Yesterday Oracle announced a major deal with Dell and HP: the two OEMs will certify and resell the Oracle VM virtual infrastructure, along with Oracle Solaris x86 and Oracle Enterprise Linux.
Those customers that will buy Dell’s and HP’s Oracle solutions will have full access to the Oracle Premier Support.

It sounds odd considering the Oracle’s tagline adopted after the acquisition of Sun, Software. Hardware. Complete., to push the idea of an end-to-end computing stack available from a single vendor. But it’s understandable that this is an attempt to increase the Oracle VM market share.
And, market share or not, Oracle VM Server now officially becomes the fourth hypervisor available out-of-the-box in industry standard servers, side by side with Citrix XenServer, Microsoft Hyper-V and VMware ESX.

It will be interesting to see how this will impact the Oracle presence one year from now.

Labels: Alliances, Dell, HP, Oracle

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The Forrester reports stated that Oracle CRM On Demand “has achieved success in the market by offering the same benefits as other CRM SaaS solutions: quick time-to-value, strong usability, and low upfront costs. This value proposition attracts midsized organizations, and it is finding increasing acceptance in units of large companies.” The reports also praised Oracle’s “innovative” features, including social CRM. Oracle’s social CRM helps increase collaboration within organizations. It also helps companies deliver differentiated sales and services. The report continued to say, “The product offers strong sales force automation (SFA) capabilities and partner channel management, and it provides sound support for customer service, customer data management, and analytics.”

Oracle’s Siebel CRM was named a leader for large organizations. The report said, “Siebel CRM is designed to provide robust capabilities that empower users to better address customer needs… the Siebel CRM product for large organizations has achieved best-of-breed status for most CRM functionalities, including: sales, marketing, customer service, field service, partner relationship management, and customer data management.”

Oracle is delighted to have been recognized by Forrester. The SVP of Oracle CRM, Anthony Lye, said, “Oracle CRM enables organizations of all sizes to transform their customer experience by promoting customer acquisition, increasing loyalty and lowering the cost of marketing, selling and servicing. We are pleased that Forrester recognized Oracle for its diverse range of solutions, targeted industry expertise, and flexible deployment options.”

Oracle’s E-Business Suite CRM and Oracle’s PeopleSoft Enterprise CRM were also mentioned in the Forrester Report as good ERP options. The E-Business suite provides easy integration and is useful in sales compensation management. PeopleSoft Enterprise CRM also provides strong integration benefits.

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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.

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