I've been thinking more about this and I think Aman's suggestion of Parquet files is worth a poc.
What we could do: Run a select * order by partCol1, partCol2, ... , partColN query against the existing large json partition file and create a new Parquet version of the file. Hand write a partition type read against the Parquet APIs using the filter APIs and see what performance looks like. Thoughts? -- Jacques Nadeau CTO and Co-Founder, Dremio On Fri, Oct 30, 2015 at 3:36 PM, Parth Chandra <[email protected]> wrote: > Thanks Steven for the link. > Your suggestion of storing only the single valued columns is a good one. > It might be OK to have some of the count* queries run a little slower as > reading the cache itself is taking way to long. I'm also looking at > squashing the column datatype info as there is a lot of redundancy there. > > > > > On Fri, Oct 30, 2015 at 3:22 PM, Steven Phillips <[email protected]> > wrote: > > > My view on storing it in some other format is that, yes, it will probably > > reduce the size of the file, but if we gzip the json file, it should be > > pretty compact. As for deserialization cost, other formats would be > faster, > > but not dramatically faster. Certainly not the order of magnitude faster > > that we really need it to be. The reason we chose JSON was because it is > > readable and easier to deal with. > > > > As for the old code, I can point you at a branch, but it's probably not > > very helpful. Unless we want to essentially disable value-based partition > > pruning when using the cache, the old code will not work. > > > > My recommendation would be to come up with a new version of the format > > which stores only the name and value of columns which are single-valued > for > > each file or row group. This will allow partition pruning to work, but > some > > count queries may not be as fast any more, because the cache won't have > > column value counts on a per-rowgroup basis any more. > > > > Anyway, here is the link to the original branch. > > > > https://github.com/StevenMPhillips/drill/tree/meta > > > > On Fri, Oct 30, 2015 at 3:01 PM, Parth Chandra <[email protected]> > wrote: > > > > > Hey Jacques, Steven, > > > > > > Do we have a branch somewhere which has the initial prototype code? > I'd > > > like to prune the file a bit as it looks like reducing the size of the > > > metadata cache file might yield the best results. > > > > > > Also, did we have a particular reason for going with JSON as opposed > > to a > > > more compact binary format? Are there any arguments against saving this > > as > > > a protobuf/BSON/Parquet file? > > > > > > Parth > > > > > > On Mon, Oct 26, 2015 at 2:42 PM, Jacques Nadeau <[email protected]> > > > wrote: > > > > > > > My first thought is we've gotten too generous in what we're storing > in > > > the > > > > Parquet metadata file. Early implementations were very lean and it > > seems > > > > far larger today. For example, early implementations didn't keep > > > statistics > > > > and ignored row groups (files, schema and block locations only). If > we > > > need > > > > multiple levels of information, we may want to stagger (or normalize) > > > them > > > > in the file. Also, we may think about what is the minimum that must > be > > > done > > > > in planning. We could do the file pruning at execution time rather > than > > > > single-tracking these things (makes stats harder though). > > > > > > > > I also think we should be cautious around jumping to a conclusion > until > > > > DRILL-3973 provides more insight. > > > > > > > > In terms of caching, I'd be more inclined to rely on file system > > caching > > > > and make sure serialization/deserialization is as efficient as > possible > > > as > > > > opposed to implementing an application-level cache. (We already have > > > enough > > > > problems managing memory without having to figure out when we should > > > drop a > > > > metadata cache :D). > > > > > > > > Aside, I always liked this post for entertainment and the thoughts on > > > > virtual memory: > https://www.varnish-cache.org/trac/wiki/ArchitectNotes > > > > > > > > > > > > -- > > > > Jacques Nadeau > > > > CTO and Co-Founder, Dremio > > > > > > > > On Mon, Oct 26, 2015 at 2:25 PM, Hanifi Gunes <[email protected]> > > > wrote: > > > > > > > > > One more thing, for workloads running queries over subsets of same > > > > parquet > > > > > files, we can consider maintaining an in-memory cache as well. > > Assuming > > > > > metadata memory footprint per file is low and parquet files are > > static, > > > > not > > > > > needing us to invalidate the cache often. > > > > > > > > > > H+ > > > > > > > > > > On Mon, Oct 26, 2015 at 2:10 PM, Hanifi Gunes <[email protected] > > > > > > wrote: > > > > > > > > > > > I am not familiar with the contents of metadata stored but if > > > > > > deserialization workload seems to be fitting to any of > > afterburner's > > > > > > claimed improvement points [1] It could well be worth trying > given > > > the > > > > > > claimed gain on throughput is substantial. > > > > > > > > > > > > It could also be a good idea to partition caching over a number > of > > > > files > > > > > > for better parallelization given number of cache files generated > is > > > > > > *significantly* less than number of parquet files. Maintaining > > global > > > > > > statistics seems an improvement point too. > > > > > > > > > > > > > > > > > > -H+ > > > > > > > > > > > > 1: > > > > > > > > > > > > > > > > > > > > > https://github.com/FasterXML/jackson-module-afterburner#what-is-optimized > > > > > > > > > > > > On Sun, Oct 25, 2015 at 9:33 AM, Aman Sinha < > [email protected]> > > > > > wrote: > > > > > > > > > > > >> Forgot to include the link for Jackson's AfterBurner module: > > > > > >> https://github.com/FasterXML/jackson-module-afterburner > > > > > >> > > > > > >> On Sun, Oct 25, 2015 at 9:28 AM, Aman Sinha < > [email protected] > > > > > > > > wrote: > > > > > >> > > > > > >> > I was going to file an enhancement JIRA but thought I will > > discuss > > > > > here > > > > > >> > first: > > > > > >> > > > > > > >> > The parquet metadata cache file is a JSON file that contains a > > > > subset > > > > > of > > > > > >> > the metadata extracted from the parquet files. The cache file > > can > > > > get > > > > > >> > really large .. a few GBs for a few hundred thousand files. > > > > > >> > I have filed a separate JIRA: DRILL-3973 for profiling the > > various > > > > > >> aspects > > > > > >> > of planning including metadata operations. In the meantime, > the > > > > > >> timestamps > > > > > >> > in the drillbit.log output indicate a large chunk of time > spent > > in > > > > > >> creating > > > > > >> > the drill table to begin with, which indicates bottleneck in > > > reading > > > > > the > > > > > >> > metadata. (I can provide performance numbers later once we > > > confirm > > > > > >> through > > > > > >> > profiling). > > > > > >> > > > > > > >> > A few thoughts around improvements: > > > > > >> > - The jackson deserialization of the JSON file is very slow.. > > can > > > > > this > > > > > >> be > > > > > >> > speeded up ? .. for instance the AfterBurner module of jackson > > > > claims > > > > > to > > > > > >> > improve performance by 30-40% by avoiding the use of > reflection. > > > > > >> > - The cache file read is a single threaded process. If we > were > > > > > >> directly > > > > > >> > reading from parquet files, we use a default of 16 threads. > > What > > > > can > > > > > be > > > > > >> > done to parallelize the read ? > > > > > >> > - Any operation that can be done one time during the REFRESH > > > > METADATA > > > > > >> > command ? for instance..examining the min/max values to > > determine > > > > > >> > single-value for partition column could be eliminated if we do > > > this > > > > > >> > computation during REFRESH METADATA command and store the > > summary > > > > one > > > > > >> time. > > > > > >> > > > > > > >> > - A pertinent question is: should the cache file be stored > in a > > > > more > > > > > >> > efficient format such as Parquet instead of JSON ? > > > > > >> > > > > > > >> > Aman > > > > > >> > > > > > > >> > > > > > > >> > > > > > > > > > > > > > > > > > > > > > > > > > > >
