Analysis of Oracle Exadata and the Oracle Database Machine. Related subjects include:
Vendor lock-in is an important subject. Everybody knows that. But few of us realize just how complicated the subject is, nor how riddled it is with paradoxes. Truth be told, I wasn’t fully aware either. But when I set out to write this post, I found that it just kept growing longer.
1. The most basic form of lock-in is:
- You do application development for a target set of platform technologies.
- Your applications can’t run without those platforms underneath.
- Hence, you’re locked into those platforms.
2. Enterprise vendor standardization is closely associated with lock-in. The core idea is that you have a mandate or strong bias toward having different apps run over the same platforms, because:
- That simplifies your environment, requiring less integration and interoperability.
- That simplifies your staffing; the same skill sets apply to multiple needs and projects.
- That simplifies your vendor support relationships; there’s “one throat to choke”.
- That simplifies your price negotiation.
3. That last point is double-edged; you have more power over suppliers to whom you give more business, but they also have more power over you. The upshot is often an ELA (Enterprise License Agreement), which commonly works:
- For a fixed period of time, the enterprise may use as much of a given product set as they want, with costs fixed in advance.
- A few years later, the price is renegotiated, based on then-current levels of usage.
|Categories: Amazon and its cloud, Buying processes, Cassandra, Exadata, Facebook, IBM and DB2, Microsoft and SQL*Server, MongoDB, Neo Technology and Neo4j, Open source, Oracle, SAP AG||11 Comments|
When I find myself making the same observation fairly frequently, that’s a good impetus to write a post based on it. And so this post is based on the thought that there are many analogies between:
- Oracle and the Oracle DBMS.
- IBM and the IBM mainframe.
And when you look at things that way, Oracle seems to be swimming against the tide.
Drilling down, there are basically three things that can seriously threaten Oracle’s market position:
- Growth in apps of the sort for which Oracle’s RDBMS is not well-suited. Much of “Big Data” fits that description.
- Outright, widespread replacement of Oracle’s application suites. This is the least of Oracle’s concerns at the moment, but could of course be a disaster in the long term.
- Transition to “the cloud”. This trend amplifies the other two.
Oracle’s decline, if any, will be slow — but I think it has begun.
There’s a clear market lead in the core product category. IBM was dominant in mainframe computing. While not as dominant, Oracle is definitely a strong leader in high-end OTLP/mixed-use (OnLine Transaction Processing) RDBMS.
That market lead is even greater than it looks, because some of the strongest competitors deserve asterisks. Many of IBM’s mainframe competitors were “national champions” — Fujitsu and Hitachi in Japan, Bull in France and so on. Those were probably stronger competitors to IBM than the classic BUNCH companies (Burroughs, Univac, NCR, Control Data, Honeywell).
Similarly, Oracle’s strongest direct competitors are IBM DB2 and Microsoft SQL Server, each of which is sold primarily to customers loyal to the respective vendors’ full stacks. SAP is now trying to play a similar game.
The core product is stable, secure, richly featured, and generally very mature. Duh.
The core product is complicated to administer — which provides great job security for administrators. IBM had JCL (Job Control Language). Oracle has a whole lot of manual work overseeing indexes. In each case, there are many further examples of the point. Edit: A Twitter discussion suggests the specific issue with indexes has been long fixed.
Niche products can actually be more reliable than the big, super-complicated leader. Tandem Nonstop computers were super-reliable. Simple, “embeddable” RDBMS — e.g. Progress or SQL Anywhere — in many cases just work. Still, if you want one system to run most of your workload 24×7, it’s natural to choose the category leader. Read more
|Categories: Cloud computing, Database diversity, Exadata, IBM and DB2, Market share and customer counts, Microsoft and SQL*Server, NoSQL, Oracle, Software as a Service (SaaS)||27 Comments|
I last wrote about Couchbase in November, 2012, around the time of Couchbase 2.0. One of the many new features I mentioned then was secondary indexing. Ravi Mayuram just checked in to tell me about Couchbase 4.0. One of the important new features he mentioned was what I think he said was Couchbase’s “first version” of secondary indexing. Obviously, I’m confused.
Now that you’re duly warned, let me remind you of aspects of Couchbase timeline.
- 2 corporate name changes ago, Couchbase was organized to commercialize memcached. memcached, of course, was internet companies’ default way to scale out short-request processing before the rise of NoSQL, typically backed by manually sharded MySQL.
- Couchbase’s original value proposition, under the name Membase, was to provide persistence and of course support for memcached. This later grew into a caching-oriented pitch even to customers who weren’t already memcached users.
- A merger with the makers of CouchDB ensued, with the intention of replacing Membase’s SQLite back end with CouchDB at the same time as JSON support was introduced. This went badly.
- By now, however, Couchbase sells for more than distributed cache use cases. Ravi rattled off a variety of big-name customer examples for system-of-record kinds of use cases, especially in session logging (duh) and also in travel reservations.
- Couchbase 4.0 has been in beta for a few months.
Technical notes on Couchbase 4.0 — and related riffs — start: Read more
|Categories: Cache, Clustering, Couchbase, Data models and architecture, Databricks, Spark and BDAS, Exadata, Hadoop, MarkLogic, MongoDB, MySQL, NoSQL, Open source, Schema on need, Structured documents, Web analytics||1 Comment|
Oracle is announcing today what it’s calling “Oracle Big Data SQL”. As usual, I haven’t been briefed, but highlights seem to include:
- Oracle Big Data SQL is basically data federation using the External Tables capability of the Oracle DBMS.
- Unlike independent products — e.g. Cirro — Oracle Big Data SQL federates SQL queries only across Oracle offerings, such as the Oracle DBMS, the Oracle NoSQL offering, or Oracle’s Cloudera-based Hadoop appliance.
- Also unlike independent products, Oracle Big Data SQL is claimed to be compatible with Oracle’s usual security model and SQL dialect.
- At least when it talks to Hadoop, Oracle Big Data SQL exploits predicate pushdown to reduce network traffic.
And by the way – Oracle Big Data SQL is NOT “SQL-on-Hadoop” as that term is commonly construed, unless the complete Oracle DBMS is running on every node of a Hadoop cluster.
Predicate pushdown is actually a simple concept:
- If you issue a query in one place to run against a lot of data that’s in another place, you could spawn a lot of network traffic, which could be slow and costly. However …
- … if you can “push down” parts of the query to where the data is stored, and thus filter out most of the data, then you can greatly reduce network traffic.
“Predicate pushdown” gets its name from the fact that portions of SQL statements, specifically ones that filter data, are properly referred to as predicates. They earn that name because predicates in mathematical logic and clauses in SQL are the same kind of thing — statements that, upon evaluation, can be TRUE or FALSE for different values of variables or data.
The most famous example of predicate pushdown is Oracle Exadata, with the story there being:
- Oracle’s shared-everything architecture created a huge I/O bottleneck when querying large amounts of data, making Oracle inappropriate for very large data warehouses.
- Oracle Exadata added a second tier of servers each tied to a subset of the overall storage; certain predicates are pushed down to that tier.
- The I/O between Exadata’s two sets of servers is now tolerable, and so Oracle is now often competitive in the high-end data warehousing market,
Oracle evidently calls this “SmartScan”, and says Oracle Big Data SQL does something similar with predicate pushdown into Hadoop.
Oracle also hints at using predicate pushdown to do non-tabular operations on the non-relational systems, rather than shoehorning operations on multi-structured data into the Oracle DBMS, but my details on that are sparse.
- Chris Kanaracus’ coverage of the announcement quotes me at length.
|Categories: Data warehousing, Exadata, Hadoop, Oracle, SQL/Hadoop integration, Theory and architecture||9 Comments|
As part of my series on the keys to and likelihood of success, I outlined some examples from the DBMS industry. The list turned out too long for a single post, so I split it up by millennia. The part on 20th Century DBMS success and failure went up Friday; in this one I’ll cover more recent events, organized in line with the original overview post. Categories addressed will include analytic RDBMS (including data warehouse appliances), NoSQL/non-SQL short-request DBMS, MySQL, PostgreSQL, NewSQL and Hadoop.
DBMS rarely have trouble with the criterion “Is there an identifiable buying process?” If an enterprise is doing application development projects, a DBMS is generally chosen for each one. And so the organization will generally have a process in place for buying DBMS, or accepting them for free. Central IT, departments, and — at least in the case of free open source stuff — developers all commonly have the capacity for DBMS acquisition.
In particular, at many enterprises either departments have the ability to buy their own analytic technology, or else IT will willingly buy and administer things for a single department. This dynamic fueled much of the early rise of analytic RDBMS.
Buyer inertia is a greater concern.
- A significant minority of enterprises are highly committed to their enterprise DBMS standards.
- Another significant minority aren’t quite as committed, but set pretty high bars for new DBMS products to cross nonetheless.
- FUD (Fear, Uncertainty and Doubt) about new DBMS is often justifiable, about stability and consistent performance alike.
A particularly complex version of this dynamic has played out in the market for analytic RDBMS/appliances.
- First the newer products (from Netezza onwards) were sold to organizations who knew they wanted great performance or price/performance.
- Then it became more about selling “business value” to organizations who needed more convincing about the benefits of great price/performance.
- Then the behemoth vendors became more competitive, as Teradata introduced lower-price models, Oracle introduced Exadata, Sybase got more aggressive with Sybase IQ, IBM bought Netezza, EMC bought Greenplum, HP bought Vertica and so on. It is now hard for a non-behemoth analytic RDBMS vendor to make headway at large enterprise accounts.
- Meanwhile, Hadoop has emerged as serious competitor for at least some analytic data management, especially but not only at internet companies.
Otherwise I’d say: Read more
Relational DBMS used to be fairly straightforward product suites, which boiled down to:
- A big SQL interpreter.
- A bunch of administrative and operational tools.
- Some very optional add-ons, often including an application development tool.
Now, however, most RDBMS are sold as part of something bigger.
- Oracle has hugely thickened its stack, as part of an Innovator’s Solution strategy — hardware, middleware, applications, business intelligence, and more.
- IBM has moved aggressively to a bundled “appliance” strategy. Even before that, IBM DB2 long sold much better to committed IBM accounts than as a software-only offering.
- Microsoft SQL Server is part of a stack, starting with the Windows operating system.
- Sybase was an exception to this rule, with thin(ner) stacks for both Adaptive Server Enterprise and Sybase IQ. But Sybase is now owned by SAP, and increasingly integrated as a business with …
- … SAP HANA, which is closely associated with SAP’s applications.
- Teradata has always been a hardware/software vendor. The most successful of its analytic DBMS rivals, in some order, are:
- Netezza, a pure appliance vendor, now part of IBM.
- Greenplum, an appliance-mainly vendor for most (not all) of its existence, and in particular now as a part of EMC Pivotal.
- Vertica, more of a software-only vendor than the others, but now owned by and increasingly mainstreamed into hardware vendor HP.
- MySQL’s glory years were as part of the “LAMP” stack.
- Various thin-stack RDBMS that once were or could have been important market players … aren’t. Examples include Progress OpenEdge, IBM Informix, and the various strays adopted by Actian.
I’ll start with three observations:
- Computer systems can’t be entirely tightly coupled — nothing would ever get developed or tested.
- Computer systems can’t be entirely loosely coupled — nothing would ever get optimized, in performance and functionality alike.
- In an ongoing trend, there is and will be dramatic refactoring as to which connections wind up being loose or tight.
As written, that’s probably pretty obvious. Even so, it’s easy to forget just how pervasive the refactoring is and is likely to be. Let’s survey some examples first, and then speculate about consequences. Read more
- The trend to clustered computing is sustainable.
- The trend to appliances is also sustainable.
- The “single” enterprise cluster is almost as much of a pipe dream as the single enterprise database.
I shall explain.
Arguments for hosting applications on some kind of cluster include:
- If the workload requires more than one server — well, you’re in cluster territory!
- If the workload requires less than one server — throw it into the virtualization pool.
- If the workload is uneven — throw it into the virtualization pool.
Arguments specific to the public cloud include:
- A large fraction of new third-party applications are SaaS (Software as a Service). Those naturally live in the cloud.
- Cloud providers have efficiencies that you don’t.
That’s all pretty compelling. However, these are not persuasive reasons to put everything on a SINGLE cluster or cloud. They could as easily lead you to have your VMware cluster and your Exadata rack and your Hadoop cluster and your NoSQL cluster and your object storage OpenStack cluster — among others — all while participating in several different public clouds as well.
Why would you not move work into a cluster at all? First, if ain’t broken, you might not want to fix it. Some of the cluster options make it easy for you to consolidate existing workloads — that’s a central goal of VMware and Exadata — but others only make sense to adopt in connection with new application projects. Second, you might just want device locality. I have a gaming-class PC next to my desk; it drives a couple of monitors; I like that arrangement. Away from home I carry a laptop computer instead. Arguments can be made for small remote-office servers as well.
|Categories: Cloud computing, Clustering, Data warehouse appliances, Exadata, NoSQL, Software as a Service (SaaS)||5 Comments|
1. It boggles my mind that some database technology companies still don’t view compression as a major issue. Compression directly affects storage and bandwidth usage alike — for all kinds of storage (potentially including RAM) and for all kinds of bandwidth (network, I/O, and potentially on-server).
Trading off less-than-maximal compression so as to minimize CPU impact can make sense. Having no compression at all, however, is an admission of defeat.
2. People tend to misjudge Hadoop’s development pace in either of two directions. An overly expansive view is to note that some people working on Hadoop are trying to make it be all things for all people, and to somehow imagine those goals will soon be achieved. An overly narrow view is to note an important missing feature in Hadoop, and think there’s a big business to be made out of offering it alone.
At this point, I’d guess that Cloudera and Hortonworks have 500ish employees combined, many of whom are engineers. That allows for a low double-digit number of 5+ person engineering teams, along with a number of smaller projects. The most urgently needed features are indeed being built. On the other hand, a complete monument to computing will not soon emerge.
3. Schooner’s acquisition by SanDisk has led to the discontinuation of Schooner’s SQL DBMS SchoonerSQL. Schooner’s flash-optimized key-value store Membrain continues. I don’t have details, but the Membrain web page suggests both data store and cache use cases.
4. There’s considerable personnel movement at Boston-area database technology companies right now. Please ping me directly if you care.
I recently complained that the Gartner Magic Quadrant for Data Warehouse DBMS conflates many use cases into one set of rankings. So perhaps now would be a good time to offer some thoughts on how to tell use cases apart. Assuming you know that you really want to manage your analytic database with a relational DBMS, the first questions you ask yourself could be:
- How big is your database? How big is your budget?
- How do you feel about appliances?
- How do you feel about the cloud?
- What are the size and shape of your workload?
- How fresh does the data need to be?
Let’s drill down. Read more