Unstructured Data and LOBs
In part 1 of this series we took a look at database and DBA trends; part 2 examined the changing world of DB2 table spaces. Today, in part 3, we take a look at the burgeoning role of unstructured data and the impact of using LOBs to store that data in our DB2 databases.
The Growth of Unstructured Data
Although structured data remains the bedrock of the information infrastructure in most organizations, unstructured data is growing in importance. And there is much more unstructured data “out there” than there is structured. Indeed, analysts at IDC estimate that unstructured data accounts for as much as 90 percent of all digital information.
The rise of unstructured data is often attributed to the growing amount of multimedia data being adopted by organizations as they store more audio, video, and images. But that is only part of the story. Text documents, in the form of business forms, letters, and documents, and most importantly, e-mail, greatly contribute to the growing importance of unstructured data.
As organizations expand their digital information footprint, more types of unstructured data are being captured, stored, and made available for analysis. And not just internally generated data, but from more external data sources, too.
DB2 and Unstructured Data
DB2 for z/OS can be used to store unstructured data using BLOB, CLOB, and DBCLOB data types – collectively known as LOBs. Historically, LOBs were added to relational products like DB2 to compete more capably against the Object-Oriented databases of the time – this was back in the late 1990s. The idea was to enable relational tables to house unstructured data like images and audio and video data.
But DB2 for z/OS users were slow to adopt LOBs in their mainframe databases. This was due to several different reasons, not the least of which is that it took several versions of DB2 for LOBs to mature into capable, useful things for enterprise applications. Early implementations of LOBs in DB2 were somewhat kludgy and difficult to administer and use. But IBM has corrected many of those deficiencies over time and there are now tools that can help organizations to exploit and effectively manage DB2 LOBs, too.
The other new force driving LOB usage is the whole Big Data movement. Big Data is driving organizations to accumulate and analyze more data, and more varied types of data, to gain business insight. A specific example of Big Data driving the usage of LOBs in DB2 is the JSON support that has been added to DB2. JSON objects are stored in DB2 as BLOBs.
So, more and more organizations are adopting LOB data in their DB2 databases – to support unstructured data, for their big data projects, and to store documents and multimedia data. Nevertheless, sometimes I hear DBAs say things like “Oh, well, I don’t use LOBs so I don’t really need to worry about them.” That can cause you some trouble, because you have been using LOBs for some time now, whether you know it or not. LOB data has been part of the DB2 Catalog since Version 7 and the number of LOB columns used in the DB2 Catalog has been increasing over the past couple of DB2 releases. As you can see in table below, the DB2 Catalog now has 42 LOB columns. So even if you have not created any user DB2 tables with LOBs, you have system DB2 tables with LOBs. For example, SYSIBM.SYSVIEWS contains a CLOB containing the source text used to create the VIEW.
LOBs are also used in the DB2 Directory, in the DBD01 “table” a BLOB column is used to store the DBD data (2GB). And in the SPT01 “table” there are two BLOB columns for storing data and explain information.
Using LOBs in DB2
I will not attempt to train you on the usage of LOBs in DB2 in this blog post. Suffice it to say that LOBs require different management and administration tactics to ensure their accuracy and usability. But I do want to bring up some of the administration concerns that using LOBs can create.
The first thing to keep in mind is that most LOBs are larger than your typical column data. I mean, it’s right there in the name LOB = Large OBject. As the size of data increases, so do the management concerns, such as lengthy elapsed times to run utilities on the data, performance of accessing the data, and so on. And you’ll also need to keep in mind whether or not to log changes to your LOB data. If you do log LOB changes, then you can stress your logs because of the size of the LOB data. If you do not log LOB changes, then you will need to make sure that you have sufficient methods to recover the LOB data because the changes between image copies won’t be there in the log. Generally speaking, you usually will want to avoid the logging of LOBs. You can turn off LOB logging by specifying NOT LOGGED in the LOB table space DDL.
There are also many restrictions on how LOB data can be used with SQL. LOB data is not like traditional, structured database data, so DB2 imposes some limitations, for example:
- Cannot use a LOB in a GROUP BY or ORDER BY clause
- Cannot specify SELECT DISTINCT on a LOB
- LOBs cannot be used in the context of an INCLUDE(column-name) clause in a MERGE statement
- Cannot define check constraints, primary key, unique, or foreign keys on LOBs
- LOBs cannot be used in any predicate except EXISTS, LIKE and NULL
These are for illustrative purposes only. There are other restrictions, all of which can be found in the IBM SQL Reference manual.
Unless it is an inline LOB where the entire LOB is stored in the base table, a LOB will require a LOB table space, auxiliary table, and LOB index. When building auxiliary tables and indexes, you do not specify columns the same way that you do for normal tables and indexes. For the Auxiliary Table you specify the LOB column and base table and DB2 automatically generates the columns needed. For the Auxiliary Index you just specify the auxiliary table and DB2 implicitly generates the needed index keys.
Each table can have 0, 1, or many LOB columns and each LOB instance can be up to 2GB in size. Each table with at least 1 LOB must have a ROWID; the ROWID is a varying-length 17 byte field. One page of a LOB table space will never contain more than one LOB, but one LOB can span multiple LOB table space pages. An auxiliary table, which resides in the LOB table space, can store only one LOB column of a base table; there must be one and only one index on this column. All of these things can alter the way in which you manage and administer your DB2 tables and table spaces.
Let’s backtrack and review the size of LOBs in a little more depth. Each LOB instance can be up to 2G – and that is per row! Each LOB table space can have as many as 254 different data sets with a DSSIZE from 2G to 64G each for a total of about 16 terabytes (TB). This is per partition, so if there are 4096 partitions (which is the maximum), then the total size for a single LOB is over 66,000 TB. Think about that for a moment. I don’t care who you are, that is big!
Unless all of your LOB data is static – meaning it never changes – the size of your LOB data sets will continue to grow. Are you prepared for running utilities on such large table spaces?
When you drop a LOB column from a base table, DB2 will not automatically clean up LOB table spaces. After removing the LOB column you can either drop the LOB table space yourself explicitly, or perhaps reuse it for another LOB.
And finally, LOB columns are not really updated. The old version of the LOB is de-allocated, and a new is allocated. So LOBs are a bit different than the traditional data we are used to managing.
What can go wrong with LOBs?
Errors with LOBS occur when there are inconsistencies between the components of the LOB. We all know that “normal” DB2 indexes can be inconsistent with their associated table, but the issues are multiplied for LOB indexes
- The ROWID-Version number in the base table row may not be found in the LOB index.
- There may be entries in the LOB index that are not referenced by any row in the base table.
- The LOB data itself may not be where the LOB index points to.
- There may be LOBs in the LOB table space that are not referenced by the LOB index.
CHECK DATA can be used to find errors 1 and 2 (from the list above); CHECK LOB can be used to find errors 3 and 4. But it is possible that CHECK LOB will convert a type 4 error into a type 2 error, so proceed with caution.
Then there is the issue of LOB index consistency. If the LOB index is inconsistent with the base table data, the LOB data cannot be accessed. There is no direct access to the LOB table space except through the LOB index. If the LOB index is inconsistent with the LOB table space, DB2 will get errors trying to access the LOB data for that row.
DB2 maintains LOB data in the LOB table space using a hierarchical structure.
LOB data in the LOB table space can be distributed over many different pages of the LOB table space. Remember, this LOB data is very large. DB2 uses a structure of map pages to point to data pages. At the top is the first map page and it is this page number that is stored in the LOB index. This first map page contains a list of pages, which can be other map pages and data pages. It also contains the total size of the LOB data. If all the data pages are not referenced by map pages or if the map pages are not properly referenced by a higher level map page, LOB data will be lost.
With all of these pointers and structures to maintain, there are a variety of things that can go wrong. To verify that your LOBs are structurally sound you must run a series of DB2 utilities, in the following order:
- Run CHECK DATA to verify that the ID fields specified in the base table are also found in the LOB index.
- Run CHECK INDEX to verify that the LOB index is valid.
- Run CHECK LOB to verify that the internal structure of the LOB table space is sound.
Of course, there are easier ways. It can make a lot of sense to consider using a modern tool that understands the nuances of LOBs so you can manage them accordingly and appropriately.
The Bottom Line
Business and industry trends dictate that unstructured data, in the form of LOBs, is increasingly being stored in our DB2 databases. This type of data is different than traditional, structured data and must be managed with these differences in mind. To do so requires in-depth knowledge and planning to avoid inconsistencies and errors.