This latest piece of research just came out on Google’s Research Buzz page. It’s about a system called Dremel (note: Dremel is a company building hardware tools which I happened to use a lot when I was building model R/C airplanes as a kid). Dremel is an interesting move by Google which provides a system for interactive analysis of data. It was created because it was thought that native MapReduce has too much latency for for fast interactive querying/analysis. It uses data that sits on different storage systems like GFS or BigTable. Data is modeled in a columnar, semi-structured format and the query language is SQL-like with extensions to handle the non-relational data model. I find this interesting – below is my analysis of what Dremel is and the big conclusion.

Main characteristics of the system:

Data & Storage Model
• Data is stored in a semi-structured format. This is not XML, rather it uses Google’s Protocol Buffers. Protocol Buffers (PB) allow developers to define schemas that are nested.
• Every field is stored in its own file, i.e. every element of the Protocol Buffers schema is columnar-ized. Columnar modeling is especially important for Dremel for two specific reasons:
- Protocol Buffer data structures can be huge (> 1000 fields).
- Dremel does not offer any data modeling tools to help break these data structures down. E.g. there’s nothing in the paper that explains how you can take a Protocol Buffers data structure and break it down to 5 different tables.
• Data is stored in a way that makes it possible to recreate the orignial “flat” schema from the columnar representation. This however requires a full pass over the data – the paper doesn’t explain how point or indexed queries would be executed.
• There’s almost no information about how data gets in the right format, how is it stored, deleted, replicated, etc. My best guess is that when someone defines a Dremel table, data is copied from the underlying storage to the local storage of Dremel nodes (“leaf nodes”) and at the same time is replicated across the leaf nodes. Since data in Dremel cannot be updated, once a table is replicated it doesn’t need to be deleted, which probably simplifies the design & implementation.

Interface
• Query interface is SQL-like but with extensions to handle the semi-structured, nested nature of data. Input of queries is semi-structured, and output is semi-structured as well. One needs to get used to this since it’s significantly different from the relational model.
• Tables can be defined from files, e.g. stored in GFS by means of a “DEFINE TABLE” command.
• The data model and query language makes Dremel appropriate for developers; for Dremel to be used by analysts or database folks, a different/simpler data model and a good number of tools (for loading, changing the data model etc) would be needed.

Query Execution
• Queries do NOT use MapReduce, unlike Hadoop query tools like Pig & Hive.
• Dremel provides optimizations for sequential data access, such as async I/O & prefetching.
• Dremel supports approximate results (e.g. return partial results after reading X% of data – this speeds up processing in systems with 100s of servers or more since you don’t have to wait for laggards).
• Dremel can use replicas to speed up execution if a server becomes too slow. This is similar to the “backup copies” idea from the original Google MapReduce paper.
• There seems to be a tree-like model of executing queries, meaning that there are intermediate layers of servers between the leaf nodes and the top node (which receives the user query). This is useful for very large deployments (e.g. thousands of servers) since it provides some intermediate aggregation points that reduce the amount of data that needs to flow to any single node.

Performance & Scale
• Compared to Google’s native MapReduce implementation, Dremel  is two orders of magnitude faster in terms of query latency. As mentioned above, part of the reason is that the Protocol Buffers are usually very large and Dremel doesn’t have a way to break those down except for its columnar modeling. Another reason is the low latency of Google’s MapReduce implementation.
• Following Google’s tradition, Dremel was shown to scale reasonably well to thousands of servers although this was demonstrated only over a single query that parallelizes nicely and from what I understand doesn’t reshuffle much data. To really understand scalability, it’d be interesting to see benchmarks with a more complex workload collection.
• The paper mentions little to nothing about how data is partitioned across the cluster. Scalability of the system will probably be sensitive to partitioning strategies, so that seems like a significant omission IMO.

So the big question – can MapReduce itself handle fast, interactive querying?
• There’s a difference between the MapReduce paradigm, as an interface for writing parallel applications, and a MapReduce implementation (two examples are Google’s own MapReduce implementation, which is mentioned in the Dremel paper, and open-source Hadoop). MapReduce implementations have unique performance characteristics.
• It is well known that Google’s MapReduce implementation & Hadoop’s MapReduce implementation are optimized for batch processing and not fast, interactive analysis. Besides the Dremel paper, look at this Berkeley paper for some Hadoop numbers and an effort to improve the situation.
• Native MapReduce execution is not fundamentally slow; however Google’s MapReduce and Hadoop happen to be oriented more towards batch processing. Dremel tries to overcome that by building a completely different system that speeds interactive querying. Interestingly, Aster Data’s SQL-MapReduce came about to address this in the first place and offers very fast interactive queries even though it uses MapReduce. So the idea that one needs to get rid of MapReduce to achieve fast interactivity is something I disagree with – we’ve shown this is not the case with SQL-MapReduce.

Syndicated via RSS From: http://www.asterdata.com/blog

I find this development interesting, not only because it makes scientific applications in the cloud a possibility, but also because it’s an indication of where cloud infrastructure is heading.

In the early days, Amazon EC2 was very simple: if you wanted 5 “instances” (that is, 5 virtual machines), that’s what you got. However, memory of the instances was low, as well as disk capacity. Over time, more and more configurations were added and now one can choose an instance type from a variety of disk & memory characteristics with up to 15GB of memory and 2TBs of disks per instance. However, network was always a problem independently of the size of the instance. (According to rumors, EC2 would make things worse by distributing instances as far away from each other as possible in the datacenter to increase reliability – as a result, network latency would suffer.) Now, the network problem is being solved by means of these special “Cluster Compute Instances” that provide guaranteed, non-blocking access to a 10GbE network infrastructure.

Overall this course represents a departure from the super-simple black-box model that EC2 started from. Amazon – wisely – realizes that accommodating more applications requires transparency – and providing guarantees – for the underlying infrastructure. Guaranteeing network latency is just the beginning: Amazon has the opportunity add much more options and guarantees around I/O performance, quality of service, SSDs versus hard drives, fail-over behavior etc. The more options & guarantees Amazon offers the closer we’ll get to the promise of the cloud – at least for resource-intensive IT applications.

Syndicated via RSS From: http://www.asterdata.com/blog

Surprisingly though, several vendors went down the path of making their data warehousing offerings fatter and fatter. Greenplum is a good example. Prior to Sun’s acquisition by Oracle, they were heavily pushing systems based on the Sun Thumper, a 48-disk-heavy 4U box that can store up to 100TBs/box. I was quite familiar with that box as it partly came out of a startup called Kealia that my Stanford advisor, David Cheriton, and Sun co-founder Andy Bechtolsheim had founded and then sold to Sun in 2004. I kept wondering, though, what a 50TB/CPU configuration has to do with data analytics.

After long deliberation I came to the conclusion that it has nothing to do with it. There were two reasons why people were interested in this configuration. First, there were some use cases that required “near-line storage”, a term that’s used to describe a data repository whose major purpose is to store data but also allows for basic & infrequent data access. In that respect, Greenplum’s software on top of the Sun Thumpers represented a cheap storage solution that offered basic data access and was very useful for applications where processing or analytics was not the main focus.

The second reason for the interest, though, is a tendency to drive DW projects towards an absolute low per-TB price to reduce costs. Experienced folks will recognize that such an approach leads to disaster, because – as mentioned above – analytics is more than just Terabytes. Perfectly low per-TB price using fat storage looks great on glossy paper but in reality it’s no good because nobody’s analytical problems are that simple.

The point here is that analytics have more to do with processing rather than storage. It requires a fair number of balanced servers (thus good scalability & fault tolerance), CPU cycles, networking bandwidth, smart & efficient algorithms, fair amounts of memory to avoid thrashing etc. It’s also about how much processing can it be done by SQL, and how much of your analytics need to use next-generation interfaces like MapReduce or pre-packaged in-database analytical engines. In the new decade in which we’re embarking, solving business problems like fraud, market segmentation & targeting, financial optimization, etc., require much more than just cheap, overweight storage.

So going to the EMC/Greenplum news, I think such an acquisition makes sense, but in a specific way. It will lead to systems that live between storage and data warehousing, systems able to store data and also give the ability to retrieve it on an occasional basis or if the analysis required is trivial. But the problems Aster is excited about are those of advanced in-database analytics for rich, ad hoc querying, delivered through a full application environment inside a MPP database. It’s these problems that we see as opportunities to not only cut IT costs but also provide tremendous competitive advantages to our customers. And on that front, we promise to continue innovating and pushing the limits of technology as much as possible.

Syndicated via RSS From: http://www.asterdata.com/blog

Today there in research and development in major software organizations from IBM to Google to Apple about the crowdsourcing model of innovation. Infact Apple had adopted to this model and the success of iPhone is a very shining example. So did Google with Android platform, today there are scores of applications for this platform all over the world.

Software as we know it and Business as we knew it will change in the next five years to a total model of crowd driven innovation and growth model. Your Business is going Social, adopt to the new ways of the new world.

Syndicated via RSS From: http://www.b-eye-network.in/blogs/krishnan/

For 2011, EDBT is held in Uppsala, Sweden in March. I’m on the Program Committee for the “industrial application” section and I’d like to encourage anyone with an interesting commercial technology and an interest in Europe to consider submitting a paper to the conference. Papers on applications and position papers on technology trends are equally welcome. The deadline for submission is September 8, 2010 and you can find more info on submitting here.

Syndicated via RSS From: http://www.asterdata.com/blog

There are several things to understand in this move

1. Majir mover advantage for a commercial cloud provision – apart from Amazon
2. EMC establishing its presence as a player in the DWBI and Analytics space
3. The fact that BI in the cloud is a reality
4. The new platform for DWBI and Analytics may move out of SQL
5. DWBI as we know it will change

As we start moving from a SQL intensive DWBI to a non-SQL intensive BI, we will start looking at these kinds of platforms for a new alternative. Good news for all technology enthusiasts and Analytical BI users.

Another fact is now the DW Appliances Space has very few pure-play vendors – Netezza vs. AsterData is certainly the next battle

Syndicated via RSS From: http://www.b-eye-network.in/blogs/krishnan/

I think it does. Historically, a lot of data analysis in the enterprise has been done with pure SQL. SQL-based analysis is a type of “relational analysis,” which I define as analysis done via a set-based declarative language like SQL. Note how SQL treats every table as a set of values; SQL statements are relational set operations; and any intermediate SQL results, even within the same query, need to follow the relational model. All these are characteristics of a relational analysis language. Although recent SQL standards define the language to be Turing Complete, meaning you can implement any algorithm in SQL, in practice implementing any computation that departs from the simple model of sets, joins, groupings, and orderings is severely sub-optimal, in terms of performance or complexity.

On the other hand, an interface like MapReduce is clearly non-relational in terms of its algorithmic and computational capabilities. You have the full flexibility of a procedural programming language, like C or Java; MapReduce intermediate results can follow any form; and the logic of a MapReduce analytical application can implement almost arbitrary formations of code flow and data structures. In addition, any MapReduce computation can be automatically extended to a shared-nothing parallel system which implies ability to crunch big amounts of data. So MapReduce is one version of “non-relational” analysis.

So Aster Data’s SQL-MapReduce becomes really interesting if you see it as a way of doing non-relational analytics on top of relational data. In Aster Data’s platform, you can store your data in a purely relational form. By doing that, you can use popular RDBMS mechanisms to achieve things like adherence to a data model, security, compliance, integration with ETL or BI tools etc. The similarities, however, stop there. Because you can then use SQL-MapReduce to do analytics that were never possible before in a relational RDBMS, because they are MapReduce-based and non-relational and they extend to TBs or PBs. And that includes a large number of analytical applications like fraud detection, network analysis, graph algorithms, data mining, etc.

Syndicated via RSS From: http://www.asterdata.com/blog

So what do you do with all that memory? There are two classes of use cases that are emerging today. First is the case where you need to increase concurrent access to data with reduced latency. Tools like memcached offer in-memory caching that, used properly, can vastly improve latency and concurrency for large-scale OLTP applications like websites. Also the nice thing with object caching is that it scales well in a distributed way and people have build TB-level caches. Memory-only OLTP databases have started to emerge, such as VoltDB. And memory is used implicitly as a very important caching layer in open-source key-value products like Voldemort. We should only expect memory to play a more and more important role here.

The second way to use memory is to gain “processing flexibility” when doing analytics. The idea is to throw your data into memory (however much it fits, of course) without spending much time thinking how to do that or what queries you’ll need to run. Because memory is so fast, most simple queries will be executed at interactive times and also concurrency is handled well. European upstart QlikView exploits this fact to offer a memory-only BI solution which provides simple and fast BI reporting. The downside is its applicability to only 10s of GBs of data as Curt Monash notes.

By exploiting an MPP shared-nothing architecture, Aster Data has production clusters with TBs of total memory. Our software takes advantage of memory in two ways: first, it uses caching aggressively to ensure the most relevant data stays in memory; and when data is in memory, processing is much faster and more flexible. Secondly, MapReduce is a great way to utilize memory as it provides full flexibility to the programmer to use memory-focused data structures for data processing. In addition, Aster Data’s SQL-MapReduce provides tools to the user to encourage the development of memory-only MapReduce applications.

However, one shouldn’t fall into the trap of thinking that all analytics will be in-memory anytime soon. While memory is down to $30/GB, disk manufacturers have been busy increasing platter density and dropping their price to less than $0.06/GB. Given that the amount of data in the world grows faster than Moore’s law and memory, there will always be more data to be stored and analyzed than what fits into any amount of memory that an enterprise can use. In fact, most big data applications will have data sets that do not fit into memory because, while tools like memcached worry only about the present (e.g. current Facebook users), analytics need to worry about the past, as well – and that means much more data. So a multi-layer architecture will be the only cost-effective way of analyzing large amounts of data for some time.

One shouldn’t be discussing memory without mentioning solid-state disk products (like Aster Data partner company Fusion-io). SSDs are likely to make the surprise here given that their per-GB price is falling faster than disks (being a solid-state product that follows Moore’s law does help). In the next few years we’ll witness SSDs in read-intensive applications providing similar advantages to memory while accommodating much larger data sizes.

Syndicated via RSS From: http://www.asterdata.com/blog

We’re definitely talking about a different era. In the old days, you could barely fit a single core in a chip. (I still remember 15 years ago when I had to buy and install a separate math co-processor on my Mac LC to run Microsoft Excel and Mathematica.) And with the hardware, software has to change, too. In fact, modern software means software that can handle parallelism. This is what makes MapReduce such an essential and timely tool for big data applications. MapReduce’s purpose in life is to simplify data and processing parallelism for big data applications. It gives ample freedom to the programmer on how to do things locally; and takes over when data needs to be communicated across processes/cores/servers, thus evaporating a lot of the parallelism complexity.

Once someone designs their software and data to operate in a parallelized environment using MapReduce, gains will come on multiple levels. Not only will MapReduce help your analytical applications scale across a cluster of servers with terabytes of data, it will also exploit the billions of transistors and the 10s of CPU cores inside each server. The best part: the programmer doesn’t need to think about the difference.

As an example, consider this great paper out of Stanford discusses MapReduce implementations of popular Machine Learning algorithms. The Stanford researchers considered MapReduce as a way of “porting” these algorithms (traditionally implemented to run in a single CPU) to a multi-core architecture. But, of course, the same MapReduce implementations can be used to scale these algorithms across a distributed cluster as well.

Hardware has changed – MPP, shared-nothing, commodity servers, and, of course, multi-core. In this new world MapReduce is Software’s response for big data processing. Intel and Westmere have just found an unexpected friend.

Syndicated via RSS From: http://www.asterdata.com/blog

HP StorageWorks D2D4000 Backup System wins High Honors in Data Protection

Montana State University (MSU) Bozeman is the main campus of the Montana State University System, and the state’s land grant university. The 1,170-acre MSU Bozeman campus sits at an elevation of 4,900 feet on the south side of the scenic city. The Information Technology Center (ITC) on the Bozeman campus is the main computing center for the university system. While individual university departments have their own mail servers and local files, the ITC is responsible for maintaining critical university-wide databases and mail servers.

Availability of services is the No. 1 business level goal for the ITC. “Four different campuses around the state are dependent on our databases,” says Mike Hall, UNIX® Systems Administrator. “They are out of business without those databases. We have all the student transcripts and employee information, and hear about it quickly if that information is not available. But with a solid disaster-recovery system in place, we can still do business even if we have a catastrophe.

Complete the form below to access this White Paper from HP: