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Landscape

A query optimizer is often a large and complex piece of code, which enumerates alternative evaluation plans from which 'the best' plan is selected for evaluation. Limited progress has been made sofar to decompose the optimizer into (orthogonal) components, because it is a common believe in research that a holistic view on the problem is a prerequisite to find the best plan. Conversely, commercial optimizers use a cost-model driven approach, which explores part of the space using a limited (up to 300) rewriting rules.

Our hypothesis is that query optimization should be realized with a collection of query optimizer transformers (QOT), each dedicated to a specific task. Furthermore, they are assembled in scenarios to support specific application domains or achieve a desired behavior. Such scenarios are selected on a session basis, a query basis, or dynamically at runtime; they are part of the query plan.

The query transformer list below is under consideration for development. For each we consider its goal, approach, and expected impact. Moreover, the minimal prerequisites identify the essential optimizers that should have done their work already. For example, it doesn't make sense to perform a static evaluation unless you have already propagated the constants using Alias Removal.

Constant expressions Goal: to remove scalar expressions which need be evaluated once during the query lifetime. Rationale: static expressions appear when variables used denote literal constants (e.g. 1+1), when catalog information can be merged with the plan (e.g. max(B.salary)), when session variables are used which are initialized once (e.g. user()). Early evaluation aids subsequent optimization. Approach: inspect all instructions to locate static expressions. Whether they should be removed depends on the expected re-use, which in most cases call for an explicit request upon query registration to do so. The result of a static evaluation provides a ground for alias removal. Impact: relevant for stored queries (MAL functions) Prereq: alias removal, common terms.

Alias Removal Goal: to reduce the number of variables referenceing the same value, thereby reducing the analysis complexity. Rationale: query transformations often result in replacing the right-hand side expression with a result variable. This pollutes the code block with simple assignments e.g. V:=T. Within the descendant flow the occurrence of V could be replaced by T, provided V is never assigned a new value. Approach: literal constants within a MAL block are already recognized and replaced by a single variable. Impact: medium.

Common Term Optimizer Goal: to reduce the amount of work by avoiding calculation of the same operation twice. Rationale: to simplify code generation for front-ends, they do not have to remember the subexpressions already evaluated. It is much easier to detect at the MAL level. Approach: simply walk through the instruction sequence and locate identical patterns. (Enhance is with semantic equivalent instructions) Impact: High Prereq: Alias Removal

Dead Code Removal Goal: to remove all instructions whose result is not used Rationale: due to sloppy coding or alternative execution paths dead code may appear. Als XML Pathfinder is expected to produce a large number of simple assignments. Approach: Every instruction should produce a value used somewhere else. Impact: low

Join Path Optimizer Goal: to reduce the volume produced by a join sequence Rationale: join paths are potentially expensive operations. Ideally the join path is evaluated starting at the smallest component, so as to reduce the size of the intermediate results. Approach: to successfully reduce the volume we need to estimate their processing cost. This calls for statistics over the value distribution, in particular, correlation histograms. If statistics are not available upfront, we have to restore to an incremental algorithm, which decides on the steps using the size of the relations. Impact: high

Operator Sort Goal: to sort the dataflow graph in such a way as to reduce the cost, or to assure locality of access for operands. Rationale: A simple optimizer is to order the instructions for execution by permutation of the query components Approach: Impact:

Singleton Set Goal: to replace sets that are known to produce precisely one tuple. Rationale: Singleton sets can be represented by value pairs in the MAL program, which reduces to a scalar expression. Approach: Identify a set variable for replacement. Impact:

Range Propagation Goal: look for constant ranges in select statements and propagate them through the code. Rationale: partitioned tables and views may give rise to expressions that contain multiple selections over the same BAT. If their arguments are constant, the result of such selects can sometimes be predicted, or the multiple selections can be cascaded into a single operation. Impact: high, should be followed by alias removal and dead code removal

Result Cacher Goal: to reduce the processing cost by keeping track of expensive to compute intermediate results Rationale: Approach: result caching becomes active after an instruction has been evaluated. The result can be cached as long as its underlying operands remain unchanged. Result caching can be made transparent to the user, but affects the other quer optimizers. Impact: high

Staged Execution Goal: to split a query plan into a number of steps, such that the first response set is delivered as quickly as possible. The remainder is only produced upon request. Rationale: interactive queries call for quick response and an indication of the processing time involved to run it too completion. Approach: staged execution can be realized using a fragmentation scheme over the database, e.g. each table is replaced by a union of fragments. This fragmentation could be determined upfront by the user or is derived from the query and database statistics. impact: high

Code Parallizer Goal: to exploit parallel IO and cpu processing in both SMP and MPP settings. Rationale: throwing more resources to solve a complex query helps, provided it is easy to determine that parallel processing recovers the administrative overhead Approach: every flow path segment can be handled by an independent process thread. Impact: high

Query Evaluation Maps Goal: to avoid touching any tuple that is not relevant for answering a query. Rationale: the majority of work in solving a query is to disgard tuples of no interest and to find correlated tuples through join conditions. Ideally, the database learns these properties over time and re-organizes (or builts a map) to replace disgarding by map lookup. Approach: piggyback selection and joins as database fragmentation instructions Impact: high

MAL Compiler (tactics) Goal: to avoid interpretation of functional expressions Rationale: interpretation of arithmetic expressions with an interpreter is always expensive. Replacing a complex arithmetic expressin with a simple dynamically compiled C-functions often pays off. Especially for cached (MAL) queries Approach: Impact: high

Dynamic Query Scheduler (tactics) Goal: to organize the work in a way so as to optimize resource usage Rationale: straight interpretation of a query plan may not lead to the best use of the underlying resources. For example, the content of the runtime cache may provide an opportunity to safe time by accessing a cached source Approach: query scheduling is the last step before a relation algebra interpreter takes over control. The scheduling step involves a re-ordering of the instructions within the boundaries imposed by the flow graph. impact: medium

Aggregate Groups Goal: to reduce the cost of computing aggregate expressions over times Rationale: many of our applications call for calculation of aggregates over dynamically defined groupings. They call for lengtly scans and it pays to piggyback all aggregate calculates, leaving their result in the cache for later consumption (eg the optimizers) Approach: Impact: High

Demand Driven Interpreter (tactics) Goal: to use the best interpreter and libraries geared at the task at hand Rationale: Interpretation of a query plan can be based on different computational models. A demand driven interpretation starts at the intended output and 'walks' backward through the flow graph to collect the pieces, possibly in a pipelined fashion. (Vulcano model) Approach: merely calls for a different implementation of the core operators Impact: high

Iterator Strength Reduction Goal: to reduce the cost of iterator execution by moving instructions out of the loop. Rationale: although iteration at the MAL level should be avoided due to the inherent low performance compared to built-in operators, it is not forbidden. In that case we should confine the iterator block to the minimal work needed. Approach: inspect the flowgraph for each iterator and move instructions around. Impact: low

Accumulator Evaluation Goal: to replace operators with cheaper ones. Rationale: based on the actual state of the computation and the richness of the supporting libraries there may exists alternative routes to solve a query. Approach: Operator rewriting depends on properties. No general technique. The first implementation looks at calculator expressions such as they appear frequently in the RAM compiler. Impact: high Prerequisite: should be called after common term optimizer to avoid clashes. Status: Used in the SQL optimizer.

Code Inliner Goal: to reduce the calling depth of the interpreter and to obtain a better starting point for code squeezing Rationale: substitution of code blocks (or macro expansion) leads to longer linear code sequences. This provides opportunities for squeezing. Moreover, at runtime building and managing a stackframe is rather expensive. This should be avoided for functions called repeatedly. Impact: medium Status: Used in the SQL optimizer to handle SQL functions.

Code Outliner Goal: to reduce the program size by replacing a group with a single instruction Rationale: inverse macro expansion leads to shorter linear code sequences. This provides opportunities for less interpreter overhead, and to optimize complex, but repetative instruction sequences with a single hardwired call Approach: called explicitly to outline a module (or symbol) Impact: medium

Garbage Collector Goal: to release resources as quickly as possible Rationale: BATs referenced from a MAL program keep resources locked. Approach: In cooperation with a resource scheduler we should identify those that can be released quickly. It requires a forced gargabe collection call at the end of the BAT's lifespan. Impact: large Status: Implemented. Algorithm based on end-of-life-span analysis.

Foreign Key replacements Goal: to improve multi-attribute joins over foreign key constraints Rationale: the code produced by the SQL frontend involves foreign key constraints, which provides many opportunities for speedy code using a join index. Impact: large Status: Implemented in the SQL strategic optimizer.