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The Third Generation of Database Technology - Part III (Final)

fredho66 Tags: ids ibm idc database ‎ 4,576 Views

In this final part of Carl Olofson’s paper on “The Third Generation of Database Technology”, I am summarizing the key points in the remainder of his paper, taking liberties to remove much of the detailed descriptions of each vendor, and jumping to his thoughts on Market Strategies, Technology Assessment and Essential Guidance.

It should be apparent to the reader by now that I chose to spend the time on this paper (versus many other papers available on the subject) because it validates a lot of the work that we are currently pursuing within the Research and Development teams at IBM. In addition to the incremental functionalities in each new release, we have to pay particular attention to where the market is headed, both in terms of customer needs and in terms of technology advances. Many of the technologies discussed are already available in the IBM Information Management portfolio, as you will see them mentioned below, and others are being actively developed, though not ready for public announcement. The challenge will always be getting the multiple pieces properly integrated and getting them through the product release process.

Vendors and Products of the “3^rd Generation Database Technology” (in Alphabetic Order)

Only the vendor and product names are listed here. You can find technologies associated with each vendor in Part II of my blog entry on this topic.

Amazon – SimpleDB

ENEA – Polyhedra

Four Js – Genero DB

Google – Bigtable

IBM – SolidDB, Streams

Infobionics – Knowledge Server

Ingres – VectorWise

McObject – eXtremeDB

Microsoft – SQL Azure

Oracle – TimesTen, Exadata Storage Server

ParAccel – PADB

RainStor – RainStor cell-based RDBMS

Sybase – ASE IMDB

Vertica – FlexStor

Market Strategies

The author believes that fundamentally different approaches to data definition, storage, manipulation, and delivery is needed to provide for more, better, faster, cheaper database management. The desire to acquire and use business analytics in moment-by-moment decision making, accumulate and reconcile large amounts of enterprise data for reporting and analysis, streamline operations with better and more precise data collection and movement all result in demand for DBMS technologies that deliver orders of magnitude better performance, with much higher reliability, than what the leading conventional products can offer.

Technology Assessment

As stated in previous parts of this blog entry, the author believes that the DBMS industry has long been held back by the relational paradigm, which has proven to be both a blessing and a curse. The blessing is that its simplicity and broad applicability have enabled DBMS technology to become the standard way that business application is stored and managed, which brings order and manageability to both the data and the applications that use it. The curse is that relational databases are unable to hold semantic metadata or directly represent data organization concepts such as multidimensionality, containment, derivation, recursion, or collection. This limitation can force DBAs to store data that reflects such concepts in arcane combinations of cross-reference tables that require multiple complex joins to navigate, and to encapsulate the management of such table relationship combinations in program code or stored procedures both of which, in the absence of detailed documentation, tend to mask the actual nature of the way the data is logically organized. [Though readers familiar with IDS’ extensibility features will know that IDS has solved some of these problems for many years]

The use of Web services and the growth in event-driven architectures (EDAs) are diminishing the need for SQL support in favor of the publication of data services that can be triggered or invoked in a SOA or EDA context. Freeing database management from the limitations imposed by SQL, even at the interface level, can further accelerate the development of DBMS technology that is, from the current perspective, truly revolutionary.

Essential Guidance (Carl Olofson’s conclusions)

The DBMS world is dominated by second-generation disk-based RDBMS technologies today. The third-generation technologies will probably not displace the existing disk-based RDBMS products overnight. In fact, it is more likely that the leading RDBMS products will evolve to include some of these technologies, and the leading vendors may acquire third-generation DBMS companies to add to their data management portfolios.

Third-generation technologies will arise in a market that acknowledges that one size truly does not fit all, and that some existing RDBMS technologies, perhaps with third-generation enhancements, remain perfectly appropriate for a range of OLTP and data warehousing workloads. Other workloads, such as those that depend on the rapid capture and processing of streaming data, involve the collection and analysis of vast amounts of heterogeneous data, or support the highly variable demands of the public cloud, will require these emerging technologies, often deployed in specialized, workload-specific ways.

Comments welcomed.

The Third Generation of Database Technology – Part II

fredho66 Tags: dbms nonschematic columnar imdb clusters ‎ 5,046 Views

This is a continuation of the entry last week where I summarized IDC’s paper on “The Third Generation of Database Technlogy: Vendors and Products That Are Shaking up the Market” by Carl Olofson. Those interested in Part I should refer to the previous entry on this topic.

The Third Generation

The economics of computing have changed in the 2000s such that multi-core processors are common. 4 and 8 way systems, each with dual core processors, are common for even moderate workloads. 64-bit technology is taking over for enterprise servers, especially for database servers, and memory is cheap. Even though disks are also cheaper and faster than ever before, reading and writing whole records on disk represent a drag on overall system performance. Clever adaptations to reduce I/O help, but don’t really eliminate the problem. Third generation DBMS products began to emerge in the late 1990’s and are now beginning to supplant second-generation products in significant ways.

In-Memory Database Technology

Instead of conventional disk-based RDBMS, which involves mapping buffer contents to segments that, in turn, represent pages (disk blocks), with database keys that must be translated and mapped for the data to be located, in-memory databases simply manage blocks of main memory, using memory addresses as direct pointers to the data. This technique eliminates the I/O drag of disk-based data and also claims to reduce the instruction path length of a typical database operation by anywhere from 20 to 200 times.

An IMDB is not simply a shared memory cache, and its contents cannot be accessed directly by applications. Instead, applications access the data through the services of the DBMS as exposed in an API, which may be proprietary, or most commonly a relational convention such as SQL, ODBC, or JDBC. This ensures that not only the application can’t corrupt the database but the database contents are controlled by the IMDB, which uses various techniques to ensure a consistent view of data to each application, and, in most cases, manages multiple concurrent sessions.

A common misconception regarding IMDB is that it lacks the ACID properties of a transactional database. This is not true. Most IMDB implementations used for transaction processing still have transaction logs for error recovery, and can stream the logs to physically persistent storage such as SSM or spinning disk. A few provide full recoverability by nonlogging means. They also commonly replicate their memory contents to other servers, to provide high availability functionality through failover support.

Examples of DBMS products that include this technology are Oracle TimesTen, Sybase ASE 15.5 IMDB, ENEA’s Polyhedra, GeneroDB from Four Js, Xcelerix IMDB from Frontex, IBM’s SolidDB, eXtremeDB from McObject, and VoltDB.

Peer-to-Peer Clusters

DBMS servers are using peer-to-peer clusters to provide a hybrid of failover and scalability support, spreading workloads across many servers and exchanging small units of data at high rates of speed to execute database operations. This is an especially useful approach when the workload mainly consists in access to a large number of read-only tables, but a small number of updatable tables, and where the transactions do not require a high degree of data sharing for execution, such as classic data entry operations.

Examples of products that include this technology include ParAccel, VoltDB and (Informix Mach11).

Columnar Data Storage

Storing table rows as blocks with selected indexed columns is a spectacularly inefficient approach for databases that are commonly used for statistical analysis, such as data warehouses. This is because many data warehouse operations scan tables, performing aggregate operations on the data, usually in select columns, and because when they randomly select rows, it is normally for only a few columns in that row, and not for the whole row.

Columnar data storage involves storing tables as blocks by column rather than by row. This offers several advantages for analytic databases. One advantage is that operations on a column of data can be carried out with very few I/O operations. Another is that since the column contains all data of the same type, it can very easily compressed to a tiny fraction of its size by using indexing to eliminate duplicate values and then compressing the values themselves. Once that is done, any random select on the table will result in a very quick result because every column is, in effect, indexed. Finally, the index structures that define the columns can themselves be cross-indexed, further optimizing access.

Examples of products that incorporate this technology include Oracle Exadata Database Machine V2, Sybase IQ, Vertica, ParAccel, and VectorWise from Ingres.

Nonschematic DBMS

Nonschematic DBMS may be one of the most controversial and least understood of the 3^rd generation DBMS technologies, though it is based on an idea that is not all that new. It could be argued that every native XML DBMS is really a nonschematic DBMS, in that it dynamically builds index structures based on the tag structures found in the XML documents that are loaded into it, and uses those index structures to organize the data. (Kevin Brown and I have had several discussions with one of the vendors mentioned below to assess its suitability with IDS)

A nonschematic DBMS is one that stores data as a complex of key-value pairs, building cross-pair structures, such as table rows, based on the order inherent in the data presented to it The user can then use tools to discover structures inherent in the data, and derive a schema from those structures.

Nonschematic DBMS could be used as a tool in preparing data to be loaded into a data warehouse, but would make a poor platform for the warehouse itself. It is best used for managing semi-structured data, such as XML, and for aggregating large amounts of data from many sources for performing search and discovery operations. It seems to have a bright future for a variety of applications that may be offered as services on the Internet and in cloud environments.

Examples of products with this technology include Google’s BigTable, Infobionics Knowledge Server, and Amazon’s SimpleDB. Among general-purpose XML DBMS vendors, the pure-plays include Raining Data (TigerLogic XDMS), Software AG (webmethods Tamino), and Xpriori (XMS). A number of leading relational DBMS products also offer native XML support, including IBM DB2, Oracle, and Sybase ASE.

Cloud Technology

Cloud computing is based on the principles of utility computing, resource virtualization, and an approach to shared resource management, such as multitenancy, that enables a service to offer to users the illusion of limitlessly expandable abstracted resources (memory, processing power, etc.). To fit into such as framework, DBMS technology must also offer virtualization, horizontal scalability, and multitenancy.

These capabilities can be offered through server and storage resources that are tightly controlled, or through virtualization techniques that cooperates with other external resource management systems. A third approach is to encapsulate these capabilities within a physical environment that is directly controlled by the vendor, offering DBMS functionality as cloud services.

Only a few DBMS products feature dynamic, virtualized hardware and software resource management for scaling processing power or storage up or down without manual reconfiguration. Examples of products that explicitly use this technology include Amazon SimpleDB and Microsoft SQL Server Azure.

Part III of this entry will discuss some of the vendors mentioned so far and what specific products that fit into the categories discussed so far. Still further out, there will be discussion on what IBM and Informix are doing with these technologies.

The 3rd Generation of Database Technology - Part I

fredho66 Tags: database idc in-memory ibm columnar ‎ 3 Comments ‎ 13,520 Views

This entry is primarily a recap of a recent article published by IDC (Carl Olofson) titled "The Third Generation of Database Technology: Vendors and Products That Are Shaking Up the Market". My disclaimer for this entry is that I cannot attach the original article published by IDC (due to copyright). Therefore, I will attempt to summarize and discuss the salient points in the article. It will not be a complete summary or review of the article, due to time and relevance to this audience. At times, I may inject my own thoughts and comments (highlighted). I am hoping to make people aware of some of the competitive products already in the marketplace and others along the way (including IBM and Informix). Part I will cover the 1st and 2nd generation technology, while Part II (the good stuff) will cover the 3rd generation systems.

The premise of the article is that a new generation of DBMS technology is sending a simple message to current generation of DBA's and users that "Everything you know is wrong". These new systems will encourage you to forget disk-based partitioning schemes, indexing strategies, and buffer management. It will embrace a world of large-memory models, multi-core processors, clustered servers, and highly compressed columnwise storage. He further predicts that wihin 5 years:

Most data warehouses will be stored in a columnar fashion.
Most OLTP databases will either be segmented by an in-memory database or reside entirely in memory.
Most large-scale database servers will achieve horizontal scalability through clustering.
Many data collection and reporting problems will be solved with databases that have no formal schema at all.

The First Generation

The first generation of database technology started in the 60's and continued into the 70's. It served two distinct purposes: 1) to enable disparate but related applications to share their data by a means other than passing files back and forth, and 2) to provide a platform for independent data query and reporting that did not require custom code.

At the time, computer programs typically ran in batch mode, so the databases were linked to applications as layers of data organization and indexing between the application code and the file system. Because memory was extremely limited, these systems lacked generalized data access support beyond services that required very explicit data structure navigation, which was coded directly into the application.

Each database management system was different, having its own style for organizing data, and its own set of services or access languages (DDL and DML) that were quite distinct and completely incompatible. This meant that once one developed an application for one of the DBMSs, that application was "locked in". A DBMS migration would require a total application rewrite.

Because of this "lock in" factor, most of these DBMSs are still actively in use today. These products include IBM's IMS, CA's IDMS and DATACOM, Unisys' DMS II (that I worked on), and Software AG's Adabas. All these products have undergone various forms of "modernization" since the 1970's, including the addition of relational interfaces, and support for service conventions such as SOA. The reason that these products are out of favor is due to the lock in quality as well as the cost and complexity of developing and maintaining these databases.

The Second Generation

A new paradigm began to emerge in 1970 with the publication of A Relational Model of Data for Large Shared Data Banks by Dr. E. F. Codd. His premise was that data should be made accessible by managing it in a catalog structured along the lines of mathematical set theory, presented as attributes of relations organized into tuples. IBM then developed a test DBMS based on Codd's ideas, called System R using jargons like "tables", "columns" and "rows", metaphors made familiar by popular spreadsheet products that were delivering computer power to the masses. IBM made relational DBMS seem comprehensible and accessible to ordinary people.

Relational DBMS offer a standard way of modeling and accessing data that could enable users to break out of the dreaded lock in effect. At first, multiple competing languages were proposed for relational databases. IBM took a generalized query language that it had developed for System/R, called the structured query language (SQL), and extended it to include full DDL and DML capabilities.

By the mid-1980's, users resented lock in at not only the DBMS level but also the operating system level. Most systems, whether mainframes or midrange, were driven by proprietary OSs designed and controlled by their computer vendors. Users were increasingly attracted to what were then called minicomputers, driven by Unix. Such systems were called "open systems".

New RDBMS vendors quickly embraced open systems, pushing the idea that users could get cheaper processing and escape lock in at both the OS and the DBMS level. The were fantastically successful, and companies like Ingres, Oracle, Informix, and Sybase emerged as the major players in the late 1980s and early 1990s.

These second generation DBMS products were well designed for the economic constraints of their times. Memory was expensive, and most systems had one or a few processors. So, they used buffer management techniques designed to minimize memory usage. They tended to manage processing threads based on single processor architectures, and they based their internal data structures on optimal layout on dedicated disk drives that were typically direct attached storage using SCSI connections. Some RDBMS products offered cluster configurations for greater scalability, but these would typically require purpose-built hardware provided by the manufacturer: Teradata, Sequent, and Tandem (where I also worked) were examples of vendors of such products.

Since the 1990s, some RDBMS products have been enhanced with various kinds of alternate clustering support that features shared disk support enabled by cluster file systems deployed on network-attached storage (NAS) or storage area networks (SANs). [Mach-11 anyone?] Some also offer caching techniques, 64-bit buffers, and automated database and query optimization, multi-level partitioning, and some degree of parallel query processing, all designed to deliver incremental benefits over the basic product.

Nonetheless, these products are still based on the core desgin principles that drove their early development, i.e. laying out data on disk for optimal performance by scattering data across volumes, partition data to simplify indexes and maintenance operations, and query optimization options that take full advantage of the way the data is organized on storage. In short, the systems are still essentially focused on so-called spinning disks, and with that comes both limitations of internal operation scalability and the bottlenecks represented by the disk I/O.

Everything up to now should be already familiar to the reader. It does represent an accurate view (as shared by other authors as well) of the current state of database technology. Stayed tuned for Part II of this entry.

Week in Brussels, Belgium for SW IOT Tech Sales Training

fredho66 Tags: iot informix brussels sw ‎ 1 Comment ‎ 6,941 Views

This is Brussels covered with snow from one of the floors at the Sheraton Hotel where the SW IOT Sales and Tech Sales Training was held during the week of 2/8/10-2/12/10. SW IOT is defined as the Southwest region of Europe comprising of countries like Spain, Portugal, France, Italy, Belgium, Netherlands, and Luxembourg. Subjects covered by this internal sales training include Infosphere, Data Management, Optim, and others. Representing Informix were Terri Gerber for Sales and I for Tech Sales. I presented sessions on the following: 1) Overall Informix business and recent references, 2) Preview of Panther features, 3) Informix Warehousing and ISAO, 4) Informix High Availability features and 5) Informix Embeddability.

The first day consisted of a number of general sessions where Alyse Passerali (WW DM Sales VP) talked about the overall state of business and key plays for DM in 2010. This is followed by general sessions on how to use "White Boarding" for Sales. White-boarding is a new process to get our Sales teams in engaging potential customers in an interactive dialogue to learn their business problems and demonstrate our solutions. I sat through each of the sales sessions that Terri Gerber conducted (Spain/Portugal, Benelux, France, Italy0 and answered questions whereever I could.

My technical sales training sessions were quite well attended, with some dedicated IT specialists from France, Italy, Belgium and Spain. Jose Manuel Gallud has always been a great help in the region. I met others including Mario Rocco Garasto, Stefan Van den Borre and Dr. Simone Moncini. I was able to use a number of public references, e.g. Cisco, Jalisco Mexico, DHL-Germany, China Mobile, Peapod, Hilton Hotels, and others.

One of the keynote session was regarding the new solution offered by Guardiam, a recent acquisition specializing in Data Governance. Their solutions work with any database as they do "sniffing" of SQL operations before they get to the database. Their solution usually involves a hardware component, but a software only solution also exists. I've confirmed with our own in-house security expert (Jonathan Leffler) that this is a solution we should be discussing with our Informix customers.

Besides the presentations, I had good interactions with many of the account teams resulting in possible opportunities in the region. If I do return to the region at their request, I hope it will have better weather. With the snow in Brussels, the airport was temporarily closed. Just walking around the hotel was difficult due to the snow and windy conditions. But there were some fantastic food. Of course, some authentic Belgium waffle and Belgium chocolate. On my way back to the U.S., I was delayed in Washington DC for 6 hours, not due to snow, but due to mechanical problems on the plane. They finally switched plane and instead of getting me back around 7:30pm, I got in at 1:30am. in SFO. Add another hour of driving to get home after that.

My First Entry

fredho66 Tags: informix ‎ 5 Comments ‎ 4,046 Views

As a first time blogger, I'm wondering why anyone would want to see another blog. Even for the Informix loyalist, there are many other more knowledgeable and technically savvy people that have blogs on Informix products and other notable developments in the Informix world. So let me start by saying a few words about my past experiences. If that is in any way interesting, perhaps there are some common areas that warrant discussions.

I have been working in the computer industry and primarily in database development in one form or another for some undisclosed number of years. Places I've worked for include Burroughs (for which most people have never heard of), Sperry Univac, HP (for a very short time), Sun (yes, the Oracle one, also for a very short time), Tandem (14 years), Informix (Red Brick for about 3 years) before being acquired by IBM in 2001. Since then, I've managed teams on DWE, Cube Views, IDS Engineering, and in my current position, as manager of the Competitive Technologies and Enablement team.

My area of interest (not necessarily area of expertise) include DBMS, Fault-tolerant systems, Data Warehousing, Business Intelligence, Clusters and High Availability. Over the years, I have been fortunate to work with some real experts in the database area, including 3 of the original 7 authors of the System R paper, who all happenned to have started at IBM Research but were working at Tandem at the time. And at Red Brick, a company originally founded by Ralph Kimball, is where I really understood how data warehousing works.

Feel free to comment if you are interested in any of this. I will discuss more on what the Competitive Team works on in the next comment.

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The Third Generation of Database Technology - Part III (Final)

The Third Generation of Database Technology – Part II

The 3rd Generation of Database Technology - Part I

Week in Brussels, Belgium for SW IOT Tech Sales Training

My First Entry