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in 5.1, Solr 5.2 will add efficient set cardinality using the HyperLogLog algorithm.

Basic Usage

Like most of the existing stat component options, cardinality of a field (or function values) can be requested using a simple local param option with a value. For example…

Here we see that in the sample data, the 32 (numFound) documents contain 18 (count) total values in the field — and of those 14 (cardinality) are unique.

And of course, like all stats, this can be combined with pivot facets to find things like the number of unique manufacturers per category…


Astute readers may ask: “Hasn’t Solr always supported cardinality using the option?” The answer to that question is: sort of.

The option has never been recommended for anything other then trivial use cases because it used a very naive implementation of computing set cardinality — namely: it built in memory (and returned to the client) a full set of all the . This performs fine for small sets, but as the cardinality increases, it becomes trivial to crash a server with an OutOfMemoryError with only a handful of concurrent users. In a distributed search, the behavior is even worse (and much slower) since all of those full sets on each shard must be sent over the wire to the coordinating node to be merged.

Solr 5.2 improves things slightly by splitting the option in two and letting clients request independently from the set of all . Under the covers Solr is still doing the same amount of work (and in distributed requests, the nodes are still exchanging the same amount of data) but asking only for spares the clients from having to receive the full set of all values.

How the new option differs, is that it uses probabilistic “HyperLogLog” (HLL) algorithm to the cardinality of the sets in a fixed amount of memory. Dorothy Perkins Womens Noisy May Ruffle Dress Cheapest Free Shipping Good Selling Cheap Great Deals Latest Online Explore Online EyaoRv
far better then I could, but the key points Solr users should be aware of are:

The examples we’ve seen so far used as a local param — this is actually just syntactic sugar for . Any number between 0.0 and 1.0 (inclusively) can be specified to indicate how the user would like to trade off RAM vs accuracy:

Internally these floating point values, along with some basic heuristics about the Solr field type (ie: 32bit field types like int and float have a much smaller max-possible cardinality then fields like long, double, strings, etc…) are used to tune the “log2m” and “regwidth” options of the underlying implementation . Advanced Solr users can provide explicit values for these options using the and localparams, see the StatsComponent documentation for more details.

Accuracy versus Performance: Comparison Testing

To help showcase the trade offs between using the old logic, and the new HLL based option, I setup a Miss Selfridge Sweetheart Neckline Body Sale Popular Free Shipping Purchase IympdWdS8
to help compare them.

The initial setup is fairly straight forward:

Note that because we generated 3 random values in each field for each documents, we expect the cardinality results of each query to be ~3x the number of documents matched by that query. (Some minor variations may exist if multiple documents just so happened to contain the same randomly generated field values).

With this pre-built index, and this set of pre-generated random queries, we can then execute the query set over and over again with different options to compute the cardinality. Specifically, for both of our test fields, the following variants were tested:

For each field, and each , 3 runs of the full query set were executed sequentially using a single client thread, and both the resulting cardinality as well as the mean+stddev of the response time (as observed by the client) were recorded.

Test Results

Looking at graphs of the raw numbers returned by each approach isn’t very helpful, it basically just looks like a perfectly straight line with a slope of 1 — which is good. A Straight line means we got the answers we expect.

But the devil is in the details. What we really need to look at in order to meaningfully compare the measured accuracy of the different approaches is the “ relative error “. As we can see in this graph below, the most accurate results clearly come from using . After that is a very close second, and the measured accuracy gets worse as the tuning value for the option gets lower.

Looking at these results, you may wonder: Why bother using the new option at all?

To answer that question, let’s look at the next 2 graphs. The first shows the mean request time (as measured from the query client) as the number of values expected in the set grows. There is a lot of noise in this graph at the low values due to poor warming queries on my part in the testing process — so the second graph shows a cropped view of the same data

Here we start to see some obvious advantage in using the option. While the response times continue to grow and get more and more unpredictable — largely because of extensive garbage collection — the cost (in processing time) of using the option practically levels off. So it becomes fairly clear that if you can accept a small bit of approximation in your set cardinality statistics, you can gain a lot of confidence and predictability in the behavior of your queries. And by tuning the parameter, you can trade off accuracy for the amount of RAM used at query time, with relatively minor impacts on response time performance.

If we look at the results for the string field we can see that while the Boohoo Distressed 5Pocket Denim Skirt Visit New Best Place Online Best Seller For Sale Countdown Package For Sale Aberdeen NPPNR33Z
are virtually identical to, and the request time performance of the option is consistent with, that of the numeric fields (due to hashing) the request time performance of completely falls apart — even though these are relatively small string values….

I would certainly never recommend anyone use with non trivial string fields.

Next Steps

There are still several things about the HLL implementation that could be be made “user tunable” with a few more request time knobs/dials once users get a chance to try out and experiment with this new feature and give feedback — but I think the biggest bang for the buck will be to add index time hashing support — which should help a lot in speeding up the response times of cardinality computations using the classic trade off: do more work at index time, and make your on disk index a bit larger, to save CPU cycles at query time and reduce query response time.

What’s coming in Solr 5.2 | Jorge Luis Betancourt, my thoughts on tech

[…] dive in how this or any other of the features is implemented, Hoss have done a really good job on this post by Lucidworks, suffice to say that this new stat added to the Stats Component is a big win if you’re using […]

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Indeed, that does seem to be the case. And given that it’s songwriting that has prompted me to get into these things, I am trying to understand the terminology typically used, but it’s awfully ambiguous sometimes.

Part of my problem, I’m sure, is that a lot of the stuff I listen to doesn’t neatly fit into these kinds of structures. In many cases, even verses and choruses are not clear-cut, pretty much the only part that is really identifiable is a bridge (and @Paul999 I like the way you defined “bridge” here).

And given those influences, I try to avoid things that are “too normal” in that structural sense, although I should probably not aim quite so high… walk before I run and all that.

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Feb '17

Part of my problem, I’m sure, is that a lot of the stuff I listen to doesn’t neatly fit into these kinds of structures. In many cases, even verses and choruses are not clear-cut

That’s funny – I literally grew up listening to the Beatles and as such have to really force myself NOT to use a pre-chorus and bridge, it just comes so naturally

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I think you’ve hit on the crux of it all. Whenever I teach music theory, I start with the soapbox speech that the music came first and the theory came later in order to attempt to understand/categorize/explain what’s going on.

All of these terms are just that - terms that we use to make sense of what’s happening in the music. Yes, there are some formulas, structures and patterns that have proven to be more robust and popular than others, but when it comes down to it, music theory is not a set of rules or boundaries in which you must remain.

And it’s not saying that you have to rebel against the standard “song structure” - just don’t feel that colouring outside the lines once in a while will get you lynched.

Chordwainer Regular
Feb '17

I also grew up with the Beatles and all of the “traditional” song structures, yet even so am often confused when reading songwriting articles or mixing stuff-- naturally, not everyone uses these terms the same way.

And I sure know that I’m in the right community not to get lynched for being adventuresome! My main goal on this is just to gain a better understanding when I’m reading jargon-y writeups where these sorts of terms are tossed around. I’m not shy about either adhering to “accepted” structures or about violating them!

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Yes bridge is part that leads to chorous, usually involves a repeating theme or chord change ready to let the chorous have a bigger impact.

Middle 8 as i know it is an 8 bar part nearer the end of the song, typically before a double chorous or big ending at the end which although similar to the bridge tends to have a different lyrical content, rythem or melody to the rest of the song so when the song reverts back to the chorus at the end it has a bigger impact. I always see it as a sing structure like this: Verse-chorus-verse-chorus-middle8-chorus

Clear as mud lol

Hard to explain, But you will hear examples in loads of songs you know.

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Here’s a song i did a while back which sort of exagerates what i mean about a middle 8 (Please excuse the shitty mix and pitchy vocals, its all one take and i got fed up withit so this is how it ended up)

The ‘middle 8’ ish part is at 1:52, you’ll understand what i mean if u listen to the whole track.

the call, an upbeat track written , performed and produced at home by me. enjoy...

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