Tag Federated Data

Why Don’t We Have Federated Databases?

Federated databases are a dream that have not materialized. The SQL/MED extension to the ANSI/ISO SQL specification is a step in the right direction. In addition, both SQL Server and Oracle have proprietary extensions that make it possible to query external data sources. If all of this technology is available today, why aren’t more people using it?

Why Don’t We Have a Federated Database?

If federated databases are such a powerful thing, why hasn’t anyone built one? Surely this is something that many businesses are clamoring for, or at least in need of.

There are a number of problems facing anyone attempting to implement a federated database. Frankly, federated databases are fraught with technical difficulties. Every database vendor supports a different subset of the ANSI/ISO SQL standard, different vendors use different data types and metadata, concurrency is a huge concern with dealing with remote resources, and technology is a moving target.

Different Dialects of SQL

Anyone who has attempted to port an application from one database engine to another knows about the pain involved in translating queries. Different vendors adhere to the SQL standard to varying degrees. This problem can be solved by creating wrappers to translate sub-queries between different querying languages, but it’s still a problem that exists. This problem could be partially solved in a federated database by limiting the database vendors to a small subset of the ANSI/ISO SQL standard, but this doesn’t solve the problem, it merely avoids it by limiting functionality.

Different Metadata

Different databases may have different metadata – different data types are used to represent the same data and different structures are used to describe data. To solve this problem it becomes necessary to create elaborate mappings between columns that represent the same data. There may be situations where such a mapping becomes computationally intensive or functionally impossible. SQL Server allows the creation of .NET data types with custom methods for data searching, access, and manipulation. PostgreSQL features several data types (notably tsvector, hstore, and arrays) that would be difficult to convert to data types in other databases.

Metadata differences don’t just stop at the data type level. It’s possible to model data in a number of different ways; the type of an address could be indicated using an integer key value that references a lookup table in one database or as a VARCHAR column with values constrained by the database. It’s even possible for something as simple as Unicode text encodings to cause problems: SQL Server uses the NVARCHAR data type for storing Unicode strings while other databases do not use a separate data type.

Concurrency

Concurrency, depending on your database, may be a concern. Managing concurrent operations within a single database is a difficult task, much less managing concurrency across multiple databases. Unfortunately, correct handling of concurrency across all components of a federated database is critical.

Many potential problems of a federated database can be solved through different trade offs. However, managing concurrency is a nearly impossible task. To properly and effectively manage concurrency across multiple databases is to ask the impossible. Not only would this require the federated database vendor be able to account for all possible concurrency issues in relational databases, but they would need to be able to account for potential concurrency issues in any database that integrates with the federated database.

The Moving Target

Even were a database vendor to take on this task, they’d be consistently aiming for a moving target. New features are added to relational databases all the time, and there are enough major players in the market to make it difficult for users to keep up to date, much less a federated database vendor. Once you factor in the wealth of other, non-relational databases, the idea of creating a federated database system to handle metadata mapping, concurrency control, and query language resolution trends toward impossible.

The State of The Industry

Where we stand now, there is almost no chance of any independent software vendor creating a true federated database. There is hope, but not from where you would expect it.

Enterprise data warehouses can fulfill much of the function of a federated database, but they still require complex ETL and data mapping to be truly useful. Adding additional information to an enterprise data warehouse can require extra work to prepare the data warehouse and ETL processes for the new data. Unfortunately, enterprise data warehouses require too much manual intervention to be a candidate for a federated database.

As we’ve discussed, SQL Server and Oracle provide ways to reference remote database servers. These methods have their own problems. SQL Server linked tables are prone to problems with some objects not being remoteable. When we’re querying a remote server, we need to make sure that the parts of our query going to the remote server are handled on the remote server. This is difficult to get right. On some occasions we might even see an entire remote table be streamed across the wire to be filtered on the originating server. This is something that we don’t want to see happening. For a federated database to be a tenable product there must be an easy way to offload queries to a remote table and a guarantee of adequate performance.

Properly remoting queries is incredibly complex. Assume, for a moment, that we have a report that queries data on the sales department’s database server and we also need to include data from human resources data. Our query might look something like this:

SELECT sp.first_name,
       sp.last_name,
       eh.employment_duration,
       ts.year,
       ts.total_sales_by_year,
       r.average_review_score
FROM   public.sales_person sp
       JOIN HRDB01.employee_info.public.employee_history eh
            ON sp.employee_id = eh.employee_id
       JOIN ( SELECT o.employee_id,
                     o.year,
                     SUM(o.total) total_sales_by_year                     
              FROM   public.orders AS o
              GROUP BY o.employee_id, o.year
            ) AS ts ON sp.employee_id = ts.employee_id
       JOIN ( SELECT r.year,
                     r.employee_id,
                     AVG(r.score) AS average_review_score
              FROM   HRDB01.employee_info.reviews.review r
              GROUP BY r.year, r.employee_id
       ) AS rv ON sp.employee_id = rv.employee_id
                  AND ts.year = rv.year;

Looking at this query we’re hitting two separate remote objects in one remote database. In order for this query to be effective, our query optimizer must b able to re-write the query in a way that lets it build an intelligent query for the two remote tables HRDB01.employee_info.public.employee_history and HRDB01.employee_info.reviews.review. But, in order to effectively re-write the local query to properly reference remote objects, we need to know everything about the remote objects – our calling server must be aware of as much metadata as possible so the remoted query can be re-written before being sent to the remote server. While this is doable, it puts additional load on the calling server. This server now has to maintain information about remote database objects. But that’s not all! If we want our queries to be truly optimal, our federating sever will need to be aware of how data types will behave on the remote servers and how the remote data types will interact with local data types.

Once you examine the intricacies of a federated database, it becomes obvious why the federated database, as a boxed product, is beyond our reach. It’s not that the task is impossible; on the contrary such a task is very possible. The difficulty lies in coordinating all of the information available and using it to deliver data quickly. There are enough moving and potentially unknown parts that it’s non-trivial to create heterogeneous systems capable of filling out the promise of federated databases. Faced with this situation, the only viable solution is to build your own solution that answers the needs of the business.

http://railstips.org/blog/archives/2011/01/27/data-modeling-in-performant-systems/

The Promise and Failure of Federated Data

One of the biggest problems facing businesses today is integrating data from multiple sources. The holy grail of data integration is called a federated database system. Basically, a federated database stores meta data about other databases and makes it easier to integrate them through a single interface. Many relational databases have features that support integrating with other relational databases through SQL Server’s linked servers or Oracle’s database links. One of the problems with these features is that they only allow relational databases to talk to other relational databases. As the volumes of data we collect every day increase, more and more of that data is being stored outside of relational databases in CSVs, spreadsheets, log files, PDFs, and plain text as well as in a variety of non-relational databases like MongoDB, HBase, Riak, and Cassandra.

If the only thing we’re looking for is access to meta data for external data, SQL Server will provide the [FileTable][8] data type in SQL Server 2011. Admittedly, FileTable isn’t an acceptable solution because it’s really intended to make it possible to reference files in the database that are being managed by external applications through the filesystem and Win32 APIs. Clearly, this doesn’t suit our need for querying external data.

It’s also possible to use ETL tools to move data into relational databases. One of the problems with ETL tools (SSIS, Pentaho Data Integration/Kettle, or Oracle Data Integrator) is that they are effectively batch operations. New data insertions will have to be triggered by some external event; the data isn’t available until it’s migrated into some master system.

Luckily, there’s an extension to the SQL Standard to help us: SQL/MED. MED stands for Management of External Data. This is a way to link up any external data source to a database server. It doesn’t have to be another relational database – there’s already a twitter foreign data wrapper library. Unfortunately, PostgreSQL is currently the only major database player on the market with any potential for an implementation for SQL/MED.

The implementation of SQL/MED just isn’t here, yet.

Another promising project is HadoopDB. HadoopDB is a project coming out of Yale University. The aim is to make it possible to run analytical workloads in parallel across many commodity RDBMS servers. One of the goals of HadoopDB is to excel in areas where parallel data warehouses simple do not perform well. Many of these situations are outline in the paper HadoopDB: An Architectural Hybrid of MapReduce and DBMS Technologies for Analytical Workloads. To summarize, parallel data warehouses provide near linear scaling up to several hundred nodes running on homogeneous hardware. Parallel data warehouse also operate under the assumption that failures are rare. Google and others have demonstrated that hardware failure is inevitable at scale. HadoopDB presents a phenomenal way to scale databases and integrate disparate technologies.

Despite its promise of scaling databases, HadoopDB still doesn’t solve the problems that we face when trying to build a federated database system. The truth is a depressing one – there is currently no solution for building federated databases that incorporate data from across the enterprise. Database vendors, DBAs, and more traditional corporate IT departments will tell you that this is a Good Thing™. I’ve mentioned before that you should choose the database that is best suited for the task at hand.

Where do we go from here? SQL/MED doesn’t meet its own promises – only one vendor is implementing the SQL/MED standard and that support is going to depend on third parties releasing drivers. HadoopDB isn’t a federated database so much as it is a way to avoid scaling a relational database into thousands of cheap nodes and paying millions of dollars in licensing fees for Teradata, Microsoft’s Parallel Data Warehouse, or Oracle’s Exadata. The unfortunate truth is that if we want a federated database we’re going to have to build it ourselves.

What sounds like a Sisyphean task isn’t as difficult as it sounds. If we’re collecting data in multiple databases, the problem is already almost solved. Some of those technologies are already here. LINQ lets us treat all data sources equally; we can query an array of objects as easily as we can query a database. ARel is a relational algebra for Ruby. While ARel is specifically focused on working with relational databases, it could be extended to work with many different data sources. Business intelligence vendor Jaspersoft recently announced support for a number of non-relational databases to complement their existing business intelligence products. Quest Software, makes Toad for Cloud Databases – a tool for querying both relational and non-relational databases.

A federated database may never materialize, but it’s already possible to build a hybrid database solution today.

References

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