The Power and Magic of LPG

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With the availability of V5R3 i5/OS and DB2 UDB for iSeries, the SQL Query Engine now supports a powerful strategy for minimizing query input/output (I/O) and maximizing query performance. This new strategy appears as magic, given the Query Optimizer's ability to rewrite the query using generated local selection predicates where none were specified. This article explores the technique and benefits of look-ahead predicate generation (LPG).

In the world of query optimization, minimizing or even eliminating the reading and processing of unnecessary data is the key to good performance because I/O operations are relatively slow operations. The job of the query optimizer is to choose the appropriate methods and build a strategy that will access and process the rows as quickly and efficiently as possible. With a high number of strategies available, the optimizer will normally be able to build a query plan that meets the user response time requirements.

Within a query, the local selection predicates are used to specify which rows are to be selected for processing. One technique for eliminating rows from further processing requires reading the data from the table(s) and testing values. Another technique for eliminating rows is accomplished without actually reading the data from the table(s) but instead relies upon mathematic principles and other database objects to avoid testing rows in the table(s). For example, given a query with local selection that identifies one row out of a one-billion-row table, the database engine can read and test every row (one billion tests), or it can use an index to identify only the one matching row, eliminating all the other rows without reading and testing them. While the result will be the same, the difference in query performance between these two strategies will be significant.

Another query scenario that can cause a lot of reading (and poor performance) is joining of tables. Specifying a join usually causes the database engine to select rows from one table and perform a read operation to find any matching rows in another table. By definition, the join can reduce the result set, but only by testing for the existence of the row. The potential for reading and ultimately rejecting many rows makes this type of query problematic. Again, if the query optimizer can employ specific strategies to eliminate rows before the join, query performance can be increased and use of database server resources can be reduced.

DB2 UDB for iSeries supports many methods and employs many strategies to substantially reduce the reading of rows and the processing of data. One such strategy is called look-ahead predicate generation (LPG). This powerful and elegant feature can significantly reduce query times.

A simple join scenario will help explain why the LPG support can have such a positive affect on a query's performance.

A Couple of Methods

Assume two tables: one relatively small (SmallTableA) and one relatively large (LargeTableB). A join query is issued referencing both tables. Local selection is defined against SmallTableA, but not LargeTableB. The selectivity of the local selection predicate is very high (i.e., it identifies two rows in SmallTableA).

FROM     SmallTableA  A,
         LargeTableB  B
WHERE    A.Join_Col = B.Join_Col
AND      A.Col1 IN (112358, 132134)

With no indexes defined on LargeTableB, you have various table access and join processing combinations:

One strategy is to join LargeTableB to SmallTableA. Select all rows via a full table scan of LargeTableB and join every row to SmallTableA via an index or a hash table. Figure 1 illustrates this join.

Figure 1: Here, LargeTableB is joined to SmallTableA. (Click images to enlarge.)

Given no local selection, every row selected from LargeTableB will be used to try the join to SmallTableA. This combination will produce a large number of reads.

Another strategy is to join SmallTableA to LargeTableB. Select all rows via full table scan of LargeTableB to create a temporary indexed list or temporary hash table, as shown in Figure 2.

Figure 2: Here, SmallTableA is joined to LargeTableB.

Given no local selection to reduce the rows, the temporary indexed list or temporary hash table on LargeTableB will have to represent all the rows from LargeTableB, and both temporary data structures will take a relatively long time to populate. These structures will be relatively large as well.

A Couple of Better Methods

With DB2's LPG and query rewrite techniques, the join scenarios can be enhanced considerably.

If LargeTableB has some local selection defined and this local selection is somewhat selective (i.e., narrowing down the rows to be selected and processed), then the database engine can use methods that will reduce the number of rows accessed. This is where LPG comes in! The query optimizer can "look ahead" to generate local selection from other tables in the query and then transfer that local selection to another table. When this local selection is available, methods that take advantage of indexing technology can be employed to speed up the query request. (Click here for more information about indexing strategies.)

Using the example above, the local selection specified for SmallTableA will be used to identify not only the rows in SmallTableA but also the corresponding join column values. For example, the SmallTableA row containing A.Col1 = 112358 also contains A.Join_Col = Value1 (i.e., the corresponding join column value).

These distinct join column values derived from SmallTableA are used to generate local selection on LargeTableB. Now that LargeTableB has local selection, additional techniques can be applied to minimize or eliminate reading large sets of rows.

Here's a representation of the rewritten query:

FROM     SmallTableA  A,
         LargeTableB  B
WHERE    A.Join_Col = B.Join_Col
AND      A.Col1 IN (112358, 132134)
AND      B.Join_Col IN (Value1Value2)

The new local selection allows the optimizer to consider additional join strategies. If the (generated) local selection on LargeTableB significantly reduces the number of rows accessed, LargeTableB may be placed in the first (primary) join position and accessed via an index.

Another strategy has LargeTableB placed in the second join position. If the join on LargeTableB is implemented via a temporary hash table, the amount of data placed in the hash will also be reduced by the (generated) local selection. Populating the temporary hash table can be much faster if an index is created for the (generated) local selection. Figure 3 illustrates this strategy.

Figure 3: Here, LargeTableB is placed in the second join position.

The real magic of LPG comes into play when there is more than one table joined to the larger table.

Assume four tables: three relatively small (SmallTableA, SmallTableB, SmallTableC) and one relatively large (LargeTableD). A join query is issued referencing both small and large tables. Local selection is defined against the small tables, but not the larger LargeTableD. The selectivity of the local selection predicates is very high (i.e., it identifies few rows in SmallTableA, SmallTableB, and SmallTableC).

FROM     SmallTableA  A,
         SmallTableB  B,
         SmallTableC  C,
         LargeTableD  D
WHERE    A.Join_Col = D.Join_Col1
AND      B.Join_Col = D.Join_Col2
AND      C.Join_Col = D.Join_Col3
AND      A.Col1 IN (112358, 132134)
AND      B.Col6= 'ABC'
AND      C.Col4= 2005
AND      C.Col5= 'January'

These are the various table access and join processing combinations for the first join pair:

  • SmallTableA joined to LargeTableD
  • SmallTableB joined to LargeTableD
  • SmallTableC joined to LargeTableD

Select all rows via full table scan of LargeTableB to create a temporary indexed list or a temporary hash table. For all rows joined to LargeTableD, join to other two SmallTables, as shown in Figure 4.

Figure 4: Here, all rows joined to LargeTableD join to other two SmallTables.

Now, consider these table access and join processing combinations:

  • LargeTableD joined to SmallTableA
  • LargeTableD joined to SmallTableB
  • LargeTableD joined to SmallTableC

Select all rows via full table scan of LargeTableD and join every row to SmallTableA via an index or a hash table. For all rows from LargeTableD, join to the other three SmallTables, as shown in Figure 5.

Figure 5: Here, all rows from LargeTableD join to the other three SmallTables.

Using the four-table query example above, the local selection specified for SmallTableA, SmallTableB, and SmallTableC will be used not only to identify the rows in the respective tables, but also to identify the corresponding join column values. These distinct join column values (derived from SmallTableA, SmallTableB, and SmallTableC) are used to generate local selection on LargeTableD using LPG. See Figure 6.

Figure 6: The distinct join column values use LPG to generate local selection on LargeTableD.

The rewritten query would be represented like this:

FROM     SmallTableA  A,
         SmallTableB  B,
         SmallTableC  C,
         LargeTableD  D
WHERE    A.Join_Col = D.Join_Col1
AND      B.Join_Col = D.Join_Col2
AND      C.Join_Col = D.Join_Col3
AND      A.Col1 IN (112358, 132134)
AND      B.Col6= 'ABC'
AND      C.Col4= 2005
AND      C.Col5= 'January'
AND      D.Join_Col1 IN (Value1, Value2)
AND      D.Join_Col2 IN (Value1, Value2, ... Value n)
AND      D.Join_Col3 IN (Value1, Value2, ... Value n)

Now that LargeTableD has local selection defined, an appropriate set of indexes can be created and applied to minimize (or eliminate) reading large sets of rows.

Yet another DB2 UDB for iSeries feature can be employed to handle the newly generated local selection--namely, index ANDing. This is the ability of the query optimizer and database engine to use more than one index to specifically identify and access rows within a given table. Using single-column-key indexes defined for each join column in LargeTableD, the database engine can merge the list of rows identified by each respective index and use the final list to access the table. The intersection (ANDing) of all three local selection predicates should result in a much narrower set of rows to be accessed and processed in LargeTableD. Using the indexes allows the database engine to avoid reading a large set of rows, resulting in a more efficient and faster query. See Figure 7.

Figure 7: Index ANDing allows the database engine to avoid reading a large set of rows.

The optimizer can also use LPG with common join requests that contain a one-to-one relationship. In this case, LPG is used to recursively look ahead and generate local selection from the neighboring table "downstream." This has the tremendous benefit of allowing the optimizer much more latitude in setting the join order. Furthermore, this strategy reduces the risk of any one particular join order delivering poor performance.

Assume four tables all roughly the same size: TableA, TableB, TableC, and TableD. A join query is issued referencing all four tables with a one-to-one relationship (A to B, B to C, C to D). Local selection is defined against only one table, TableA.

FROM     TableA  A,
         TableB  B,
         TableC  C,
         TableD  D
WHERE    A.Join_Col1 = B.Join_Col1
AND      B.Join_Col2 = C.Join_Col2
AND      C.Join_Col3 = D.Join_Col3
AND      A.Col5 = 'ABC'

Determining the best-performing query plan can be problematic, depending on the selectivity of the local selection predicates defined for TableA and the relative position of TableA in the join order. If all the tables have some local selection defined, the optimizer has many more choices for the query join order and consequently more opportunities to provide multiple high-performance query plans.

Using the four-table query example above, the local selection specified for TableA will be used not only to identify the rows in TableA, but also to identify the corresponding join column values. These distinct join column values (derived from TableA) are used to generate local selection on TableB.

The generated local selection specified for TableB will be used to identify the rows in TableB and identify the corresponding join column values. These distinct join column values (derived from TableB) are used to generate local selection on TableC.

The generated local selection specified for TableC will be used to identify the rows in TableD and identify the corresponding join column values. These distinct join column values (derived from TableC) are used to generate local selection on TableD.

Now the query has been rewritten and optimized with local selection on all four tables (Figure 8).

Figure 8: The query is optimized with local selection on all four tables.

Here's a representation of the rewritten query:

FROM     TableA  A,
         TableB  B,
         TableC  C,
         TableD  D
WHERE    A.Join_Col1 = B.Join_Col1
AND      B.Join_Col2 = C.Join_Col2
AND      C.Join_Col3 = D.Join_Col3
AND      A.Col5 = 'ABC'
AND      B.Join_Col1 IN (Value1, Value2, ... Value n)
AND      C.Join_Col2 IN (Value1, Value2, ... Value n)
AND      D.Join_Col3 IN (Value1, Value2, ... Value n)

Being aware of and planning for LPG will allow the creation of indexes to effectively support join queries. Without this knowledge, and the corresponding indexes, better query performance will be squandered. The strategy for creating indexes for the generated local selection predicates is identical to creating indexes for user-supplied local predicates. Identifying the join columns is a clue to identifying which columns to create indexes on.

Query Magic

Like a good magician, the query optimizer uses techniques that are hidden from the audience. Only the final result is obvious. Given that query rewrite and LPG occurs out of sight, it will be helpful to get some feedback from the optimizer when this strategy is employed. One simple technique is to observe the estimated selectivity of each table in the query when that table has no user-supplied local selection predicates. Without LPG, the estimated number of rows selected should be equal to the total number of rows in the table. If the estimated number of rows selected is less than the total, then some selection is being applied.

Another more accurate technique is to rely on iSeries Navigator - Visual Explain to render the query graph. Once the query graph is displayed, any nodes that are under the influence of LPG can be highlighted (in green). To identify the use of LPG, select the "Highlight LPG" option on the View menu within the Visual Explain window.

Figure 9: iSeries Navigator - Visual Explain renders the query graph.

The key to fully utilizing an optimization capability is in integrating that capability into the normal flow of query optimization--in effect, making that capability pervasive. Traditionally, sophisticated optimization techniques rely on interrogation of the query or query environment to recognize when and where to apply the specific technique. If this identification is successful, then the technique can be successful; otherwise, the technique is useless or even detrimental.

While the DB2 UDB for iSeries LPG may seem like a specialized and rarely needed technique, it really is a pervasive and powerful tool within the optimizer's bag of tricks. The simple join examples illustrated here are but one area in which this capability can be applied. Other areas include queries against star schema and snowflake schema data models, as well as SQL requests with subqueries, common table expressions, or derived tables.

Rob Bestgen is a senior technical staff member and query optimizer design leader on the IBM DB2 for iSeries team in Rochester, Minnesota. Rob can be reached at This email address is being protected from spambots. You need JavaScript enabled to view it..

Mike Cain is a senior technical staff member and leader of the DB2 for iSeries Center of Competency in Rochester, Minnesota. Mike can be reached at This email address is being protected from spambots. You need JavaScript enabled to view it..

Mike Cain

Mike Cain is the team leader of the IBM DB2 for i Center of Excellence in Rochester, Minnesota. He can be reached at







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    • How can RDi help me find out if I’ve tested all lines of a program?
    • What’s the best way to transition from PDM to RDi?
    • How do I convince my long-term developers to use RDi?

    This is a unique, online opportunity to hear how you can get more out of RDi.


  • Node.js on IBM i Webinar Series Pt. 2: Setting Up Your Development Tools

    Profound Logic Software, Inc.Have you been wondering about Node.js? Our free Node.js Webinar Series takes you from total beginner to creating a fully-functional IBM i Node.js business application. In Part 2, Brian May teaches you the different tooling options available for writing code, debugging, and using Git for version control. Attend this webinar to learn:

    • Different tools to develop Node.js applications on IBM i
    • Debugging Node.js
    • The basics of Git and tools to help those new to it
    • Using as a pre-built development environment



  • Inside the Integrated File System (IFS)

    SB_HelpSystems_WC_GenericDuring this webinar, you’ll learn basic tips, helpful tools, and integrated file system commands—including WRKLNK—for managing your IFS directories and Access Client Solutions (ACS). We’ll answer your most pressing IFS questions, including:

    • What is stored inside my IFS directories?
    • How do I monitor the IFS?
    • How do I replicate the IFS or back it up?
    • How do I secure the IFS?

    Understanding what the integrated file system is and how to work with it must be a critical part of your systems management plans for IBM i.


  • Expert Tips for IBM i Security: Beyond the Basics

    SB PowerTech WC GenericIn this session, IBM i security expert Robin Tatam provides a quick recap of IBM i security basics and guides you through some advanced cybersecurity techniques that can help you take data protection to the next level. Robin will cover:

    • Reducing the risk posed by special authorities
    • Establishing object-level security
    • Overseeing user actions and data access

    Don't miss this chance to take your knowledge of IBM i security beyond the basics.



  • 5 IBM i Security Quick Wins

    SB PowerTech WC GenericIn today’s threat landscape, upper management is laser-focused on cybersecurity. You need to make progress in securing your systems—and make it fast.
    There’s no shortage of actions you could take, but what tactics will actually deliver the results you need? And how can you find a security strategy that fits your budget and time constraints?
    Join top IBM i security expert Robin Tatam as he outlines the five fastest and most impactful changes you can make to strengthen IBM i security this year.
    Your system didn’t become unsecure overnight and you won’t be able to turn it around overnight either. But quick wins are possible with IBM i security, and Robin Tatam will show you how to achieve them.

  • How to Meet the Newest Encryption Requirements on IBM i

    SB PowerTech WC GenericA growing number of compliance mandates require sensitive data to be encrypted. But what kind of encryption solution will satisfy an auditor and how can you implement encryption on IBM i? Watch this on-demand webinar to find out how to meet today’s most common encryption requirements on IBM i. You’ll also learn:

    • Why disk encryption isn’t enough
    • What sets strong encryption apart from other solutions
    • Important considerations before implementing encryption



  • Security Bulletin: Malware Infection Discovered on IBM i Server!

    SB PowerTech WC GenericMalicious programs can bring entire businesses to their knees—and IBM i shops are not immune. It’s critical to grasp the true impact malware can have on IBM i and the network that connects to it. Attend this webinar to gain a thorough understanding of the relationships between:

    • Viruses, native objects, and the integrated file system (IFS)
    • Power Systems and Windows-based viruses and malware
    • PC-based anti-virus scanning versus native IBM i scanning

    There are a number of ways you can minimize your exposure to viruses. IBM i security expert Sandi Moore explains the facts, including how to ensure you're fully protected and compliant with regulations such as PCI.



  • Fight Cyber Threats with IBM i Encryption

    SB PowerTech WC GenericCyber attacks often target mission-critical servers, and those attack strategies are constantly changing. To stay on top of these threats, your cybersecurity strategies must evolve, too. In this session, IBM i security expert Robin Tatam provides a quick recap of IBM i security basics and guides you through some advanced cybersecurity techniques that can help you take data protection to the next level. Robin will cover:

    • Reducing the risk posed by special authorities
    • Establishing object-level security
    • Overseeing user actions and data access




  • 10 Practical IBM i Security Tips for Surviving Covid-19 and Working From Home

    SB PowerTech WC GenericNow that many organizations have moved to a work from home model, security concerns have risen.

    During this session Carol Woodbury will discuss the issues that the world is currently seeing such as increased malware attacks and then provide practical actions you can take to both monitor and protect your IBM i during this challenging time.


  • How to Transfer IBM i Data to Microsoft Excel

    SB_HelpSystems_WC_Generic3 easy ways to get IBM i data into Excel every time
    There’s an easy, more reliable way to import your IBM i data to Excel? It’s called Sequel. During this webinar, our data access experts demonstrate how you can simplify the process of getting data from multiple sources—including Db2 for i—into Excel. Watch to learn how to:

    • Download your IBM i data to Excel in a single step
    • Deliver data to business users in Excel via email or a scheduled job
    • Access IBM i data directly using the Excel add-in in Sequel

    Make 2020 the year you finally see your data clearly, quickly, and securely. Start by giving business users the ability to access crucial business data from IBM i the way they want it—in Microsoft Excel.



  • HA Alternatives: MIMIX Is Not Your Only Option on IBM i

    SB_HelpSystems_WC_GenericIn this recorded webinar, our experts introduce you to the new HA transition technology available with our Robot HA software. You’ll learn how to:

    • Transition your rules from MIMIX (if you’re happy with them)
    • Simplify your day-to-day activities around high availability
    • Gain back time in your work week
    • Make your CEO happy about reducing IT costs

    Don’t stick with a legacy high availability solution that makes you uncomfortable when transitioning to something better can be simple, safe, and cost-effective.



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  • Backup and Recovery on IBM i: Your Strategy for the Unexpected

    SB HelpSystems SC 5413Robot automates the routine tasks of iSeries backup and recovery, saving you time and money and making the process safer and more reliable. Automate your backups with the Robot Backup and Recovery Solution. Key features include:
    - Simplified backup procedures
    - Easy data encryption
    - Save media management
    - Guided restoration
    - Seamless product integration
    Make sure your data survives when catastrophe hits. Try the Robot Backup and Recovery Solution FREE for 30 days.

  • Manage IBM i Messages by Exception with Robot

    SB HelpSystems SC 5413Managing messages on your IBM i can be more than a full-time job if you have to do it manually. How can you be sure you won’t miss important system events?
    Automate your message center with the Robot Message Management Solution. Key features include:
    - Automated message management
    - Tailored notifications and automatic escalation
    - System-wide control of your IBM i partitions
    - Two-way system notifications from your mobile device
    - Seamless product integration
    Try the Robot Message Management Solution FREE for 30 days.

  • Easiest Way to Save Money? Stop Printing IBM i Reports

    SB HelpSystems SC 5413Robot automates report bursting, distribution, bundling, and archiving, and offers secure, selective online report viewing.
    Manage your reports with the Robot Report Management Solution. Key features include:

    - Automated report distribution
    - View online without delay
    - Browser interface to make notes
    - Custom retention capabilities
    - Seamless product integration
    Rerun another report? Never again. Try the Robot Report Management Solution FREE for 30 days.

  • Hassle-Free IBM i Operations around the Clock

    SB HelpSystems SC 5413For over 30 years, Robot has been a leader in systems management for IBM i.
    Manage your job schedule with the Robot Job Scheduling Solution. Key features include:
    - Automated batch, interactive, and cross-platform scheduling
    - Event-driven dependency processing
    - Centralized monitoring and reporting
    - Audit log and ready-to-use reports
    - Seamless product integration
    Scale your software, not your staff. Try the Robot Job Scheduling Solution FREE for 30 days.

  • ACO MONITOR Manages your IBM i 24/7 and Notifies You When Your IBM i Needs Assistance!

    SB DDL Systems 5429More than a paging system - ACO MONITOR is a complete systems management solution for your Power Systems running IBM i. ACO MONITOR manages your Power System 24/7, uses advanced technology (like two-way messaging) to notify on-duty support personnel, and responds to complex problems before they reach critical status.

    ACO MONITOR is proven technology and is capable of processing thousands of mission-critical events daily. The software is pre-configured, easy to install, scalable, and greatly improves data center efficiency.