>
> > - 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
>
> If each document has 1000 paths, and each path (which leads to a unique
> scalar value, right?) has a value of 100 bytes associated with it … how is
> the document 10KB? Wouldn’t it need to be at least 100KB just by adding up
> all the scalar values?
>
> Adam
>
> > On Feb 4, 2019, at 6:08 AM, 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 :-)
> >>
>
>
