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Posted to dev@hbase.apache.org by Matt Corgan <mc...@hotpads.com> on 2011/06/03 00:28:30 UTC
prefix compression
I've been reading up on prefix compression for a few weeks trying to figure
out the best approach. Maybe some of you have noticed that 90% of my emails
to the user list have something to do with it... Now that people are
working on HFile v2 and the topic of Lucene's FST has come up, I thought it
would be a good time to share some thoughts. My goal here is definitely not
asking anyone to implement this, but just to start a discussion that gets
the proper jiras in place. I have spent a little time coding an initial
rowKey trie builder that could be the start of a pluggable implementation.
This is a big topic to address in one email, but I don't think the pieces
make sense on their own. Here's an outline with some thoughts:
* refer to prefix compression as "compaction" to avoid interfering with
traditional "compression"
* main goal is to reduce size of data in memory to increase effective
capacity of block index, block cache, and memstores
* disk access takes thousands of times longer than memory access,
especially over hdfs/network
* secondary goal is to speed up the processing of in memory data
* memory access takes 200+ cpu cycles, so must use cache friendly
algorithms
* structure trie node sizes around typical 64B cache line size
* investigate java's memory prefetching capabilities, possibly
increasing to ~256B nodes
* research
paper<http://www.google.com/url?sa=t&source=web&cd=5&sqi=2&ved=0CDcQFjAE&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.68.4872%26rep%3Drep1%26type%3Dpdf&rct=j&q=databases%20prefetching&ei=7tnnTcSWLuXTiALSpNCUDA&usg=AFQjCNFpVC9oDsG1-UG0x6ZsqsIHLqvGDA&sig2=o096l8cNrnX69oeBGChUKg>about
modern memory databases and prefetching
* almost always use variable length integer/long encoding
* need separate compacted structures/algorithms for rowKey, colQualifier,
and timestamp
* structures will need to interact with varInt pointers to link
rowKey<->colQualifier<->timestamp
* i think in the medium to long term (and maybe even short term) it would be
best to code this from scratch
Static row key compaction for block cache and data blocks:
* avoid schemes where each row is a diff of the previous (expensive cpu)
* utilize the fact that row keys are always sorted (unlike column
qualifiers)
* this makes for fast tree construction during flushing and compaction
* low hanging fruit: (rowKeyCompaction=simple)
* pull off the common prefix of all rows
* have a flag indicating if row key is repeat of the previous
* more aggressive: use modified Cache Sensitive Search
Trees<http://www.cs.columbia.edu/~library/TR-repository/reports/reports-1998/cucs-019-98.pdf>
(rowKeyCompaction=cssTree64,
cssTree256, etc)
* modified to accept full byte range 0-255, store full byte strings in
the tree, remove all pointers, optimize for cache line size, etc, but
similar idea
* CSS trees are considered sub-par because they are not easily modified,
but that is ok in hbase's block cache and data blocks
* input is a sorted list of byte[], output is a byte[] containing the entire
prefix trie
* possibly put all keys in the front of the block, and all values at the
end, with value offsets/lengths in the keys
Backwards compatibility
* block cache could quickly compute the trie when loaded, therefore working
with HFile v1
* eventually want to store the computed trie on disk
* HFile v2 will need to store information about the pluggable data block
compaction schemes
Memstore dynamic row key compaction:
* this will be much more difficult than the static compaction because it
needs to accept unsorted byte[]'s and support row locking
* use modified C-Burstsort<http://ww2.cs.mu.oz.au/~rsinha/papers/SinhaRingZobel-2006.pdf>
for
dynamic unsorted data
* fast and compact (best dynamic string sorting algorithm i could find)
* fragmentation friendly (not original goal, but ends up working like MSLAB)
* allocates large (customizable) byte[] for holding multiple leaf values
* "bursts" the byte[] when they fill up, creating multiple child arrays
* will need to add some sort of lockable object to the leaf of the rowKey
trie
Column qualifier compaction:
* config param colQualifierCompaction=(none, lookupTable, cssTree64, etc)
* use lookupTable when column qualifiers are a small bounded set
* store the lookup table in memory (similar to blockIndex or
fixedFileTrailer)
* on second thought, this could be difficult without 2-pass compaction,
but maybe still possible
* use cssTree when a large or unbounded set, like when appending a numerical
suffix in wide rows
Timestamp compaction:
* timestampCompaction=(none, hfileDiff, cssTree, flatten)
* each hfile needs a lowestTimestamp stored in the trailer
* hflieDiff would store a varLong relative to the lowestTimestamp
* cssTree would do the same as it does for rowKeys, but the trie overhead
might wipe out the space savings with such small values
* flatten would not store a timestamp in each record, rather use the hfile's
lowestTimestamp for every record
* many use cases don't care about the timestamp
* i think this works for maxVersions=1. may need more elaborate scheme
for multiple versions
I'll stop there... sorry for the huge email! I haven't seen much
discussion on email or jira about how prefix compression should be done, so
I hope this provides somewhat of a starting point.
Matt
Re: prefix compression
Posted by Stack <st...@duboce.net>.
On Fri, Jun 3, 2011 at 7:03 PM, Matt Corgan <mc...@hotpads.com> wrote:
>> Pluggable formats would help here so you could tune for mem vs cpu.
More history. At the time of KV and hfile incubation, we thought
about making these building blocks pluggable but it was thought that
there would be a performance cost doing the plug-in lookup so we
passed. Just FYI.
St.Ack
Re: prefix compression
Posted by Matt Corgan <mc...@hotpads.com>.
Thanks for the feedback Stack. Some inline responses:
On Thu, Jun 2, 2011 at 9:48 PM, Stack <st...@duboce.net> wrote:
> High-level this sounds like a great.
>
> Inline below is some feedback and a bit of history on how we got here
> in case it helps:
>
> On Thu, Jun 2, 2011 at 3:28 PM, Matt Corgan <mc...@hotpads.com> wrote:
> > * refer to prefix compression as "compaction" to avoid interfering with
> > traditional "compression"
> >
>
> 'Compaction' is already a term in hbase. Your suggestion below may
> reproduce an issue we currently have where the term 'split' can refer
> to the splitting of logs on regionserver recovery or spit of a region
> because its grown too large. Just a headsup.
>
> ha, yes, not sure how i overlooked that. Then maybe name the config
params: rowKeyFormat, colQualifierFormat, and timestampFormat
>
> > * main goal is to reduce size of data in memory to increase effective
> > capacity of block index, block cache, and memstores
>
>
> Ok. At some cost in CPU.
>
>
> > * disk access takes thousands of times longer than memory access,
> > especially over hdfs/network
> >
>
> Yes. I would underline the clause that starts 'especially' in the above.
>
>
> > * secondary goal is to speed up the processing of in memory data
> > * memory access takes 200+ cpu cycles, so must use cache friendly
> > algorithms
> > * structure trie node sizes around typical 64B cache line size
>
> How would you measure our effectiveness here?
>
The most important thing is to make the format pluggable, then we can run
HFile performance tests to find the optimal tuning for different
hardware/workloads.
The idea with the 64B is that if you're going to go to memory and get
64B (or 8 words) back, you might as well have designed your data structure
so that you can make use of all 64B. This requires that you structure the
prefix tries a little differently than older literature recommends.
>
>
> > * investigate java's memory prefetching capabilities, possibly
> > increasing to ~256B nodes
>
> Do you have a pointer on this phenomeon (I'm ignorant and would learn
> more)?
>
>
Paying attention to pre-fetching would be a super-optimization, but it is a
very real thing that can buy you another ~20% performance. 64B is the
normal memory fetch size right now, but sometimes the hardware will bring
back even larger chunks. Some languages offer specific commands to
prefetch... java doesn't, but the compiler is adding them behind the scenes
so it could possibly be coerced. The best reading on the topic is probably
the paper i mentioned earlier
(link<http://www.google.com/url?sa=t&source=web&cd=5&sqi=2&ved=0CDcQFjAE&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.68.4872%26rep%3Drep1%26type%3Dpdf&rct=j&q=databases%20prefetching&ei=7tnnTcSWLuXTiALSpNCUDA&usg=AFQjCNFpVC9oDsG1-UG0x6ZsqsIHLqvGDA&sig2=o096l8cNrnX69oeBGChUKg>),
and then its references if you're still interested.
>
> > * almost always use variable length integer/long encoding
> >
>
> In my limited experience I've found these to be CPU intensive making
> the parse of a compound byte array of rowkey+family+ etc. such as KV
> is now complicated.
>
> On other hand, our Benoit reports having made significant improvements
> over the hadoop varlength I was using.
>
> Pluggable formats would help here so you could tune for mem vs cpu. My
thought is that most hbase vints would be very short (1-2 byte internal
array offsets) which are faster to decode, but more importantly, save a lot
of memory. This topic is covered a lot in discussions of inverted index
posting list compression. Not perfectly relevant, but here's a good
paper<http://www2008.org/papers/pdf/p387-zhangA.pdf>about compressing
integers/longs. I have no data to back it up, but I feel
like if search engines are primarily storing lists of longs and they decide
it's best to compress them, then that's probably the best method for other
pieces of software as well.
> > * need separate compacted structures/algorithms for rowKey, colQualifier,
> > and timestamp
>
> Why you think that Matt? Wouldn't rowKey and say colQualifier share
> an algorithm because both are arbitrary byte arrays? Or, are you
> talking about context; the fact that the previous KV may have same row
> and family say? (We've talked of using a code for column family
> keeping a table of codes to family name...).
>
All three parts of the KV could share the same trie algorithm for
compressing arbitrary byte arrays. However, I think sometimes you can
compress colQualifiers even further (and faster) with a lookup table. And
timestamps even further with a delta scheme or by completely omitting them.
>
> > * structures will need to interact with varInt pointers to link
> > rowKey<->colQualifier<->timestamp
> >
> > * i think in the medium to long term (and maybe even short term) it would
> be
> > best to code this from scratch
> >
>
> I'd suggest some code sketches apart from hbase would be cool
> illustrating what you are thinking.
>
Agreed - i'm working on some simple code examples in my free time.
Hopefully can post in a couple weeks.
> KV is pervasive; from client API all the ways out to entries in HFile.
> Changing it is going to be a PITA.
>
> >
> > Static row key compaction for block cache and data blocks:
> > * avoid schemes where each row is a diff of the previous (expensive cpu)
>
> Yes. I think this is likely the case.
>
>
> > * utilize the fact that row keys are always sorted (unlike column
> > qualifiers)
>
> Column qualifiers are also sorted.
>
> Col qualifiers are sorted within a row, but the unit of compression is the
data block. If you take a data block and split it into 3 parallel lists of
equal length: a list of rowKeys, a list of colQualifiers, and a list of
timestamps, the rowKeys will be automatically be sorted, but the other two
will not be (unless the whole block is contained in one row). Does that
make sense?
> > * this makes for fast tree construction during flushing and compaction
>
> Because you are filling the tree w/ sorted data?
> > * low hanging fruit: (rowKeyCompaction=simple)
> > * pull off the common prefix of all rows
>
> In a block context? As the context's change, would we have a
> different means of doing this (Examples of contexts: memstore, block
> cache, hfile).
>
For memstore and block index, you take the common prefix of the first and
last rowKey. For data block, take the common prefix of first and last key.
As tables get really large, they are more and more likely to have longer
common prefixes.
>
>
> > * modified to accept full byte range 0-255, store full byte strings in
> > the tree, remove all pointers, optimize for cache line size, etc, but
> > similar idea
>
>
> How well can this be done in a language such as java? Does its
> indirection make it so its not possible to be cache line size
> consious?
>
I think it's the hardware that determines the cache line size and does some
of the prefetching, so java is directly affected by those. Unfortunately,
I'm not sure it's possible to align the start of an array on a cache line in
java like it is in C/C++, however, on average you can make use of half the
cache line... and there's always hope for the future.
>
>
> > * CSS trees are considered sub-par because they are not easily
> modified,
> > but that is ok in hbase's block cache and data blocks
>
> And for the memstore? Do you have an idea for what kind of structure
> we could use here, one that exploits 'compressed' values?
>
> > * input is a sorted list of byte[], output is a byte[] containing the
> entire
> > prefix trie
> > * possibly put all keys in the front of the block, and all values at the
> > end, with value offsets/lengths in the keys
> >
>
> How do you think this would improve things?
>
Not sure it's always best to do this, but the idea is to not pollute the
cache with value data unless it's actually needed. Think of the case of a
scanner with a high block cache hit ratio but returning sparse results.
This may be an improvement on the case where prefix compression isn't
needed to begin with. Another case for pluggable formats and testing...
>
>
> > Backwards compatibility
> > * block cache could quickly compute the trie when loaded, therefore
> working
> > with HFile v1
>
>
> I don't think we need to be backward compatible. We just need to be
> able to migrate from old to the new (in fact, I'd say do not be
> backward compatible -- having to do so in my experience makes the code
> more complex and inevitable compromises an implementation)
>
Agree about backwards compatibility being a bad idea (especially in a
pre-1.0 product). I guess I meant "current compatibility", like it could be
included in 0.90.4 since it doesn't require changes in serialization or file
formats. Not pushing for that, just noting...
> > * eventually want to store the computed trie on disk
> > * HFile v2 will need to store information about the pluggable data block
> > compaction schemes
> >
>
> Yes.
>
> As has been said v2 has a hierarchical index. That might be hard to
> make pluggable? What do you think?
>
> It's not as important for the block index to be pluggable as it is for the
data blocks or memstore. It could probably be done by having another
columnFamily setting "blockIndexFormat".
> >
> > Memstore dynamic row key compaction:
> > * this will be much more difficult than the static compaction because it
> > needs to accept unsorted byte[]'s and support row locking
>
> Row 'locking' is done using MVCC -- a sequence/edit number is added to
> KVs in-memory only and only edits with a sequence/edit number older
> than a current moving point are viewable. This mechanism or one like
> it could be used going forward/
>
> > * use modified C-Burstsort<
> http://ww2.cs.mu.oz.au/~rsinha/papers/SinhaRingZobel-2006.pdf>
> > for
> > dynamic unsorted data
> > * fast and compact (best dynamic string sorting algorithm i could find)
> > * fragmentation friendly (not original goal, but ends up working like
> MSLAB)
> > * allocates large (customizable) byte[] for holding multiple leaf
> values
> > * "bursts" the byte[] when they fill up, creating multiple child
> arrays
> > * will need to add some sort of lockable object to the leaf of the rowKey
> > trie
> >
>
> Do we need too?
>
Oops, guess not. But then we'll need some place to store the version of
each KV since each KV is not a standalone thing. I think this isn't a big
deal... disregard that comment.
>
> (Thanks for the paper pointers)
>
>
> >
> > Column qualifier compaction:
> > * config param colQualifierCompaction=(none, lookupTable, cssTree64, etc)
> > * use lookupTable when column qualifiers are a small bounded set
> > * store the lookup table in memory (similar to blockIndex or
> > fixedFileTrailer)
> > * on second thought, this could be difficult without 2-pass
> compaction,
> > but maybe still possible
> > * use cssTree when a large or unbounded set, like when appending a
> numerical
> > suffix in wide rows
> >
> >
> > Timestamp compaction:
> > * timestampCompaction=(none, hfileDiff, cssTree, flatten)
> > * each hfile needs a lowestTimestamp stored in the trailer
> > * hflieDiff would store a varLong relative to the lowestTimestamp
> > * cssTree would do the same as it does for rowKeys, but the trie overhead
> > might wipe out the space savings with such small values
>
> Yes. Should we be doing 64bit timestamps? Then it might not?
>
Not sure what you mean here? Maybe that same trie structure would be fine.
It does require a few extra bytes per entry though, so even if you compress
to 2 bytes, you're creeping back up towards the original 8 bytes.
>
> > * flatten would not store a timestamp in each record, rather use the
> hfile's
> > lowestTimestamp for every record
> > * many use cases don't care about the timestamp
> > * i think this works for maxVersions=1. may need more elaborate
> scheme
> > for multiple versions
> >
> >
> > I'll stop there... sorry for the huge email! I haven't seen much
> > discussion on email or jira about how prefix compression should be done,
> so
> > I hope this provides somewhat of a starting point.
> >
>
> Thanks for the provocative mail Matt.
>
> We arrived at KV because previously we had an Object of key, value and
> timestamp which we were copying multiple times on the way in and then
> again on the way. KV was an attempt at cutting down on the copies and
> object creation. At the time we thought byte arrays raw and fast. We
> looked at varint'ing it but savings didn't seem worth the bother or
> the extra CPU and it make parse of the KV byte array harder.
St.Ack
>
Re: prefix compression
Posted by Stack <st...@duboce.net>.
High-level this sounds like a great.
Inline below is some feedback and a bit of history on how we got here
in case it helps:
On Thu, Jun 2, 2011 at 3:28 PM, Matt Corgan <mc...@hotpads.com> wrote:
> * refer to prefix compression as "compaction" to avoid interfering with
> traditional "compression"
>
'Compaction' is already a term in hbase. Your suggestion below may
reproduce an issue we currently have where the term 'split' can refer
to the splitting of logs on regionserver recovery or spit of a region
because its grown too large. Just a headsup.
> * main goal is to reduce size of data in memory to increase effective
> capacity of block index, block cache, and memstores
Ok. At some cost in CPU.
> * disk access takes thousands of times longer than memory access,
> especially over hdfs/network
>
Yes. I would underline the clause that starts 'especially' in the above.
> * secondary goal is to speed up the processing of in memory data
> * memory access takes 200+ cpu cycles, so must use cache friendly
> algorithms
> * structure trie node sizes around typical 64B cache line size
How would you measure our effectiveness here?
> * investigate java's memory prefetching capabilities, possibly
> increasing to ~256B nodes
Do you have a pointer on this phenomeon (I'm ignorant and would learn more)?
> * almost always use variable length integer/long encoding
>
In my limited experience I've found these to be CPU intensive making
the parse of a compound byte array of rowkey+family+ etc. such as KV
is now complicated.
On other hand, our Benoit reports having made significant improvements
over the hadoop varlength I was using.
> * need separate compacted structures/algorithms for rowKey, colQualifier,
> and timestamp
Why you think that Matt? Wouldn't rowKey and say colQualifier share
an algorithm because both are arbitrary byte arrays? Or, are you
talking about context; the fact that the previous KV may have same row
and family say? (We've talked of using a code for column family
keeping a table of codes to family name...).
> * structures will need to interact with varInt pointers to link
> rowKey<->colQualifier<->timestamp
>
> * i think in the medium to long term (and maybe even short term) it would be
> best to code this from scratch
>
I'd suggest some code sketches apart from hbase would be cool
illustrating what you are thinking.
KV is pervasive; from client API all the ways out to entries in HFile.
Changing it is going to be a PITA.
>
> Static row key compaction for block cache and data blocks:
> * avoid schemes where each row is a diff of the previous (expensive cpu)
Yes. I think this is likely the case.
> * utilize the fact that row keys are always sorted (unlike column
> qualifiers)
Column qualifiers are also sorted.
> * this makes for fast tree construction during flushing and compaction
Because you are filling the tree w/ sorted data?
> * low hanging fruit: (rowKeyCompaction=simple)
> * pull off the common prefix of all rows
In a block context? As the context's change, would we have a
different means of doing this (Examples of contexts: memstore, block
cache, hfile).
> * modified to accept full byte range 0-255, store full byte strings in
> the tree, remove all pointers, optimize for cache line size, etc, but
> similar idea
How well can this be done in a language such as java? Does its
indirection make it so its not possible to be cache line size
consious?
> * CSS trees are considered sub-par because they are not easily modified,
> but that is ok in hbase's block cache and data blocks
And for the memstore? Do you have an idea for what kind of structure
we could use here, one that exploits 'compressed' values?
> * input is a sorted list of byte[], output is a byte[] containing the entire
> prefix trie
> * possibly put all keys in the front of the block, and all values at the
> end, with value offsets/lengths in the keys
>
How do you think this would improve things?
> Backwards compatibility
> * block cache could quickly compute the trie when loaded, therefore working
> with HFile v1
I don't think we need to be backward compatible. We just need to be
able to migrate from old to the new (in fact, I'd say do not be
backward compatible -- having to do so in my experience makes the code
more complex and inevitable compromises an implementation)
> * eventually want to store the computed trie on disk
> * HFile v2 will need to store information about the pluggable data block
> compaction schemes
>
Yes.
As has been said v2 has a hierarchical index. That might be hard to
make pluggable? What do you think?
>
> Memstore dynamic row key compaction:
> * this will be much more difficult than the static compaction because it
> needs to accept unsorted byte[]'s and support row locking
Row 'locking' is done using MVCC -- a sequence/edit number is added to
KVs in-memory only and only edits with a sequence/edit number older
than a current moving point are viewable. This mechanism or one like
it could be used going forward/
> * use modified C-Burstsort<http://ww2.cs.mu.oz.au/~rsinha/papers/SinhaRingZobel-2006.pdf>
> for
> dynamic unsorted data
> * fast and compact (best dynamic string sorting algorithm i could find)
> * fragmentation friendly (not original goal, but ends up working like MSLAB)
> * allocates large (customizable) byte[] for holding multiple leaf values
> * "bursts" the byte[] when they fill up, creating multiple child arrays
> * will need to add some sort of lockable object to the leaf of the rowKey
> trie
>
Do we need too?
(Thanks for the paper pointers)
>
> Column qualifier compaction:
> * config param colQualifierCompaction=(none, lookupTable, cssTree64, etc)
> * use lookupTable when column qualifiers are a small bounded set
> * store the lookup table in memory (similar to blockIndex or
> fixedFileTrailer)
> * on second thought, this could be difficult without 2-pass compaction,
> but maybe still possible
> * use cssTree when a large or unbounded set, like when appending a numerical
> suffix in wide rows
>
>
> Timestamp compaction:
> * timestampCompaction=(none, hfileDiff, cssTree, flatten)
> * each hfile needs a lowestTimestamp stored in the trailer
> * hflieDiff would store a varLong relative to the lowestTimestamp
> * cssTree would do the same as it does for rowKeys, but the trie overhead
> might wipe out the space savings with such small values
Yes. Should we be doing 64bit timestamps? Then it might not?
> * flatten would not store a timestamp in each record, rather use the hfile's
> lowestTimestamp for every record
> * many use cases don't care about the timestamp
> * i think this works for maxVersions=1. may need more elaborate scheme
> for multiple versions
>
>
> I'll stop there... sorry for the huge email! I haven't seen much
> discussion on email or jira about how prefix compression should be done, so
> I hope this provides somewhat of a starting point.
>
Thanks for the provocative mail Matt.
We arrived at KV because previously we had an Object of key, value and
timestamp which we were copying multiple times on the way in and then
again on the way. KV was an attempt at cutting down on the copies and
object creation. At the time we thought byte arrays raw and fast. We
looked at varint'ing it but savings didn't seem worth the bother or
the extra CPU and it make parse of the KV byte array harder.
St.Ack
Re: prefix compression
Posted by Jason Rutherglen <ja...@gmail.com>.
> Memstore dynamic row key compaction
This is interesting though I think extremely difficult. We need this
for Lucene realtime search with the RAM terms dictionary, which is
slated to use a ConcurrentSkipListMap, eg it's lock free and it works.
As Todd mentions, the KeyValue's need for access to the underlying
byte[] does create some difficulties in regards to how rowids'd work.
I think we could start with making the block index more efficient.
And prefix compression would be a big win. Is there a Jira for that?
Sounds like we need to make the HFile pluggable first regardless?
On Thu, Jun 2, 2011 at 3:28 PM, Matt Corgan <mc...@hotpads.com> wrote:
> I've been reading up on prefix compression for a few weeks trying to figure
> out the best approach. Maybe some of you have noticed that 90% of my emails
> to the user list have something to do with it... Now that people are
> working on HFile v2 and the topic of Lucene's FST has come up, I thought it
> would be a good time to share some thoughts. My goal here is definitely not
> asking anyone to implement this, but just to start a discussion that gets
> the proper jiras in place. I have spent a little time coding an initial
> rowKey trie builder that could be the start of a pluggable implementation.
>
> This is a big topic to address in one email, but I don't think the pieces
> make sense on their own. Here's an outline with some thoughts:
>
> * refer to prefix compression as "compaction" to avoid interfering with
> traditional "compression"
>
> * main goal is to reduce size of data in memory to increase effective
> capacity of block index, block cache, and memstores
> * disk access takes thousands of times longer than memory access,
> especially over hdfs/network
>
> * secondary goal is to speed up the processing of in memory data
> * memory access takes 200+ cpu cycles, so must use cache friendly
> algorithms
> * structure trie node sizes around typical 64B cache line size
> * investigate java's memory prefetching capabilities, possibly
> increasing to ~256B nodes
> * research
> paper<http://www.google.com/url?sa=t&source=web&cd=5&sqi=2&ved=0CDcQFjAE&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.68.4872%26rep%3Drep1%26type%3Dpdf&rct=j&q=databases%20prefetching&ei=7tnnTcSWLuXTiALSpNCUDA&usg=AFQjCNFpVC9oDsG1-UG0x6ZsqsIHLqvGDA&sig2=o096l8cNrnX69oeBGChUKg>about
> modern memory databases and prefetching
> * almost always use variable length integer/long encoding
>
> * need separate compacted structures/algorithms for rowKey, colQualifier,
> and timestamp
> * structures will need to interact with varInt pointers to link
> rowKey<->colQualifier<->timestamp
>
> * i think in the medium to long term (and maybe even short term) it would be
> best to code this from scratch
>
>
> Static row key compaction for block cache and data blocks:
> * avoid schemes where each row is a diff of the previous (expensive cpu)
> * utilize the fact that row keys are always sorted (unlike column
> qualifiers)
> * this makes for fast tree construction during flushing and compaction
> * low hanging fruit: (rowKeyCompaction=simple)
> * pull off the common prefix of all rows
> * have a flag indicating if row key is repeat of the previous
> * more aggressive: use modified Cache Sensitive Search
> Trees<http://www.cs.columbia.edu/~library/TR-repository/reports/reports-1998/cucs-019-98.pdf>
> (rowKeyCompaction=cssTree64,
> cssTree256, etc)
> * modified to accept full byte range 0-255, store full byte strings in
> the tree, remove all pointers, optimize for cache line size, etc, but
> similar idea
> * CSS trees are considered sub-par because they are not easily modified,
> but that is ok in hbase's block cache and data blocks
> * input is a sorted list of byte[], output is a byte[] containing the entire
> prefix trie
> * possibly put all keys in the front of the block, and all values at the
> end, with value offsets/lengths in the keys
>
> Backwards compatibility
> * block cache could quickly compute the trie when loaded, therefore working
> with HFile v1
> * eventually want to store the computed trie on disk
> * HFile v2 will need to store information about the pluggable data block
> compaction schemes
>
>
> Memstore dynamic row key compaction:
> * this will be much more difficult than the static compaction because it
> needs to accept unsorted byte[]'s and support row locking
> * use modified C-Burstsort<http://ww2.cs.mu.oz.au/~rsinha/papers/SinhaRingZobel-2006.pdf>
> for
> dynamic unsorted data
> * fast and compact (best dynamic string sorting algorithm i could find)
> * fragmentation friendly (not original goal, but ends up working like MSLAB)
> * allocates large (customizable) byte[] for holding multiple leaf values
> * "bursts" the byte[] when they fill up, creating multiple child arrays
> * will need to add some sort of lockable object to the leaf of the rowKey
> trie
>
>
> Column qualifier compaction:
> * config param colQualifierCompaction=(none, lookupTable, cssTree64, etc)
> * use lookupTable when column qualifiers are a small bounded set
> * store the lookup table in memory (similar to blockIndex or
> fixedFileTrailer)
> * on second thought, this could be difficult without 2-pass compaction,
> but maybe still possible
> * use cssTree when a large or unbounded set, like when appending a numerical
> suffix in wide rows
>
>
> Timestamp compaction:
> * timestampCompaction=(none, hfileDiff, cssTree, flatten)
> * each hfile needs a lowestTimestamp stored in the trailer
> * hflieDiff would store a varLong relative to the lowestTimestamp
> * cssTree would do the same as it does for rowKeys, but the trie overhead
> might wipe out the space savings with such small values
> * flatten would not store a timestamp in each record, rather use the hfile's
> lowestTimestamp for every record
> * many use cases don't care about the timestamp
> * i think this works for maxVersions=1. may need more elaborate scheme
> for multiple versions
>
>
> I'll stop there... sorry for the huge email! I haven't seen much
> discussion on email or jira about how prefix compression should be done, so
> I hope this provides somewhat of a starting point.
>
> Matt
>
Re: prefix compression
Posted by Jason Rutherglen <ja...@gmail.com>.
> i agree about starting with the block index since it's a relatively
> isolated piece of code. Could possibly be done without KeyValue
> modifications
Right. I don't think any KeyValue changes will be required. I'm
trying to get more info about if we can MMap the FST, then store all
the keys into it on disk/HDFS. This could completely and insanely
reduce the cost of rowids both in terms of loading them into heap,
heap usage, and the overall size of HFiles in general. Also it would
enable the user to choose keys of a much larger size and not pay a
penalty.
On Thu, Jun 2, 2011 at 8:17 PM, Matt Corgan <mc...@hotpads.com> wrote:
> What about turning KeyValue into an interface with only the essential
> getXX() methods? Whatever is using the KeyValue shouldn't *have* to know
> what sort of structure it's backed by, but could figure out the
> implementation in cases where it wants to optimize performance. Seems like
> this is warranted right now just for the sake of breaking up such a huge
> class... Possible implementations include:
>
> new SimpleKeyValue(final byte[] row, final byte[] family,
> final byte[] qualifier, final long timestamp, Type type,
> final byte[] value) (separate fields, low performance, useful for
> debugging?)
>
> new ArrayKeyValue(byte[] bytes)
>
> new SubArrayKeyValue(byte[] bytes, int offset, int length)
>
> new BlockIndexKeyValue(BlockIndex blockIndex, int kvPosition)
>
> new HFileKeyValue(HFileScanner scanner)
>
> etc... where each implementation knows how to fish out the underlying data.
> Later we add more implementations:
>
> new CssTreeBlockIndexKeyValue(CssTreeBlockIndex blockIndex, int kvPosition)
>
>
> @Jason - i agree about starting with the block index since it's a relatively
> isolated piece of code. Could possibly be done without KeyValue
> modifications. Also, i think it could work without changing the HFile if
> people are willing to let it compact the index when the HFile is read rather
> than when the when it's written. Would make for slightly slower startup
> times but would save a lot of memory. Could be configurable.
>
>
> On Thu, Jun 2, 2011 at 4:44 PM, Jonathan Gray <jg...@fb.com> wrote:
>
>> I'm here!
>>
>> Still parsing through the stuff in this e-mail but I agree that many
>> approaches will require rejiggering of KeyValue in a significant way. I
>> have made some attempts at this but nothing is very far. It's definitely
>> something that we are hoping to put some resources into over the summer.
>>
>> JG
>>
>> > -----Original Message-----
>> > From: Todd Lipcon [mailto:todd@cloudera.com]
>> > Sent: Thursday, June 02, 2011 3:37 PM
>> > To: dev@hbase.apache.org
>> > Subject: Re: prefix compression
>> >
>> > Hey Matt,
>> >
>> > Interesting email, and also something I've been thinking about recently.
>> >
>> > Unfortunately, I think one of the big prerequisites before we can start
>> > thinking about actual compression algorithms is some refactoring around
>> > how KeyValue is used. Currently, KeyValue exposes byte arrays in a lot of
>> > places
>> > - these byte arrays are expected to have discrete sub-ranges that
>> represent
>> > row key, column qualifier, etc. Various parts of the code directly grab
>> these
>> > byte arrays and offsets and do stuff with them (eg comparison,
>> serialization,
>> > etc).
>> >
>> > In order to split the different components up, or do true prefix
>> compression,
>> > we'd need to be able to have KeyValue do some smarter things for
>> > comparison, and never call functions like "getBuffer), getRowKeyOffset(),
>> > getRowKeyLength()" expecting that to show us a byte array range with a
>> row
>> > key in it.
>> >
>> > I think Jonathan Gray has some code hacked together for this, but last I
>> > heard it wasn't in shippable state... Jonathan, you out there?
>> >
>> > As for the particular compression algorithms, a nice first step would be
>> a very
>> > simplistic one: the first row key of any HFile block would be given in
>> full.
>> > Following that, each key uses a single byte token which identifies the
>> > number of key components shared with the previous row (ie same row,
>> > same col, different ts). This isn't quite as good as true prefix
>> compression,
>> > but for common cases where each cell has three versions, and each row has
>> > many columns, it's probably a huge savings.
>> >
>> > -Todd
>> >
>> > On Thu, Jun 2, 2011 at 3:28 PM, Matt Corgan <mc...@hotpads.com>
>> > wrote:
>> >
>> > > I've been reading up on prefix compression for a few weeks trying to
>> > > figure out the best approach. Maybe some of you have noticed that 90%
>> > > of my emails to the user list have something to do with it... Now
>> > > that people are working on HFile v2 and the topic of Lucene's FST has
>> > > come up, I thought it would be a good time to share some thoughts. My
>> > > goal here is definitely not asking anyone to implement this, but just
>> > > to start a discussion that gets the proper jiras in place. I have
>> > > spent a little time coding an initial rowKey trie builder that could
>> > > be the start of a pluggable implementation.
>> > >
>> > > This is a big topic to address in one email, but I don't think the
>> > > pieces make sense on their own. Here's an outline with some thoughts:
>> > >
>> > > * refer to prefix compression as "compaction" to avoid interfering
>> > > with traditional "compression"
>> > >
>> > > * main goal is to reduce size of data in memory to increase effective
>> > > capacity of block index, block cache, and memstores
>> > > * disk access takes thousands of times longer than memory access,
>> > > especially over hdfs/network
>> > >
>> > > * secondary goal is to speed up the processing of in memory data
>> > > * memory access takes 200+ cpu cycles, so must use cache friendly
>> > > algorithms
>> > > * structure trie node sizes around typical 64B cache line size
>> > > * investigate java's memory prefetching capabilities, possibly
>> > > increasing to ~256B nodes
>> > > * research
>> > > paper<
>> > >
>> > http://www.google.com/url?sa=t&source=web&cd=5&sqi=2&ved=0CDcQFj
>> > AE&url
>> > >
>> > =http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3
>> > D10.1.
>> > >
>> > 1.68.4872%26rep%3Drep1%26type%3Dpdf&rct=j&q=databases%20prefetchi
>> > ng&ei
>> > > =7tnnTcSWLuXTiALSpNCUDA&usg=AFQjCNFpVC9oDsG1-
>> > UG0x6ZsqsIHLqvGDA&sig2=o0
>> > > 96l8cNrnX69oeBGChUKg
>> > > >about
>> > > modern memory databases and prefetching
>> > > * almost always use variable length integer/long encoding
>> > >
>> > > * need separate compacted structures/algorithms for rowKey,
>> > > colQualifier, and timestamp
>> > > * structures will need to interact with varInt pointers to link
>> > > rowKey<->colQualifier<->timestamp
>> > >
>> > > * i think in the medium to long term (and maybe even short term) it
>> > > would be best to code this from scratch
>> > >
>> > >
>> > > Static row key compaction for block cache and data blocks:
>> > > * avoid schemes where each row is a diff of the previous (expensive
>> > > cpu)
>> > > * utilize the fact that row keys are always sorted (unlike column
>> > > qualifiers)
>> > > * this makes for fast tree construction during flushing and
>> > > compaction
>> > > * low hanging fruit: (rowKeyCompaction=simple)
>> > > * pull off the common prefix of all rows
>> > > * have a flag indicating if row key is repeat of the previous
>> > > * more aggressive: use modified Cache Sensitive Search Trees<
>> > > http://www.cs.columbia.edu/~library/TR-repository/reports/reports-1998
>> > > /cucs-019-98.pdf
>> > > >
>> > > (rowKeyCompaction=cssTree64,
>> > > cssTree256, etc)
>> > > * modified to accept full byte range 0-255, store full byte strings
>> > > in the tree, remove all pointers, optimize for cache line size, etc,
>> > > but similar idea
>> > > * CSS trees are considered sub-par because they are not easily
>> > > modified, but that is ok in hbase's block cache and data blocks
>> > > * input is a sorted list of byte[], output is a byte[] containing the
>> > > entire prefix trie
>> > > * possibly put all keys in the front of the block, and all values at
>> > > the end, with value offsets/lengths in the keys
>> > >
>> > > Backwards compatibility
>> > > * block cache could quickly compute the trie when loaded, therefore
>> > > working with HFile v1
>> > > * eventually want to store the computed trie on disk
>> > > * HFile v2 will need to store information about the pluggable data
>> > > block compaction schemes
>> > >
>> > >
>> > > Memstore dynamic row key compaction:
>> > > * this will be much more difficult than the static compaction because
>> > > it needs to accept unsorted byte[]'s and support row locking
>> > > * use modified C-Burstsort<
>> > > http://ww2.cs.mu.oz.au/~rsinha/papers/SinhaRingZobel-2006.pdf>
>> > > for
>> > > dynamic unsorted data
>> > > * fast and compact (best dynamic string sorting algorithm i could
>> > > find)
>> > > * fragmentation friendly (not original goal, but ends up working like
>> > > MSLAB)
>> > > * allocates large (customizable) byte[] for holding multiple leaf
>> values
>> > > * "bursts" the byte[] when they fill up, creating multiple child
>> > > arrays
>> > > * will need to add some sort of lockable object to the leaf of the
>> > > rowKey trie
>> > >
>> > >
>> > > Column qualifier compaction:
>> > > * config param colQualifierCompaction=(none, lookupTable, cssTree64,
>> > > etc)
>> > > * use lookupTable when column qualifiers are a small bounded set
>> > > * store the lookup table in memory (similar to blockIndex or
>> > > fixedFileTrailer)
>> > > * on second thought, this could be difficult without 2-pass
>> > > compaction, but maybe still possible
>> > > * use cssTree when a large or unbounded set, like when appending a
>> > > numerical suffix in wide rows
>> > >
>> > >
>> > > Timestamp compaction:
>> > > * timestampCompaction=(none, hfileDiff, cssTree, flatten)
>> > > * each hfile needs a lowestTimestamp stored in the trailer
>> > > * hflieDiff would store a varLong relative to the lowestTimestamp
>> > > * cssTree would do the same as it does for rowKeys, but the trie
>> > > overhead might wipe out the space savings with such small values
>> > > * flatten would not store a timestamp in each record, rather use the
>> > > hfile's lowestTimestamp for every record
>> > > * many use cases don't care about the timestamp
>> > > * i think this works for maxVersions=1. may need more elaborate
>> > > scheme for multiple versions
>> > >
>> > >
>> > > I'll stop there... sorry for the huge email! I haven't seen much
>> > > discussion on email or jira about how prefix compression should be
>> > > done, so I hope this provides somewhat of a starting point.
>> > >
>> > > Matt
>> > >
>> >
>> >
>> >
>> > --
>> > Todd Lipcon
>> > Software Engineer, Cloudera
>>
>
Re: prefix compression
Posted by Todd Lipcon <to...@cloudera.com>.
On Thu, Jun 2, 2011 at 9:51 PM, Stack <st...@duboce.net> wrote:
> On Thu, Jun 2, 2011 at 8:17 PM, Matt Corgan <mc...@hotpads.com> wrote:
> > What about turning KeyValue into an interface with only the essential
> > getXX() methods? Whatever is using the KeyValue shouldn't *have* to know
> > what sort of structure it's backed by, but could figure out the
> > implementation in cases where it wants to optimize performance.
>
> A few of us have looked at doing this and baulked. Maybe if we'd had
> a stiff drink first....
>
I gave it a go couple months ago on a lazy Saturday. Spent about 2 hours,
came out with a huge mess and an HBase that didn't compile.
Best of luck to you if you choose to fight this dragon :)
We _could_ attempt to do it somewhat incrementally - eg first deprecate
"getBuffer" and then slowly remove all the calls. I imagine 95% of the calls
fall into the category of a) comparison or b) serialization onto wire/file.
We can replace A with calls into KVComparator, and B with calls to a new
"write(Column|TS|RowKey|...)" call. If we can get that down to 0, then
"getBuffer" becomes private only accessible to KVComparator, and we move
onto the next call that eludes interfacedom.
...
> > new CssTreeBlockIndexKeyValue(CssTreeBlockIndex blockIndex, int
> kvPosition)
> >
>
> Sounds great.
>
> St.Ack
>
--
Todd Lipcon
Software Engineer, Cloudera
Re: prefix compression
Posted by Stack <st...@duboce.net>.
On Thu, Jun 2, 2011 at 8:17 PM, Matt Corgan <mc...@hotpads.com> wrote:
> What about turning KeyValue into an interface with only the essential
> getXX() methods? Whatever is using the KeyValue shouldn't *have* to know
> what sort of structure it's backed by, but could figure out the
> implementation in cases where it wants to optimize performance.
A few of us have looked at doing this and baulked. Maybe if we'd had
a stiff drink first....
...
> new CssTreeBlockIndexKeyValue(CssTreeBlockIndex blockIndex, int kvPosition)
>
Sounds great.
St.Ack
Re: prefix compression
Posted by Matt Corgan <mc...@hotpads.com>.
What about turning KeyValue into an interface with only the essential
getXX() methods? Whatever is using the KeyValue shouldn't *have* to know
what sort of structure it's backed by, but could figure out the
implementation in cases where it wants to optimize performance. Seems like
this is warranted right now just for the sake of breaking up such a huge
class... Possible implementations include:
new SimpleKeyValue(final byte[] row, final byte[] family,
final byte[] qualifier, final long timestamp, Type type,
final byte[] value) (separate fields, low performance, useful for
debugging?)
new ArrayKeyValue(byte[] bytes)
new SubArrayKeyValue(byte[] bytes, int offset, int length)
new BlockIndexKeyValue(BlockIndex blockIndex, int kvPosition)
new HFileKeyValue(HFileScanner scanner)
etc... where each implementation knows how to fish out the underlying data.
Later we add more implementations:
new CssTreeBlockIndexKeyValue(CssTreeBlockIndex blockIndex, int kvPosition)
@Jason - i agree about starting with the block index since it's a relatively
isolated piece of code. Could possibly be done without KeyValue
modifications. Also, i think it could work without changing the HFile if
people are willing to let it compact the index when the HFile is read rather
than when the when it's written. Would make for slightly slower startup
times but would save a lot of memory. Could be configurable.
On Thu, Jun 2, 2011 at 4:44 PM, Jonathan Gray <jg...@fb.com> wrote:
> I'm here!
>
> Still parsing through the stuff in this e-mail but I agree that many
> approaches will require rejiggering of KeyValue in a significant way. I
> have made some attempts at this but nothing is very far. It's definitely
> something that we are hoping to put some resources into over the summer.
>
> JG
>
> > -----Original Message-----
> > From: Todd Lipcon [mailto:todd@cloudera.com]
> > Sent: Thursday, June 02, 2011 3:37 PM
> > To: dev@hbase.apache.org
> > Subject: Re: prefix compression
> >
> > Hey Matt,
> >
> > Interesting email, and also something I've been thinking about recently.
> >
> > Unfortunately, I think one of the big prerequisites before we can start
> > thinking about actual compression algorithms is some refactoring around
> > how KeyValue is used. Currently, KeyValue exposes byte arrays in a lot of
> > places
> > - these byte arrays are expected to have discrete sub-ranges that
> represent
> > row key, column qualifier, etc. Various parts of the code directly grab
> these
> > byte arrays and offsets and do stuff with them (eg comparison,
> serialization,
> > etc).
> >
> > In order to split the different components up, or do true prefix
> compression,
> > we'd need to be able to have KeyValue do some smarter things for
> > comparison, and never call functions like "getBuffer), getRowKeyOffset(),
> > getRowKeyLength()" expecting that to show us a byte array range with a
> row
> > key in it.
> >
> > I think Jonathan Gray has some code hacked together for this, but last I
> > heard it wasn't in shippable state... Jonathan, you out there?
> >
> > As for the particular compression algorithms, a nice first step would be
> a very
> > simplistic one: the first row key of any HFile block would be given in
> full.
> > Following that, each key uses a single byte token which identifies the
> > number of key components shared with the previous row (ie same row,
> > same col, different ts). This isn't quite as good as true prefix
> compression,
> > but for common cases where each cell has three versions, and each row has
> > many columns, it's probably a huge savings.
> >
> > -Todd
> >
> > On Thu, Jun 2, 2011 at 3:28 PM, Matt Corgan <mc...@hotpads.com>
> > wrote:
> >
> > > I've been reading up on prefix compression for a few weeks trying to
> > > figure out the best approach. Maybe some of you have noticed that 90%
> > > of my emails to the user list have something to do with it... Now
> > > that people are working on HFile v2 and the topic of Lucene's FST has
> > > come up, I thought it would be a good time to share some thoughts. My
> > > goal here is definitely not asking anyone to implement this, but just
> > > to start a discussion that gets the proper jiras in place. I have
> > > spent a little time coding an initial rowKey trie builder that could
> > > be the start of a pluggable implementation.
> > >
> > > This is a big topic to address in one email, but I don't think the
> > > pieces make sense on their own. Here's an outline with some thoughts:
> > >
> > > * refer to prefix compression as "compaction" to avoid interfering
> > > with traditional "compression"
> > >
> > > * main goal is to reduce size of data in memory to increase effective
> > > capacity of block index, block cache, and memstores
> > > * disk access takes thousands of times longer than memory access,
> > > especially over hdfs/network
> > >
> > > * secondary goal is to speed up the processing of in memory data
> > > * memory access takes 200+ cpu cycles, so must use cache friendly
> > > algorithms
> > > * structure trie node sizes around typical 64B cache line size
> > > * investigate java's memory prefetching capabilities, possibly
> > > increasing to ~256B nodes
> > > * research
> > > paper<
> > >
> > http://www.google.com/url?sa=t&source=web&cd=5&sqi=2&ved=0CDcQFj
> > AE&url
> > >
> > =http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3
> > D10.1.
> > >
> > 1.68.4872%26rep%3Drep1%26type%3Dpdf&rct=j&q=databases%20prefetchi
> > ng&ei
> > > =7tnnTcSWLuXTiALSpNCUDA&usg=AFQjCNFpVC9oDsG1-
> > UG0x6ZsqsIHLqvGDA&sig2=o0
> > > 96l8cNrnX69oeBGChUKg
> > > >about
> > > modern memory databases and prefetching
> > > * almost always use variable length integer/long encoding
> > >
> > > * need separate compacted structures/algorithms for rowKey,
> > > colQualifier, and timestamp
> > > * structures will need to interact with varInt pointers to link
> > > rowKey<->colQualifier<->timestamp
> > >
> > > * i think in the medium to long term (and maybe even short term) it
> > > would be best to code this from scratch
> > >
> > >
> > > Static row key compaction for block cache and data blocks:
> > > * avoid schemes where each row is a diff of the previous (expensive
> > > cpu)
> > > * utilize the fact that row keys are always sorted (unlike column
> > > qualifiers)
> > > * this makes for fast tree construction during flushing and
> > > compaction
> > > * low hanging fruit: (rowKeyCompaction=simple)
> > > * pull off the common prefix of all rows
> > > * have a flag indicating if row key is repeat of the previous
> > > * more aggressive: use modified Cache Sensitive Search Trees<
> > > http://www.cs.columbia.edu/~library/TR-repository/reports/reports-1998
> > > /cucs-019-98.pdf
> > > >
> > > (rowKeyCompaction=cssTree64,
> > > cssTree256, etc)
> > > * modified to accept full byte range 0-255, store full byte strings
> > > in the tree, remove all pointers, optimize for cache line size, etc,
> > > but similar idea
> > > * CSS trees are considered sub-par because they are not easily
> > > modified, but that is ok in hbase's block cache and data blocks
> > > * input is a sorted list of byte[], output is a byte[] containing the
> > > entire prefix trie
> > > * possibly put all keys in the front of the block, and all values at
> > > the end, with value offsets/lengths in the keys
> > >
> > > Backwards compatibility
> > > * block cache could quickly compute the trie when loaded, therefore
> > > working with HFile v1
> > > * eventually want to store the computed trie on disk
> > > * HFile v2 will need to store information about the pluggable data
> > > block compaction schemes
> > >
> > >
> > > Memstore dynamic row key compaction:
> > > * this will be much more difficult than the static compaction because
> > > it needs to accept unsorted byte[]'s and support row locking
> > > * use modified C-Burstsort<
> > > http://ww2.cs.mu.oz.au/~rsinha/papers/SinhaRingZobel-2006.pdf>
> > > for
> > > dynamic unsorted data
> > > * fast and compact (best dynamic string sorting algorithm i could
> > > find)
> > > * fragmentation friendly (not original goal, but ends up working like
> > > MSLAB)
> > > * allocates large (customizable) byte[] for holding multiple leaf
> values
> > > * "bursts" the byte[] when they fill up, creating multiple child
> > > arrays
> > > * will need to add some sort of lockable object to the leaf of the
> > > rowKey trie
> > >
> > >
> > > Column qualifier compaction:
> > > * config param colQualifierCompaction=(none, lookupTable, cssTree64,
> > > etc)
> > > * use lookupTable when column qualifiers are a small bounded set
> > > * store the lookup table in memory (similar to blockIndex or
> > > fixedFileTrailer)
> > > * on second thought, this could be difficult without 2-pass
> > > compaction, but maybe still possible
> > > * use cssTree when a large or unbounded set, like when appending a
> > > numerical suffix in wide rows
> > >
> > >
> > > Timestamp compaction:
> > > * timestampCompaction=(none, hfileDiff, cssTree, flatten)
> > > * each hfile needs a lowestTimestamp stored in the trailer
> > > * hflieDiff would store a varLong relative to the lowestTimestamp
> > > * cssTree would do the same as it does for rowKeys, but the trie
> > > overhead might wipe out the space savings with such small values
> > > * flatten would not store a timestamp in each record, rather use the
> > > hfile's lowestTimestamp for every record
> > > * many use cases don't care about the timestamp
> > > * i think this works for maxVersions=1. may need more elaborate
> > > scheme for multiple versions
> > >
> > >
> > > I'll stop there... sorry for the huge email! I haven't seen much
> > > discussion on email or jira about how prefix compression should be
> > > done, so I hope this provides somewhat of a starting point.
> > >
> > > Matt
> > >
> >
> >
> >
> > --
> > Todd Lipcon
> > Software Engineer, Cloudera
>
RE: prefix compression
Posted by Jonathan Gray <jg...@fb.com>.
I'm here!
Still parsing through the stuff in this e-mail but I agree that many approaches will require rejiggering of KeyValue in a significant way. I have made some attempts at this but nothing is very far. It's definitely something that we are hoping to put some resources into over the summer.
JG
> -----Original Message-----
> From: Todd Lipcon [mailto:todd@cloudera.com]
> Sent: Thursday, June 02, 2011 3:37 PM
> To: dev@hbase.apache.org
> Subject: Re: prefix compression
>
> Hey Matt,
>
> Interesting email, and also something I've been thinking about recently.
>
> Unfortunately, I think one of the big prerequisites before we can start
> thinking about actual compression algorithms is some refactoring around
> how KeyValue is used. Currently, KeyValue exposes byte arrays in a lot of
> places
> - these byte arrays are expected to have discrete sub-ranges that represent
> row key, column qualifier, etc. Various parts of the code directly grab these
> byte arrays and offsets and do stuff with them (eg comparison, serialization,
> etc).
>
> In order to split the different components up, or do true prefix compression,
> we'd need to be able to have KeyValue do some smarter things for
> comparison, and never call functions like "getBuffer), getRowKeyOffset(),
> getRowKeyLength()" expecting that to show us a byte array range with a row
> key in it.
>
> I think Jonathan Gray has some code hacked together for this, but last I
> heard it wasn't in shippable state... Jonathan, you out there?
>
> As for the particular compression algorithms, a nice first step would be a very
> simplistic one: the first row key of any HFile block would be given in full.
> Following that, each key uses a single byte token which identifies the
> number of key components shared with the previous row (ie same row,
> same col, different ts). This isn't quite as good as true prefix compression,
> but for common cases where each cell has three versions, and each row has
> many columns, it's probably a huge savings.
>
> -Todd
>
> On Thu, Jun 2, 2011 at 3:28 PM, Matt Corgan <mc...@hotpads.com>
> wrote:
>
> > I've been reading up on prefix compression for a few weeks trying to
> > figure out the best approach. Maybe some of you have noticed that 90%
> > of my emails to the user list have something to do with it... Now
> > that people are working on HFile v2 and the topic of Lucene's FST has
> > come up, I thought it would be a good time to share some thoughts. My
> > goal here is definitely not asking anyone to implement this, but just
> > to start a discussion that gets the proper jiras in place. I have
> > spent a little time coding an initial rowKey trie builder that could
> > be the start of a pluggable implementation.
> >
> > This is a big topic to address in one email, but I don't think the
> > pieces make sense on their own. Here's an outline with some thoughts:
> >
> > * refer to prefix compression as "compaction" to avoid interfering
> > with traditional "compression"
> >
> > * main goal is to reduce size of data in memory to increase effective
> > capacity of block index, block cache, and memstores
> > * disk access takes thousands of times longer than memory access,
> > especially over hdfs/network
> >
> > * secondary goal is to speed up the processing of in memory data
> > * memory access takes 200+ cpu cycles, so must use cache friendly
> > algorithms
> > * structure trie node sizes around typical 64B cache line size
> > * investigate java's memory prefetching capabilities, possibly
> > increasing to ~256B nodes
> > * research
> > paper<
> >
> http://www.google.com/url?sa=t&source=web&cd=5&sqi=2&ved=0CDcQFj
> AE&url
> >
> =http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3
> D10.1.
> >
> 1.68.4872%26rep%3Drep1%26type%3Dpdf&rct=j&q=databases%20prefetchi
> ng&ei
> > =7tnnTcSWLuXTiALSpNCUDA&usg=AFQjCNFpVC9oDsG1-
> UG0x6ZsqsIHLqvGDA&sig2=o0
> > 96l8cNrnX69oeBGChUKg
> > >about
> > modern memory databases and prefetching
> > * almost always use variable length integer/long encoding
> >
> > * need separate compacted structures/algorithms for rowKey,
> > colQualifier, and timestamp
> > * structures will need to interact with varInt pointers to link
> > rowKey<->colQualifier<->timestamp
> >
> > * i think in the medium to long term (and maybe even short term) it
> > would be best to code this from scratch
> >
> >
> > Static row key compaction for block cache and data blocks:
> > * avoid schemes where each row is a diff of the previous (expensive
> > cpu)
> > * utilize the fact that row keys are always sorted (unlike column
> > qualifiers)
> > * this makes for fast tree construction during flushing and
> > compaction
> > * low hanging fruit: (rowKeyCompaction=simple)
> > * pull off the common prefix of all rows
> > * have a flag indicating if row key is repeat of the previous
> > * more aggressive: use modified Cache Sensitive Search Trees<
> > http://www.cs.columbia.edu/~library/TR-repository/reports/reports-1998
> > /cucs-019-98.pdf
> > >
> > (rowKeyCompaction=cssTree64,
> > cssTree256, etc)
> > * modified to accept full byte range 0-255, store full byte strings
> > in the tree, remove all pointers, optimize for cache line size, etc,
> > but similar idea
> > * CSS trees are considered sub-par because they are not easily
> > modified, but that is ok in hbase's block cache and data blocks
> > * input is a sorted list of byte[], output is a byte[] containing the
> > entire prefix trie
> > * possibly put all keys in the front of the block, and all values at
> > the end, with value offsets/lengths in the keys
> >
> > Backwards compatibility
> > * block cache could quickly compute the trie when loaded, therefore
> > working with HFile v1
> > * eventually want to store the computed trie on disk
> > * HFile v2 will need to store information about the pluggable data
> > block compaction schemes
> >
> >
> > Memstore dynamic row key compaction:
> > * this will be much more difficult than the static compaction because
> > it needs to accept unsorted byte[]'s and support row locking
> > * use modified C-Burstsort<
> > http://ww2.cs.mu.oz.au/~rsinha/papers/SinhaRingZobel-2006.pdf>
> > for
> > dynamic unsorted data
> > * fast and compact (best dynamic string sorting algorithm i could
> > find)
> > * fragmentation friendly (not original goal, but ends up working like
> > MSLAB)
> > * allocates large (customizable) byte[] for holding multiple leaf values
> > * "bursts" the byte[] when they fill up, creating multiple child
> > arrays
> > * will need to add some sort of lockable object to the leaf of the
> > rowKey trie
> >
> >
> > Column qualifier compaction:
> > * config param colQualifierCompaction=(none, lookupTable, cssTree64,
> > etc)
> > * use lookupTable when column qualifiers are a small bounded set
> > * store the lookup table in memory (similar to blockIndex or
> > fixedFileTrailer)
> > * on second thought, this could be difficult without 2-pass
> > compaction, but maybe still possible
> > * use cssTree when a large or unbounded set, like when appending a
> > numerical suffix in wide rows
> >
> >
> > Timestamp compaction:
> > * timestampCompaction=(none, hfileDiff, cssTree, flatten)
> > * each hfile needs a lowestTimestamp stored in the trailer
> > * hflieDiff would store a varLong relative to the lowestTimestamp
> > * cssTree would do the same as it does for rowKeys, but the trie
> > overhead might wipe out the space savings with such small values
> > * flatten would not store a timestamp in each record, rather use the
> > hfile's lowestTimestamp for every record
> > * many use cases don't care about the timestamp
> > * i think this works for maxVersions=1. may need more elaborate
> > scheme for multiple versions
> >
> >
> > I'll stop there... sorry for the huge email! I haven't seen much
> > discussion on email or jira about how prefix compression should be
> > done, so I hope this provides somewhat of a starting point.
> >
> > Matt
> >
>
>
>
> --
> Todd Lipcon
> Software Engineer, Cloudera
Re: prefix compression
Posted by Todd Lipcon <to...@cloudera.com>.
Hey Matt,
Interesting email, and also something I've been thinking about recently.
Unfortunately, I think one of the big prerequisites before we can start
thinking about actual compression algorithms is some refactoring around how
KeyValue is used. Currently, KeyValue exposes byte arrays in a lot of places
- these byte arrays are expected to have discrete sub-ranges that represent
row key, column qualifier, etc. Various parts of the code directly grab
these byte arrays and offsets and do stuff with them (eg comparison,
serialization, etc).
In order to split the different components up, or do true prefix
compression, we'd need to be able to have KeyValue do some smarter things
for comparison, and never call functions like "getBuffer),
getRowKeyOffset(), getRowKeyLength()" expecting that to show us a byte array
range with a row key in it.
I think Jonathan Gray has some code hacked together for this, but last I
heard it wasn't in shippable state... Jonathan, you out there?
As for the particular compression algorithms, a nice first step would be a
very simplistic one: the first row key of any HFile block would be given in
full. Following that, each key uses a single byte token which identifies the
number of key components shared with the previous row (ie same row, same
col, different ts). This isn't quite as good as true prefix compression, but
for common cases where each cell has three versions, and each row has many
columns, it's probably a huge savings.
-Todd
On Thu, Jun 2, 2011 at 3:28 PM, Matt Corgan <mc...@hotpads.com> wrote:
> I've been reading up on prefix compression for a few weeks trying to figure
> out the best approach. Maybe some of you have noticed that 90% of my
> emails
> to the user list have something to do with it... Now that people are
> working on HFile v2 and the topic of Lucene's FST has come up, I thought it
> would be a good time to share some thoughts. My goal here is definitely
> not
> asking anyone to implement this, but just to start a discussion that gets
> the proper jiras in place. I have spent a little time coding an initial
> rowKey trie builder that could be the start of a pluggable implementation.
>
> This is a big topic to address in one email, but I don't think the pieces
> make sense on their own. Here's an outline with some thoughts:
>
> * refer to prefix compression as "compaction" to avoid interfering with
> traditional "compression"
>
> * main goal is to reduce size of data in memory to increase effective
> capacity of block index, block cache, and memstores
> * disk access takes thousands of times longer than memory access,
> especially over hdfs/network
>
> * secondary goal is to speed up the processing of in memory data
> * memory access takes 200+ cpu cycles, so must use cache friendly
> algorithms
> * structure trie node sizes around typical 64B cache line size
> * investigate java's memory prefetching capabilities, possibly
> increasing to ~256B nodes
> * research
> paper<
> http://www.google.com/url?sa=t&source=web&cd=5&sqi=2&ved=0CDcQFjAE&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.68.4872%26rep%3Drep1%26type%3Dpdf&rct=j&q=databases%20prefetching&ei=7tnnTcSWLuXTiALSpNCUDA&usg=AFQjCNFpVC9oDsG1-UG0x6ZsqsIHLqvGDA&sig2=o096l8cNrnX69oeBGChUKg
> >about
> modern memory databases and prefetching
> * almost always use variable length integer/long encoding
>
> * need separate compacted structures/algorithms for rowKey, colQualifier,
> and timestamp
> * structures will need to interact with varInt pointers to link
> rowKey<->colQualifier<->timestamp
>
> * i think in the medium to long term (and maybe even short term) it would
> be
> best to code this from scratch
>
>
> Static row key compaction for block cache and data blocks:
> * avoid schemes where each row is a diff of the previous (expensive cpu)
> * utilize the fact that row keys are always sorted (unlike column
> qualifiers)
> * this makes for fast tree construction during flushing and compaction
> * low hanging fruit: (rowKeyCompaction=simple)
> * pull off the common prefix of all rows
> * have a flag indicating if row key is repeat of the previous
> * more aggressive: use modified Cache Sensitive Search
> Trees<
> http://www.cs.columbia.edu/~library/TR-repository/reports/reports-1998/cucs-019-98.pdf
> >
> (rowKeyCompaction=cssTree64,
> cssTree256, etc)
> * modified to accept full byte range 0-255, store full byte strings in
> the tree, remove all pointers, optimize for cache line size, etc, but
> similar idea
> * CSS trees are considered sub-par because they are not easily modified,
> but that is ok in hbase's block cache and data blocks
> * input is a sorted list of byte[], output is a byte[] containing the
> entire
> prefix trie
> * possibly put all keys in the front of the block, and all values at the
> end, with value offsets/lengths in the keys
>
> Backwards compatibility
> * block cache could quickly compute the trie when loaded, therefore working
> with HFile v1
> * eventually want to store the computed trie on disk
> * HFile v2 will need to store information about the pluggable data block
> compaction schemes
>
>
> Memstore dynamic row key compaction:
> * this will be much more difficult than the static compaction because it
> needs to accept unsorted byte[]'s and support row locking
> * use modified C-Burstsort<
> http://ww2.cs.mu.oz.au/~rsinha/papers/SinhaRingZobel-2006.pdf>
> for
> dynamic unsorted data
> * fast and compact (best dynamic string sorting algorithm i could find)
> * fragmentation friendly (not original goal, but ends up working like
> MSLAB)
> * allocates large (customizable) byte[] for holding multiple leaf values
> * "bursts" the byte[] when they fill up, creating multiple child arrays
> * will need to add some sort of lockable object to the leaf of the rowKey
> trie
>
>
> Column qualifier compaction:
> * config param colQualifierCompaction=(none, lookupTable, cssTree64, etc)
> * use lookupTable when column qualifiers are a small bounded set
> * store the lookup table in memory (similar to blockIndex or
> fixedFileTrailer)
> * on second thought, this could be difficult without 2-pass compaction,
> but maybe still possible
> * use cssTree when a large or unbounded set, like when appending a
> numerical
> suffix in wide rows
>
>
> Timestamp compaction:
> * timestampCompaction=(none, hfileDiff, cssTree, flatten)
> * each hfile needs a lowestTimestamp stored in the trailer
> * hflieDiff would store a varLong relative to the lowestTimestamp
> * cssTree would do the same as it does for rowKeys, but the trie overhead
> might wipe out the space savings with such small values
> * flatten would not store a timestamp in each record, rather use the
> hfile's
> lowestTimestamp for every record
> * many use cases don't care about the timestamp
> * i think this works for maxVersions=1. may need more elaborate scheme
> for multiple versions
>
>
> I'll stop there... sorry for the huge email! I haven't seen much
> discussion on email or jira about how prefix compression should be done, so
> I hope this provides somewhat of a starting point.
>
> Matt
>
--
Todd Lipcon
Software Engineer, Cloudera
Re: prefix compression
Posted by Matt Corgan <mc...@hotpads.com>.
Ah - I see. It's generating multiple duplicate timestamps per millisecond,
so there are fewer than 50mm unique strings. Duplicates just require
incrementing a counter. Agree it's very cool though!
sent from my phone
On Jun 3, 2011 9:02 PM, "Jason Rutherglen" <ja...@gmail.com>
wrote:
> Yeah it's truly super wild! Here's the code: http://pastebin.com/bnB53UQz
>
> You can see the line that's adding the string:
>
> fstBuilder.add(new BytesRef(date), new Long(x));
>
> On Fri, Jun 3, 2011 at 8:56 PM, Matt Corgan <mc...@hotpads.com> wrote:
>> Jason - are you feeding it that whole string for each date? Input data
is
>> 17 bytes per record * 50mm records = 850MB, and that reduces to 984
bytes?
>> Is it possible to compress by that much? Maybe I'm missing something
about
>> how the FST works.
>>
>> Matt
>>
>>
>> On Fri, Jun 3, 2011 at 8:51 PM, Jason Rutherglen <
jason.rutherglen@gmail.com
>>> wrote:
>>
>>> Also the next thing to measure with the FST is the key lookup speed.
>>> I'm not sure what that'd look like, or how to compare with HBase right
>>> now?
>>>
>>> On Fri, Jun 3, 2011 at 8:42 PM, Jason Rutherglen
>>> <ja...@gmail.com> wrote:
>>> > Here's a nice preliminary number with the FST, 50 million dates of the
>>> > form yyyyMMddHHmmssSSS, with each incremented by one millisecond. The
>>> > FST is 984 bytes, with an incrementing long to point to the presumably
>>> > MMap'd value data. This's a bit crazy.
>>> >
>>> > Perhaps we should try other increments as well? Given that HBase keys
>>> > especially are probably close increments of each other, I think the
>>> > FST can always be loaded into RAM with pointers out to the actual
>>> > values.
>>> >
>>>
>>
Re: prefix compression
Posted by Jason Rutherglen <ja...@gmail.com>.
Here's some more data for the 10 mil dates:
68.1 MB random increment up to 1000
87.1 MB random increment up to 10,000
162.1 MB total not using the FST
On Fri, Jun 3, 2011 at 10:57 PM, Stack <st...@duboce.net> wrote:
> That can't be true? (smile) How would you search a 'key' in the FST?
> St.Ack
>
> On Fri, Jun 3, 2011 at 9:01 PM, Jason Rutherglen
> <ja...@gmail.com> wrote:
>> Yeah it's truly super wild! Here's the code: http://pastebin.com/bnB53UQz
>>
>> You can see the line that's adding the string:
>>
>> fstBuilder.add(new BytesRef(date), new Long(x));
>>
>> On Fri, Jun 3, 2011 at 8:56 PM, Matt Corgan <mc...@hotpads.com> wrote:
>>> Jason - are you feeding it that whole string for each date? Input data is
>>> 17 bytes per record * 50mm records = 850MB, and that reduces to 984 bytes?
>>> Is it possible to compress by that much? Maybe I'm missing something about
>>> how the FST works.
>>>
>>> Matt
>>>
>>>
>>> On Fri, Jun 3, 2011 at 8:51 PM, Jason Rutherglen <jason.rutherglen@gmail.com
>>>> wrote:
>>>
>>>> Also the next thing to measure with the FST is the key lookup speed.
>>>> I'm not sure what that'd look like, or how to compare with HBase right
>>>> now?
>>>>
>>>> On Fri, Jun 3, 2011 at 8:42 PM, Jason Rutherglen
>>>> <ja...@gmail.com> wrote:
>>>> > Here's a nice preliminary number with the FST, 50 million dates of the
>>>> > form yyyyMMddHHmmssSSS, with each incremented by one millisecond. The
>>>> > FST is 984 bytes, with an incrementing long to point to the presumably
>>>> > MMap'd value data. This's a bit crazy.
>>>> >
>>>> > Perhaps we should try other increments as well? Given that HBase keys
>>>> > especially are probably close increments of each other, I think the
>>>> > FST can always be loaded into RAM with pointers out to the actual
>>>> > values.
>>>> >
>>>>
>>>
>>
>
Re: prefix compression
Posted by Jason Rutherglen <ja...@gmail.com>.
I varied the ms increment randomly between 1-20, then created 10 mil
dates. The FST was then 58,481,582 bytes, eg, 57 MB. Guess it's not
perfect! 19,739,994 bytes, eg, 18.8 MB for random 1-5 increments. I
think that's still pretty good. I need to try varying the long value
stored alongside to see how much that affects the total size.
How many keys are there for a typical region, and total per region
server? One could try MMap'ing the FST however because access to it
is completely random, there's a high risk of page faults.
If a key set is sequentially incremented by 1, then that's clearly
optimal. I'd guess that's a fairly common case though?
> How would you search a 'key' in the FST?
Searching on a key is via BytesRefFSTEnum.seekCeil method, which'll
match anything greater than or equal to the given parameter.
Clearly more benchmarking is required, however this would seem to be a
highly promising avenue of research for HBase.
On Fri, Jun 3, 2011 at 10:57 PM, Stack <st...@duboce.net> wrote:
> That can't be true? (smile) How would you search a 'key' in the FST?
> St.Ack
>
> On Fri, Jun 3, 2011 at 9:01 PM, Jason Rutherglen
> <ja...@gmail.com> wrote:
>> Yeah it's truly super wild! Here's the code: http://pastebin.com/bnB53UQz
>>
>> You can see the line that's adding the string:
>>
>> fstBuilder.add(new BytesRef(date), new Long(x));
>>
>> On Fri, Jun 3, 2011 at 8:56 PM, Matt Corgan <mc...@hotpads.com> wrote:
>>> Jason - are you feeding it that whole string for each date? Input data is
>>> 17 bytes per record * 50mm records = 850MB, and that reduces to 984 bytes?
>>> Is it possible to compress by that much? Maybe I'm missing something about
>>> how the FST works.
>>>
>>> Matt
>>>
>>>
>>> On Fri, Jun 3, 2011 at 8:51 PM, Jason Rutherglen <jason.rutherglen@gmail.com
>>>> wrote:
>>>
>>>> Also the next thing to measure with the FST is the key lookup speed.
>>>> I'm not sure what that'd look like, or how to compare with HBase right
>>>> now?
>>>>
>>>> On Fri, Jun 3, 2011 at 8:42 PM, Jason Rutherglen
>>>> <ja...@gmail.com> wrote:
>>>> > Here's a nice preliminary number with the FST, 50 million dates of the
>>>> > form yyyyMMddHHmmssSSS, with each incremented by one millisecond. The
>>>> > FST is 984 bytes, with an incrementing long to point to the presumably
>>>> > MMap'd value data. This's a bit crazy.
>>>> >
>>>> > Perhaps we should try other increments as well? Given that HBase keys
>>>> > especially are probably close increments of each other, I think the
>>>> > FST can always be loaded into RAM with pointers out to the actual
>>>> > values.
>>>> >
>>>>
>>>
>>
>
Re: prefix compression
Posted by Stack <st...@duboce.net>.
That can't be true? (smile) How would you search a 'key' in the FST?
St.Ack
On Fri, Jun 3, 2011 at 9:01 PM, Jason Rutherglen
<ja...@gmail.com> wrote:
> Yeah it's truly super wild! Here's the code: http://pastebin.com/bnB53UQz
>
> You can see the line that's adding the string:
>
> fstBuilder.add(new BytesRef(date), new Long(x));
>
> On Fri, Jun 3, 2011 at 8:56 PM, Matt Corgan <mc...@hotpads.com> wrote:
>> Jason - are you feeding it that whole string for each date? Input data is
>> 17 bytes per record * 50mm records = 850MB, and that reduces to 984 bytes?
>> Is it possible to compress by that much? Maybe I'm missing something about
>> how the FST works.
>>
>> Matt
>>
>>
>> On Fri, Jun 3, 2011 at 8:51 PM, Jason Rutherglen <jason.rutherglen@gmail.com
>>> wrote:
>>
>>> Also the next thing to measure with the FST is the key lookup speed.
>>> I'm not sure what that'd look like, or how to compare with HBase right
>>> now?
>>>
>>> On Fri, Jun 3, 2011 at 8:42 PM, Jason Rutherglen
>>> <ja...@gmail.com> wrote:
>>> > Here's a nice preliminary number with the FST, 50 million dates of the
>>> > form yyyyMMddHHmmssSSS, with each incremented by one millisecond. The
>>> > FST is 984 bytes, with an incrementing long to point to the presumably
>>> > MMap'd value data. This's a bit crazy.
>>> >
>>> > Perhaps we should try other increments as well? Given that HBase keys
>>> > especially are probably close increments of each other, I think the
>>> > FST can always be loaded into RAM with pointers out to the actual
>>> > values.
>>> >
>>>
>>
>
Re: prefix compression
Posted by Jason Rutherglen <ja...@gmail.com>.
Yeah it's truly super wild! Here's the code: http://pastebin.com/bnB53UQz
You can see the line that's adding the string:
fstBuilder.add(new BytesRef(date), new Long(x));
On Fri, Jun 3, 2011 at 8:56 PM, Matt Corgan <mc...@hotpads.com> wrote:
> Jason - are you feeding it that whole string for each date? Input data is
> 17 bytes per record * 50mm records = 850MB, and that reduces to 984 bytes?
> Is it possible to compress by that much? Maybe I'm missing something about
> how the FST works.
>
> Matt
>
>
> On Fri, Jun 3, 2011 at 8:51 PM, Jason Rutherglen <jason.rutherglen@gmail.com
>> wrote:
>
>> Also the next thing to measure with the FST is the key lookup speed.
>> I'm not sure what that'd look like, or how to compare with HBase right
>> now?
>>
>> On Fri, Jun 3, 2011 at 8:42 PM, Jason Rutherglen
>> <ja...@gmail.com> wrote:
>> > Here's a nice preliminary number with the FST, 50 million dates of the
>> > form yyyyMMddHHmmssSSS, with each incremented by one millisecond. The
>> > FST is 984 bytes, with an incrementing long to point to the presumably
>> > MMap'd value data. This's a bit crazy.
>> >
>> > Perhaps we should try other increments as well? Given that HBase keys
>> > especially are probably close increments of each other, I think the
>> > FST can always be loaded into RAM with pointers out to the actual
>> > values.
>> >
>>
>
Re: prefix compression
Posted by Matt Corgan <mc...@hotpads.com>.
Jason - are you feeding it that whole string for each date? Input data is
17 bytes per record * 50mm records = 850MB, and that reduces to 984 bytes?
Is it possible to compress by that much? Maybe I'm missing something about
how the FST works.
Matt
On Fri, Jun 3, 2011 at 8:51 PM, Jason Rutherglen <jason.rutherglen@gmail.com
> wrote:
> Also the next thing to measure with the FST is the key lookup speed.
> I'm not sure what that'd look like, or how to compare with HBase right
> now?
>
> On Fri, Jun 3, 2011 at 8:42 PM, Jason Rutherglen
> <ja...@gmail.com> wrote:
> > Here's a nice preliminary number with the FST, 50 million dates of the
> > form yyyyMMddHHmmssSSS, with each incremented by one millisecond. The
> > FST is 984 bytes, with an incrementing long to point to the presumably
> > MMap'd value data. This's a bit crazy.
> >
> > Perhaps we should try other increments as well? Given that HBase keys
> > especially are probably close increments of each other, I think the
> > FST can always be loaded into RAM with pointers out to the actual
> > values.
> >
>
Re: prefix compression
Posted by Jason Rutherglen <ja...@gmail.com>.
Also the next thing to measure with the FST is the key lookup speed.
I'm not sure what that'd look like, or how to compare with HBase right
now?
On Fri, Jun 3, 2011 at 8:42 PM, Jason Rutherglen
<ja...@gmail.com> wrote:
> Here's a nice preliminary number with the FST, 50 million dates of the
> form yyyyMMddHHmmssSSS, with each incremented by one millisecond. The
> FST is 984 bytes, with an incrementing long to point to the presumably
> MMap'd value data. This's a bit crazy.
>
> Perhaps we should try other increments as well? Given that HBase keys
> especially are probably close increments of each other, I think the
> FST can always be loaded into RAM with pointers out to the actual
> values.
>
Re: prefix compression
Posted by Jason Rutherglen <ja...@gmail.com>.
Here's a nice preliminary number with the FST, 50 million dates of the
form yyyyMMddHHmmssSSS, with each incremented by one millisecond. The
FST is 984 bytes, with an incrementing long to point to the presumably
MMap'd value data. This's a bit crazy.
Perhaps we should try other increments as well? Given that HBase keys
especially are probably close increments of each other, I think the
FST can always be loaded into RAM with pointers out to the actual
values.