HANA, BLU, Hekaton, and Oracle 12c vs. Teradata and Greenplum – November 2013

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I would like to point out a very important section in the paper on Hekaton on the Microsoft Research site here. I will quote the section in total:

2. DESIGN CONSIDERATIONS 

An analysis done early on in the project drove home the fact that a 10-100X throughput improvement cannot be achieved by optimizing existing SQL Server mechanisms. Throughput can be increased in three ways: improving scalability, improving CPI (cycles per instruction), and reducing the number of instructions executed per request. The analysis showed that, even under highly optimistic assumptions, improving scalability and CPI can produce only a 3-4X improvement. The detailed analysis is included as an appendix. 

The only real hope is to reduce the number of instructions executed but the reduction needs to be dramatic. To go 10X faster, the engine must execute 90% fewer instructions and yet still get the work done. To go 100X faster, it must execute 99% fewer instructions. This level of improvement is not feasible by optimizing existing storage and execution mechanisms. Reaching the 10-100X goal requires a much more efficient way to store and process data. 

This is important because it confirms the difference in a Level 3 and a Level 2 columnar implementation as described here. It is just not possible for a Level 2 implementation with a row-based join engine to achieve the performance of a Level 3 implementation. This will allow the Level 3 implementations: HANA, BLU, Hekaton, and Oracle 12c to distance themselves from the Level 2 products: Teradata and Greenplum; by more than 10X… and this is a very significant advantage.

Related articles

CPUs and HW for HANA, BLU, Hekaton, and Oracle 12c

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This short post is intended to provide a quick warning regarding in-memory columnar and cpu requirements… with a longer post to follow.

When a row is inserted or bulk-loaded into a DBMS, if there are no indexes, the amount of cpu required is very small. The majority of the time is spent committing a transaction is the time to write a log record to persist the data.

When the same record is reformatted into a column the amount of processing required is significantly higher. The data must be parsed into columns, the values must be compressed, dictionaries may be updated, and the breadcrumbs that let the columnar data be regenerated into rows must be laid. Further, if the columnar structure are to be optimized then the data must be ordered… with a sort or some kind of index structure. I have seen academic papers that suggest that for an insert columnar processing may be 100X more than row processing… and you can see why this could be true (I apologize for not finding the reference… I’ll dig it up… as I recall I read it in a post some time back by Daniel Abadi).

Now let’s think about this… several vendors are suggesting that you can deploy their columnar features with no changes required… no new hardware… in-place. But this does not ring true if the new columnar feature requires 100X extra CPU cycles per row… or 50X… or 10X… unless you are running your database on an empty server.

This claim is a shot at SAP who, more honestly, suggests new hardware with high-end processors for their in-memory columnar product… but methinks it is marketing, not architecture, from these other folks.

BLU Meanies: Data In-memory

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IBM is presenting a DB2 Tech Talk that compares the BLU Accelerator to HANA. There are several mistakes and some odd thinking in the pitch so let me address the issues as a way to explain some things about HANA and about BLU. This blog will consider what data needs to be in-memory.

IBM like several others, continues to repeat a talking point along the lines of: “We believe that you should not have to fit all of you active data in memory…”. Let’s think about this…

Note that in the current release HANA has a constraint that all of the data in a single column, the entire vector that represents the data in that column, must be in-memory before it can be operated on. If the table is partitioned and partition-elimination is applied then the data in the partition for the column must be loaded in-memory. This is a real constraint that will be removed in a subsequent release… but it is not a very severe constraint if you think about it.

But let’s be clear… HANA does not require all data to be in-memory… it will read data from peripheral devices in and out as required just as BLU does.

Now what does this mean? Let’s walk through some scenarios.

First, let’s imagine a customer with 10TB of user data, per the scenario IBM discusses. Let’s not get into a whose product compresses better discussion and assume that both BLU and HANA will get 4X compression… so there is 2.5TB of user data to be processed.

Now let’s imagine a system with only a very little memory available for data. In other words, let’s configure both BLU and HANA so that they are full columnar databases, but not in-memory databases. In this case BLU would operate by doing constant I/O without constraint and HANA would fail whenever it could not fit a required column in memory. Note that HANA might not fail at all… it would depend on whether there was a large single un-partitioned column that was required.

This scenario is really silly though… HANA is an in-memory database, designed to keep data in-memory from the start… so SAP would not support this imaginary configuration. The fact that you could make BLU work out of memory is not really relevant as nowhere does IBM position, or reference, BLU as a disk-based column store add-on… you would just use DB2.

Now let’s configure a system to IBM’s specification with 400GB of memory. IBM does not really say how much of this memory is available to BLU for data… but for the sake of argument let’s ignore the system requirements and assume that BLU uses one-half, 200GB, as work space to process queries so that 200GB is available to store data in-memory. As you will see it does not really matter in this argument whether I am spot on here or not. So using IBM’s recommendation there is now a 200GB cache that can be used as data is paged in and out. Anyone who has ever used a data warehouse knows that caching does not work well for BI queries as each query touches large enough volumes of the data to flush the cache… so BLU will effectively be performing I/O for most queries and is back to being an out-of-memory columnar database. Note that this flushing issue is why the in-memory capabilities from Oracle and Teradata pin certain tables into memory. In this scenario HANA will operate exactly as BLU does with the constraint that any single column that in a compressed form exceeds 200GB will not be able to be processed.

Finally let’s configure a system with 5TB of memory per SAP’s recommendation for HANA. In this case BLU and HANA both fit all of the data in-memory… with 2.5TB of compressed user data in and 2.5TB of work space… and there is no I/O. This is an in-memory DBMS.

But according to the IBM Power 770 spec (here) there is no way to get 5TB of memory on a single p770 node… so to match HANA and eliminate all I/O they would require two nodes… but BLU cannot be deployed on a cluster… so on they would have to deploy on a single node and perform I/O on 20% of the data. The latency for SSD I/O is 200Kns and for disk it is 10Mns… for DRAM it is 100ns and HANA loads full cache lines so that the average latency is under 20ns… so the penalty paid by BLU is severe and it will never keep up with HANA.

There is more bunk around recommendations for the number of cores but I can make no sense of it at all so I do not know where to begin to debunk it. SAP recommends high-end Intel servers to run HANA. In the scenario above we would recommend multiple servers… soon enough there will be Haswell servers with 6TB of DRAM and this case will run on one node.

I have stated repeatedly that anytime a vendor presents a slide comparing their product to their competitors you should immediately throw them out… it will always be twisted. Don’t trust them. And don’t trust me as I work for SAP. But hopefully you can see some logic in my case. If you need an IMDB then you need memory. If you are short of memory then the IMDB operates like a columnar RDBMS with a memory cache. If you are running a BI query workload then you need to pin data in the cache or the system will thrash. Because of this SAP recommends that you get enough memory to get all of the data in… we recommend that you operate our in-memory database product in-memory…

This really the point of the post. The Five Minute Rule informs us about what data should be in-memory (see here). An in-memory database is designed from the bottom up to manage hot data in-memory. The in-memory add-ons being offered over legacy systems are very capable and should not be ignored… and as the price of memory drops the Five Minute Rule will suggest that data in-memory will account for and ever larger percentage of your EDW. But to offer an in-memory capability and recommend that you should keep the bulk of the data on disk is silly… and to state that your product has a competitive advantage because you do not recommend that all of the data managed by your in-memory feature be kept in-memory is silliness squared.

DB2 BLU vs. Netezza… and the Winner is…

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I wondered here how IBM would position DB2 with BLU versus Netezza. Please have a look before you go on… and let me admit here and now that when I wrote this I chickened out. As I sat down this time I became convinced that I should predict the end of Netezza.

Why?

In the post here Bob Picciano, the general manager of IBM’s Information Management Software Division, made it nearly clear. He said that DB2 BLU is for systems “under 50 terabytes” only because BLU does not cluster. I suspect that if IBM converted all of the Netezza clusters with under 50TB of data to BLU it would knock out 70% or more of the Netezza install base. He states that “most data warehouses are in the under-10-terabyte range”… and so we can assume that Netezza, precluded from anything under 50TB, has a relatively small market left. He suggests that Netezza is for “petabyte-size collections”… but as I suggested here (check out the picture!), Hadoop is going to squeeze the top away from Netezza… while in-memory takes away the bottom… and IBM is very much into Hadoop so the take-away will not require a fight. Finally, we can assume, I think, that the BLU folks are working on a clustered version that will eat more from the bottom of Netezza’s market.

We should pay Netezza some respect as it fades. When they entered the market Teradata was undisputed. Netezza did not knock out the champ but, for the first time, they proved that it was possible to stay in the ring… and this opened the market for Exadata, Greenplum, Vertica and the rest.

Who is How Columnar? Exadata, Teradata, and HANA – Part 2: Column Processing

In my last post here I suggested that there were three levels of maturity around column orientation and described the first level, PAX, which provides columnar compression. This apparently is the level Exadata operates at with its Hybrid Columnar Compression.

In this post we will consider the next two levels of maturity: early materialized column processing and late materialized column processing which provide more I/O avoidance and some processing advantages.

In the previous post I suggested a five-column table and depicted each of those columns oriented on disk in separate file structures. This orientation provides the second level of maturity: columnar projection.

Imagine a query that selects only 4 of the five columns in the table leaving out the EmpFirst column. In this case the physical structure that stores EmpFirst does not have to be accessed; 20% less data is read, reducing the I/O overhead by the same amount. Somewhere in the process the magic has to be invoked that returns the columns to a row orientation… but just maybe that overhead costs less than the saving from the reduced I/O?

Better still, imagine a fact table with 100 columns and a query that accesses only 10 of the columns. This is a very common use case. The result is a 9X reduction in the amount of data that has to be read and a 9X reduction in the cost of weaving columns into rows. This is columnar projection and the impact of this far outweighs small advantage offered by PAX (PAX may provide a .1X-.5X, 10%-50%, compression advantage over full columnar tables). This is the advantage that lets most of the columnar databases beat Exadata in a fair fight.

But Teradata and Greenplum stop here. After data is projected and selected the data is decompressed into rows and processed using their conventional row-based database engines. The gains from more maturity are significant.

The true column stores read compressed columnar data into memory and then operate of the columnar data directly. This provides distinct advantages:

  • Since data remains compressed DRAM is used more efficiently
  • Aggregations against a single column access data in contiguous memory improving cache utilization
  • Since data remains compressed processor caches are used more efficiently
  • Since data is stored in bit maps it can be processed as vectors using the super-computing instruction sets available in many CPUs
  • Aggregations can be executed using multiplication instead of table scans
  • Distinct query optimizations are available when columnar dictionaries are available
  • Column structures behave as built-in indexes, eliminating the need for separate index structures

These advantages can provide 10X-50X performance improvements over the previous level of maturity.

Summary

  • Column Compression provides approximately a 4X performance advantage over row compression (10X instead of 2.5X). This is Column Maturity Level 1.
  • Columnar Projection includes the advantages of Column Compression and provides a further 5X-10X performance advantage (if your queries touch 1/5-1/10 of the columns). This is Column Maturity Level 2.
  • Columnar Processing provides a 10X+ performance improvement over just compression and projection. This is Column Maturity Level 3.

Of course your mileage will vary… If your workload tends to touch more than 80% of the columns in your big fact tables then columnar projection will not be useful… and Exadata may win. If your queries do not do much aggregation then columnar processing will be less useful… and a product at Level 2 may win. And of course, this blog has not addressed the complexities of joins and loading and workload management… so please do not consider this as a blanket promotion for Level 3 column stores… but now that you understand the architecture I hope you will be better able to call BS on the marketing…

Included is a table that outlines the maturity level of several products:

Product

Columnar Maturity Level

Notes

Teradata

2

 Columnar tables, Row Engine
Exadata

1

 PAX only
HANA

3

 Full Columnar Support
Greenplum

2

 Columnar tables, Row Engine
DB2

3

 BLU Hybrid
SQL Server

2

 I think… researching…
Vertica

3

 Full Columnar Support
Paraccel

3

 Full Columnar Support
Netezza

n/a

 No Columnar Support
Hadapt

2

 I think… researching…