A few weeks back one of the Vertica developers put up a blog post on counting triangles in an undirected graph with reciprocal edges. The author was comparing the size of the data and the elapsed times to run this calculation on Hadoop and Vertica and put up the work on github and encouraged others: “do try this at home.” So I did.
Vertica draws attention to the fact that their compression brought the size of the 86,220,856 tuples down to 560MB in size, from a flat file size of 1,263,234,543 bytes resulting in around a 2.25X compression ratio. My first task was to load the data and see how Oracle’s Hybrid Columnar Compression would compare. Below is a graph of the sizes.
As you can see, Oracle’s default HCC query compression (query high) compresses the data over 2X more than Vertica and even HCC query low compression beats out Vertica’s compression number.
Query Elapsed Times
The closest gear I had to Vertica’s hardware was an Exadata X2-2 system — both use 2 socket, 12 core Westmere-EP nodes. While one may try to argue that Exadata may somehow influence the execution times, I’ll point out that I was using In-Memory Parallel Execution so no table data was even read from spinning disk or Exadata Flash Cache — it’s all memory resident in the database nodes’ buffer cache. This seems to be inline with how Vertica executed their tests though not explicitly stated (it’s a reasonable assertion).
After I loaded the data and gathered table stats, I fired off the exact same SQL query that Vertica used to count triangles to see how Oracle would compare. I ran the query on 1, 2 and 4 nodes just like Vertica. Below is a graph of the results.
As you can see, the elapsed times are reasonably close but overall in the favor of Oracle winning 2 of the 3 scale points as well as having a lower sum of the three executions: Vertica 519 seconds, Oracle 487 seconds — advantage Oracle of 32 seconds.
It Should Go Faster!
As a database performance engineer I was thinking to myself, “it really should go faster!” I took a few minutes to look over things to see what could make this perform better. You might think I was looking at parameters or something like that, but you would be wrong. After a few minutes of looking at the query and the execution plan it became obvious to me — it could go faster! I made a rather subtle change to the SQL query and reran my experiments. With the modified SQL query Oracle was now executing twice as fast on 1 node than Vertica was on 4 nodes. Also, on 4 nodes, the elapsed time came in at just 14 seconds, compared to the 97 seconds Vertica reported — a difference of almost 7X! Below are the combined results.
What’s The Go Fast Trick?
I was thinking a bit more about the problem at hand — we need to count vertices but not count them twice since they are reciprocal. Given that for any edge, it exists in both directions, the query can be structured like Vertica wrote it — doing the filtering with a join predicate like e1.source < e2.source to eliminate the duplicates or we can simply use a single table filter predicate like source < dest before the join takes place. One of the first things they taught me in query planning and optimization class was to filter early! That notation pays off big here because the early filter cuts the rows going into the first join as well as the output of the first join by a factor of 2 — 1.8 billion rows output vs. 3.6 billion. That’s a huge savings not only in the first join, but also in the second join as well.
Here is what my revised query looks like:
with e1 as (select * from edges where source < dest), e2 as (select * from edges where source < dest), e3 as (select * from edges where source > dest) select count(*) from e1 join e2 on (e1.dest = e2.source) join e3 on (e2.dest = e3.source) where e3.dest = e1.source
First, I’d like to thank the Vertica team for throwing the challenge out there and being kind enough to provide the data, code and their elapsed times. I always enjoy a challenge — especially one that I can improve upon. Now, I’m not going to throw any product marketing nonsense out there as that is certainly not my style (and there certainly is more than enough of that already), but rather I’ll just let the numbers do the talking. I’d also like to point out that this experiment was done without any structure other than the table. And in full disclosure, all of my SQL commands are available as well.
The other comment that I would make is that the new and improved execution times really make a mockery of the exercise when comparing to Hadoop MapReduce or Pig, but I would also mention that this test case is extremely favorable for parallel pipelined databases that can perform all in-memory operations and given the data set is so small, this is the obviously the case. Overall, in my opinion, a poor problem choice to compare the three technologies as it obviously (over) highlights the right tool for the job cliche.
Experiments performed on Oracle Database 126.96.36.199.
Github source code: https://gist.github.com/grahn/1289188
After posting about how ridiculous some of the benchmarketing claims that database vendors are making, Dave Menninger, VP of Marketing & Product Management at Vertica posted a comment that one of their customers reported a 40,400x gain in one query (this of course is after I openly joked about the 16,200x Vertica claim). So I made my way over to check out this claim, and sure enough, someone reported this. Here is the table presented in the webcast:
To this database performance engineer, this yet another unimpressive performance claim, but rather a very creative use of numbers, or maybe better put, a good case of bad math. Or better yet, big fun with small numbers. Honestly, measuring a BI query response time in milliseconds?!?! I don’t even know if OLTP database users measure their query response time in milliseconds. I simply can’t stop laughing at the fact that there needs to be precision below 1 second. Obviously BI users could not possibly tell that their query ran in less than 1 second because the network latency would mask this. Not only that, it seems there were 154 queries to choose from and the Vertica marketing crew chose to mention this one. Brilliant I say. So yes Dave, this is even more ludicrous than the 16,200x claim. At best it is a 202x gain. You won’t get credit from me (and probably others) for fractional seconds, but thanks for mentioning it. It was a good chuckle. By the way, why add two extra places of precision for this query and not all the others?
I think it is also worth mentioning that the data set size for this case is 84GB (raw) and 10.5GB in the Vertica DB (8x compression). Given the server running the database has 32GB of RAM it easily classifies as an in-memory database, so response time should certainly be in the seconds. I don’t know about you, but performance claims on a database in which the uncompressed data fits on an Apple iPod don’t excite me.
Dave Menninger also mentions:
One other piece of information in an effort of full (or at least more) disclosure is the following blog post that breaks down the orders of magnitude differences between row stores and column stores to their constituent parts.
Debunking Yet Another Myth: Column-Stores As A Storage-Layer Only Optimization
Column stores have been a topic of many research papers. The one that has caught my attention most recently is the paper by Allison Holloway and David DeWitt (Go Badgers!) entitled Read-Optimized Databases, In Depth and the VLDB 2008 presentation which has an alternate title of Yet Another Row Store vs Column Store Paper. I might suggest that you give them a read. Perhaps the crew at The Database Column will offer some comments on Allison and David’s research. I’m surprised that they haven’t already.
Well, that’s enough fun for a Friday. Time to kick off some benchmark queries on my HP Oracle Database Machine.
A few weeks ago I read Curt Monash’s report on interpreting the results of data warehouse proofs-of-concept (POCs) and I have to say, I’m quite surprised that this topic hasn’t been covered more by analysts in the data warehousing space. I understand that analysts are not database performance engineers, but where do they think that the performance claims of 10x to 100x or more come from? Do they actually investigate these claims or just report on them? I can not say that I have ever seen any database analyst offer any technical insight into these boasts of performance. If some exist be sure to leave a comment and point me to them.
Oracle Exadata Performance Architect Kevin Closson has blogged about a 485x performance increase of Oracle Exadata vs. Oracle Exadata and his follow-up post to explain exactly where the 485x performance gain comes from gave me the nudge to finish this post that had been sitting in my drafts folder since I first read Curt’s post.
Customer Bechmarketing Claims
I thought I would compile a list of what the marketing folks at other database vendors are saying about the performance of their products. Each of these statements have been taken from the given vendor’s website.
- Netezza: 10-100 times faster than traditional solutions…but it is not uncommon to see performance differences as large as 200x to even 400x or more when compared to existing Oracle systems
- Greenplum: often 10 to 100 times faster than traditional solutions
- DATAllegro: 10-100x performance over traditional platforms
- Vertica: Performs 30x-200x faster than other solutions
- ParAccel: 20X – 200X performance gains
- EXASolution: can perform up to 100 times faster than with traditional databases
- Kognitio WX2: Tests have shown to out-perform other database / data warehouse solutions by 10-60 times
Certainly seems these vendors are a positioning themselves against traditional database solutions, whatever that means. And differences as large as 400x against Oracle? What is it exactly they are comparing?
Investigative Research On Netezza’s Performance Claims
Using my favorite Internet search engine I came across this presentation by Netezza dated October 2007. On slide 21 Netezza is comparing an NPS 8150 (112 SPU, up to 4.5 TB of user data) server to IBM DB2 UDB on a p680 with 12 CPUs (the existing solution). Not being extremely familiar with the IBM hardware mentioned, I thought I’d research to see exactly what an IBM p680 server consists of. The first link in my search results took me to here where the web page states:
The IBM eServer pSeries 680 has been withdrawn from the market, effective March 28, 2003.
Searching a bit more I came across this page which states that the 12 CPUs in the pSeries 680 are RS64 IV microprocessors. According to Wikipedia the “RS64-IV or Sstar was introduced in 2000 at 600 MHz, later increased to 750 MHz”. Given that at best, the p680 had 12 CPUs running at 750 MHz and the NPS 8150 had 112 440GX PowerPC processors I would give the compute advantage to Netezza by a significant margin. I guess it is cool to brag how your most current hardware beat up on some old used and abused server who has already been served its end-of-life notice. I found it especially intriguing that Netezza is boasting about beating out an IBM p680 server that has been end-of-lifed more than four years prior to the presentation’s date. Perhaps they don’t have any more recent bragging to do?
Going back one slide to #20 you will notice a comparison of Netezza and Oracle. Netezza clearly states they used a NPS 8250 (224 SPUs, up to 9 TB of user data) against Oracle 10g RAC running on Sun/EMC. Well ok…Sun/EMC what??? Obviously there were at least 2 Sun servers, since Oracle 10g RAC is involved, but they don’t mention the server models at all, nor the storage, nor the storage connectivity to the hosts. Was this two or more Sun Netra X1s or what??? Netezza boasts a 449x improvement in a “direct comparison on one day’s worth of data”. What exactly is being compared is up to the imagination. I guess this could be one query or many queries, but the marketeers intentionally fail to mention. They don’t even mention the data set size being compared. Given that Netezza can read data off the 224 drives at 60-70 MB/s, the NPS 8250 has a total scan rate of over 13 GB/s. I can tell you first hand that there are very few Sun/EMC solutions that are configured to support 13 GB/s of I/O bandwidth. Most configurations of that vintage probably don’t support 1/10th of that I/O bandwidth (1.3 GB/s).
Here are a few more comparisons that I have seen in Netezza presentations:
- NPS 8100 (112 SPUs/4.5 TB max) vs. SAS on Sun E5500/6 CPUs/6GB RAM
- NPS 8100 (112 SPUs/4.5 TB max) vs. Oracle 8i on Sun E6500/12 CPUs/8 GB RAM
- NPS 8400 (448 SPUs/18 TB max) vs. Oracle on Sun (exact hardware not mentioned)
- NPS 8100 (112 SPUs/4.5 TB max) vs. IBM SP2 (database not mentioned)
- NPS 8150z (112 SPUs/5.5 TB max) vs. Oracle 9i on Sun/8 CPUs
- NPS 8250z (224 SPUs/11 TB max) vs. Oracle 9i on Sun/8 CPUs
As you can see, Netezza has a way of finding the oldest hardware around and then comparing it to its latest, greatest NPS. Just like Netezza slogan, [The Power to ]Question Everything™, I suggest you question these benchmarketing reports. Database software is only as capable as the hardware it runs on and when Netezza targets the worst performing and oldest systems out there, they are bound to get some good marketing numbers. If they compete against the latest, greatest database software running on the latest, greatest hardware, sized competitively for the NPS being used, the results are drastically different. I can vouch for that one first hand having done several POCs against Netezza.
One Benchmarketing Claim To Rule Them All
Now, one of my favorite benchmarketing reports is one from Vertica. Michael Stonebraker’s blog post on customer benchmarks contains the following table:
Take a good look at the Query 2 results. Vertica takes a query running in the current row store from running in 4.5 hours (16,200 seconds) to 1 second for a performance gain of 16,200x. Great googly moogly batman, that is reaching ludicrous speed. Heck, who needs 100x or 400x when you do 16,200x. That surely warrants an explanation of the techniques involved there. It’s much, much more than simply column store vs. row store. It does raise the question (at least to me): why Vertica doesn’t run every query in 1 second. I mean, come on, why doesn’t that 19 minute row store query score better than a 30x gain? Obviously there is a bit of the magic pixie dust going on here with, what I would refer to as “creative solutions” (in reality it is likely just a very well designed projection/materaizied view, but by showing the query and telling us how it was possible would make it less unimpressive [sic]).
What Is Really Going On Here
First of all, you will notice that not one of these benchmarketing claims is against a vendor run system. Each and every one of these claims are against existing customer systems. The main reason for this is that most vendors prohibit benchmark results being published with out prior consent from the vendor in the licensing agreement. Seems the creative types have found that taking the numbers from the existing, production system is not prohibited in the license agreement so they compare that to their latest, greatest hardware/software and execute or supervise the execution of a benchmark on their solution. Obviously this is a one sided apples to bicycles comparison, but quite favorable for bragging rights for the new guy.
I’ve been doing customer benchmarks and proof of concepts (POCs) for almost 5 years at Oracle. I can guarantee you that Netezza has never even come close to getting 10x-100x the performance over Oracle running on a competitive hardware platform. Now I can say that it is not uncommon for Oracle running on a balanced system to perform 10x to 1000x (ok, in extreme cases) over an existing poorly performing Oracle system. All it takes is to have a very unbalanced system with no I/O bandwidth, not be using parallel query, not use compression, poor or no use of partitioning and you have created a springboard for any vendor to look good.
One More Juicy Marketing Tidbit
While searching the Internet for creative marketing reports I have to admit that the crew at ParAccel probably takes the cake (and not in an impressive way). On one of their web pages they have these bullet points (plus a few more uninteresting ones):
- All operations are done in parallel (A non-parallel DBMS must scan all of the data sequentially)
- Adaptive compression makes disks faster…
Ok, so I can kinda, sorta see the point that a non-parallel DBMS must do something sequentially…not sure how else it would do it, but then again, I don’t know any enterprise database that is not capable of parallel operations. However, I’m going to need a bit of help on the second point there…how exactly does compression make disks faster? Disks are disks. Whether or not compression is involved has nothing to do with how fast a disk is. Perhaps they mean that compression can increase the logical read rate from a disk given that compression allows more data to be stored in the same “space” on the disk, but that clearly is not what they have written. Reminds me of DATAllegro’s faster-than-wirespeed claims on scan performance. Perhaps these marketing guys should have their numbers and wording validated by some engineers.
Do You Believe In Magic Or Word Games?
Creditable performance claims need to be accounted for and explained. Neil Raden from Hired Brains Research offers guidance for evaluating benchmarks and interpreting market messaging in his paper, Questions to Ask a Data Warehouse Appliance Vendor. I think Neil shares the same opinion of these silly benchmarketing claims. Give his paper a read.