MAL Optimizers

MAL Optimizers mk Tue, 04/28/2020 - 16:30

One of the prime reasons to design the MAL intermediate language is to have a high-level description for database queries, which is easy to generate by a front-end compiler and easy to decode, optimize and interpret. In this section, we introduce the collection of MAL optimizers included in the code base. The tool kit is incrementally built, triggered by experimentation and curiousity. Several optimizers require further development to cope with the many features making up the MonetDB system. Such limitations on the implementation are indicated where appropriate.

Experience shows that construction and debugging of a front-end specific optimizer is simplified when you retain information on the origin of the MAL code produced as long as possible. For example, the snippet sql.insert(col, 12@0, "hello") can be the target of simple SQL rewrites using the module name as the discriminator.

Pipeline validation. The pipelines used to optimize MAL programs contain dependencies. For example, it does not make much sense to call the deadcode optimizer too early in the pipeline, although it is not an error. Moreover, some optimizers are merely examples of the direction to take, others are critical for proper functioning for e.g. SQL.

An optimizer needs several mechanisms to be effective. It should be able to perform a symbolic evaluation of a code fragment and collect the result in properties for further decision making. The prototypical case is where an optimizer estimates the result size of a selection. Another major issue is to be able to generate and explore a space of alternative evaluation plans. This exploration may take place up front, but can also be ran at runtime for query fragments

Alias Removal

Alias Removal mk Tue, 04/28/2020 - 17:14

The routine optimizer.aliasRemoval() walks through the program looking for simple assignment statements, e.g. V:=W. It replaces all subsequent occurrences of V by W, provided V is assigned a value once and W does not change in the remainder of the code. Special care should be taken for iterator blocks as illustrated in the case below:

	b:= "done";
barrier go:= true;
redo go:= i<2;
exit go;

The constant strings are propagated to the


routine, while the initial assigment


should be retained. The code block becomes:

barrier go:= true;
redo go:= i<2;
exit go;

A special case is backward propagation of constants. The following snippet is the result of the JITO emptyset. It can be further reduced to avoid useless assignments.

    _53 := sql.bind("sys","_tables","type",0);
    (_54,_56,_58,_60) := bat.partition(_53);
    _53 := nil;
    _67 := _54;
    _54 := nil;
    _75 := _67;
    _67 := nil;
    _83 := _75;
    _75 := nil;

Building Blocks

Building Blocks mk Tue, 04/28/2020 - 17:11

Optimizer Building Blocks

Some instructions are independent of the execution context. In particular, expressions over side-effect free functions with constant parameters could be evaluated before the program block is considered further.

A major task for an optimizer is to select instruction (sequences) which can and should be replaced with cheaper ones. The cost model underlying this decision depends on the processing stage and the overall objective. For example, based on a symbolic analysis their may exist better implementations within the interpreter to perform the job (e.g. hashjoin vs mergejoin). Alternative, expensive intermediates may be cached for later use.

Plan enumeration is often implemented as a Memo structure, which designates alternative sub-plans based on a cost metric. Perhaps we can combine these memo structures into a large table for all possible combinations encountered for a user.

The MAL language does not imply a specific optimizer to be used. Its programs are merely a sequence of specifications, which is interpreted by an engine specific to a given task. Activation of the engine is controlled by a scenario, which currently includes two hooks for optimization; a strategic optimizer and a tactical optimizer. Both engines take a MAL program and produce a (new/modified) MAL program for execution by the lower layers.

MAL programs end-up in the symbol table linked to a user session. An optimizer has the freedom to change the code, provided it is known that the plan derived is invariant to changes in the environment. All others lead to alternative plans, which should be collected as a trail of MAL program blocks. These trails can be inspected for a posteriori analysis, at least in terms of some statistics on the properties of the MAL program structures automatically. Alternatively, the trail may be pruned and re-optimized when appropriate from changes in the environment.

The rule applied for all optimizers is to not-return before checking the state of the MAL program, and to assure the dataflow and variable scopes are properly set. It costs some performance, but the difficulties that arise from optimizer interference are very hard to debug. One of the easiest pitfalls is to derive an optimized version of a MAL function while it is already referenced by or when polymorphic typechecking is required afterwards.

Building Your Own Optimizer

Implementation of your own MAL-MAL optimizer can best be started from refinement of one of the examples included in the code base. Beware that only those used in the critical path of SQL execution are thorouhly tested. The others are developed up to the point that the concept and approach can be demonstrated.

The general structure of most optimizers is to actively copy a MAL block into a new program structure. At each step we determine the action taken, e.g. replace the instruction or inject instructions to achieve the desired goal.

A tally on major events should be retained, because it gives valuable insight in the effectiveness of your optimizer. The effects of all optimizers is collected in a system catalog.

Each optimizer ends with a strong defense line, optimizerCheck() It performs a complete type and data flow analysis before returning. Moreover, if you are in debug mode, it will keep a copy of the plan produced for inspection. Studying the differences between optimizer steps provide valuable information to improve your code.

The functionality of the optimizer should be clearly delineated. The guiding policy is that it is always safe to not apply an optimizer step. This helps to keep the optimizers as independent as possible.

It really helps if you start with a few tiny examples to test your optimizer. They should be added to the Tests directory and administered in Tests/All.

Breaking up the optimizer into different components and grouping them together in arbitrary sequences calls for careful programming.

One of the major hurdles is to test interference of the optimizer. The test set is a good starting point, but does not garantee that all cases have been covered.

In principle, any subset of optimizers should work flawlessly. With a few tens of optimizers this amounts to potential millions of runs. Adherence to a partial order reduces the problem, but still is likely to be too resource consumptive to test continously.


Coercions mk Tue, 04/28/2020 - 17:21

A simple optimizer that removes coercions that are not needed. They may result from a sloppy code-generator or function call resolution decision. For example:


becomes a single assignment without function call.

The primary role is a small illustration of coding an optimizer algorithm.

Common Subexpressions

Common Subexpressions mk Tue, 04/28/2020 - 17:34

5.2.6 Common Subexpression Elimination

Common subexpression elimination merely involves a scan through the program block to detect re-curring statements. The key problem to be addressed is to make sure that the parameters involved in the repeatative instruction are invariant.

The analysis of optimizer.commonTerms() is rather crude. All functions with possible side-effects on their arguments should have been marked as 'unsafe'. Their use within a MAL block breaks the dataflow graph for all objects involved (BATs, everything kept in boxes).

The common subexpression optimizer locates backwards the identical instructions. It stops as soon as it has found an identical one. Before we can replace the expression with the variable(s) of the previous one, we should assure that we haven;t passed a non-empty barrier block.

    k1:= 24;
    k2:= 27;
    l:= k1+k2;
    l2:= k1+k2;
    l3:= l2+k1;

is translated into the code block where the first two instructions are not common, because they have side effects.

    b :=,:int);
    c :=,:int);
    d :=,0,100);
    e := d;
    l := calc.+(24,27);
    l3 := calc.+(l,24);

Constant Expressions

Constant Expressions mk Tue, 04/28/2020 - 17:36

5.2.7 Constant Expression Evaluation

Expressions produced by compilers involving only constant arguments can be evaluated once. It is particular relevant in functions that are repeatably called. One time queries would not benefit from this extra step.

Consider the following snippet, which contains recursive use of constant arguments

    a:= 1+1;        io.print(a);
    b:= 2;           io.print(b);
    c:= 3*b;        io.print(c);
    d:= calc.flt(c);io.print(d);
    e:= mmath.sin(d);io.print(e);

The code produced by the optimizer would be


Likewise we attempt to catch barrier blocks based on constants.

Cost Model

Cost Model mk Tue, 04/28/2020 - 17:38

5.2.8 Costmodel Approach

Cost models form the basis for many optimization decisions. The cost parameters are typically the size of the (intermediate) results and response time. Alternatively, they are running aggregates, e.g. max memory and total execution time, obtained from a simulated run. The current implementation contains a framework and an example for building your own cost-based optimized.

The optimizer.costModel() works its way through a MAL program in search for relational operators and estimates their result size. The estimated size is left behind as the property rows.

    r{rows=100} :=,:int);
    rr:= bat.reverse(r);
    j:= algebra.join(rs,rr);

changes the properties of the instructions as follows:

    r{rows=100} :=,:int);
    s{rows=1000} :=,:int);
    rs{rows=501} :=,1,1);
    rr{rows=100} := bat.reverse(r);
    j{rows=100} := algebra.join(rs,rr);

The cost estimation does not use any statistics on the actual data distribution yet. It relies on the rows property provided by the front-end or other optimizers. It just applies a few heuristic cost estimators. However, it ensures that empty results are only tagged with rows=0 if the estimate is accurate, otherwise it assumes at least one result row. This property makes it possible to safely pass the result of the cost estimation to the emptySet optimizer for code reduction.

Data Flow

Data Flow mk Tue, 04/28/2020 - 17:41

5.2.9 The dataflow optimizer

MAL programs are largely logical descriptions of an execution plan. At least as it concerns side-effect free operations. For these sub-plans the order of execution needs not to be a priori fixed and a dataflow driven evaluation is possible. Even using multiple worker threads to work their way through the dataflow graph.

The dataflow optimizer analyses the code and wraps all instructions eligible for dataflow driven execution with a guarded block:

BARRIER X_12:= language.dataflow();
  .... side-effect-free MAL calls ...
EXIT X_12;

Ofcourse, this is only necessary if you can upfront determine there are multiple threads of execution possible.

Upon execution, the interpreter instantiates multiple threads based on an the number of processor cores available. Subsequently, the eligible instructions are queued and consumed by the interpreter worker threads. A worker thread tries to continue processing with an instruction that needs the latest intermediate result produced. This improves locality of access and shortens the lifetime of temporary structures.

Dataflow blocks may not be nested. Therefore, any dataflow block produced for inlined code is removed first.

Dead Code

Dead Code mk Tue, 04/28/2020 - 17:42

5.2.10 Dead Code Removal

Dead code fragments are recognized by assignments to variables whose value is not consumed any more. It can be detected by marking all variables used as arguments as being relevant. In parallel, we built a list of instructions that should appear in the final result. The new code block is than built in one scan, discarding the superflous instructions.

Instructions that produce side effects to the environment, e.g., printing and BAT updates, should be taken into account. Such (possibly recursive) functions should be marked with a property (unsafe). For now we recognize a few important ones Likewise, instructions marked as control flow instructions should be retained.

An illustrative example is the following MAL snippet:

	V7 :=,:int);
	V10 :=,:oid);
	V16 := algebra.markH(V7);
	V17 := algebra.join(V16,V7);
	V19 :=,:int);
	V22 :=,:int);
	V23 := algebra.join(V16,V22);

The dead code removal trims this program to the following short block:


A refinement of the dead code comes from using arguments that ceased to exist due to actions taken by an optimizer. For example, in the snippet below the pushranges optimizer may conclude that variable V31 becomes empty and simply injects a 'dead' variable by dropping the assignment statement. This makes other code dead as well.

     	V30 := V7, 10,100);
     	V31 :=,-1,5);
     	V32 := aggr.sum(V31);

[implementation pending]

Garbage Collector

Garbage Collector mk Tue, 04/28/2020 - 17:44

5.2.13 Garbage Collection

Garbage collection of temporary variables, such as strings and BATs, takes place upon returning from a function call.

Join Paths

Join Paths mk Tue, 04/28/2020 - 17:49

5.2.15 Join Paths

The routine optimizer.joinPath() walks through the program looking for join operations and cascades them into multiple join paths. To illustrate, consider

	j1:= algebra.join(a,b);
	j2:= algebra.join(j1,c);
	j3:= algebra.join(b,b);
	j4:= algebra.join(b,j3);

The optimizer will first replace all arguments by their join sequence. The underlying instructions are left behind for the deadcode optimizer to be removed.

	j1:= algebra.join(a,b);
	j2:= algebra.joinPath(a,b,c);
	j3:= algebra.join(b,b);
	j4:= algebra.joinPath(b,b,b);

The same operation is applied to sequences of leftjoin operations.

Any join result used multiple times should be calculated once. A list of inlined paths is kept around to detect those.

In principle, the joinpaths may contain common subpaths, whose materialization would improve performance.

The SQL front-end produces often produces snippets of the following structure

    t1:= algebra.join(b,c);
    z1:= algebra.join(a,t1);
    t2:= algebra.join(b,d);
    z2:= algebra.join(a,t2);

The joinpath would merge them into

    z1:= algebra.joinPath(a,b,c);
    z2:= algebra.joinPath(a,b,d);

which are handle by a heuristic looking at the first two argments and re-uses a materialized join.

    _13:= algebra.join(a,b);
    z1:= algebra.join(_13,c);
    z2:= algebra.join(_13,d);

An alternative is to make recognition of any common re-useable paths an integral part of the joinPath body.

    x3:= algebra.join(a,b);
	r3:= bat.reverse(x3);
	j1:= join(c,r3);
	rb:= bat.reverse(b);
	ra:= bat.reverse(a);
	j1:= algebra.joinpath(c,rb,ra);

As a final step in the speed up of the joinpath we consider clustering large operands if that is expected to improve IO behavior.


Landscape mk Tue, 04/28/2020 - 16:35

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.


Lifespans mk Tue, 04/28/2020 - 17:12

Optimizers may be interested in the characteristic of the barrier blocks for making a decision. The variables have a lifespan in the code blocks, denoted by properties beginLifespan,endLifespan. The beginLifespan denotes the intruction where it receives its first value, the endLifespan the last instruction in which it was used as operand or target.

If, however, the last use lies within a BARRIER block, we can not be sure about its end of life status, because a block redo may implictly revive it. For these situations we associate the endLifespan with the block exit.

In many cases, we have to determine if the lifespan interferes with a optimization decision being prepared. The lifespan is calculated once at the beginning of the optimizer sequence. It should either be maintained to reflect the most accurate situation while optimizing the code base. In particular, it means that any move/remove/addition of a MAL instruction calls for either a recalculation or further propagation. Unclear what will be the best strategy. For the time being we just recalc.

See is all arguments mentioned in the instruction at point pc are still visible at instruction qc and have not been updated in the mean time. Take into account that variables may be declared inside a block. This can be calculated using the BARRIER/CATCH and EXIT pairs.

The safety property should be relatively easy to determine for each MAL function. This calls for accessing the function MAL block and to inspect the arguments of the signature.

Any instruction may block identification of a common subexpression. It suffices to stumble upon an unsafe function whose parameter lists has a non-empty intersection with the targeted instruction. To illustrate, consider the sequence

     L1 := f(A,B,C);
     G1 := g(D,E,F);
     l2:= f(A,B,C);
     L2:= h()

The instruction G1:=g(D,E,F) is blocking if G1 is an alias for {A,B,C}. Alternatively, function g() may be unsafe and {D,E,F} has a non-empty intersection with {A,B,C}. An alias can only be used later on for readonly (and not be used for a function with side effects).

Macro Processing

Macro Processing mk Tue, 04/28/2020 - 17:51

5.2.16 Macro and Orcam Processing

The optimizers form the basis for replacing code fragments. Two optimizers are focused on code expansion and contraction. The former involves replacing individual instructions by a block of MAL code, i.e. a macro call. The latter depicts the inverse operation, a group of instructions is replaced by a single MAL assignment statement, i.e. a orcam call.

The macro facility is limited to type-correct MAL functions, which means that replacement is not essential from a semantic point of view. They could have been called, or the block need not be compressed. It is equivalent to inline code expansion.

The macro and orcam transformations provide a basis to develop front-end specific code generation templates. The prototypical test case is the following template:

     function user.joinPath( a:bat[:any_1,:any_2],
     address fastjoinpath;
         z:= join(a,b);
         zz:= join(z,c);
         return zz;
     end user.joinPath;

The call optimizer.macro("user", "joinPath") hunts for occurrences of the instruction call in the block in which it is called and replaces it with the body, i.e. it in-lines the code. Conversely, the optimizer.orcam("user", "joinPath") attempts to localize a block of two join operations and, when found, it is replaced by the direct call to joinPath. In turn, type resolution then directs execution to a built-in function fastjoinpath.

The current implementation is limited to finding a consecutive sequence, ending in a return-statement. The latter is needed to properly embed the result in the enclosed environment. It may be extended in the future to consider the flow of control as well.

5.2.17 Known issues

The functions subject to expansion or contraction should be checked on 'proper' behavior.

The current implementation is extremely limited. The macro optimizer does not recognize use of intermediate results outside the block being contracted. This should be checked and it should block the replacement, unless the intermediates are part of the return list. Likewise, we assume here that the block has a single return statement, which is also the last one to be executed.

The macro optimizer can only expand functions. Factories already carry a significant complex flow of control that is hard to simulate in the nested flow structure of an arbitrary function.

The orcam optimizer can not deal with calls controlled by a barrier. It would often require a rewrite of several other statements as well.

     pattern optimizer.macro(targetmod:str,targetfcn:str):void
     address OPTmacro
     comment "Inline the code of the target function.";
     pattern optimizer.macro(mod:str,fcn:str,targetmod:str,targetfcn:str):void
     address OPTmacro
     comment "Inline a target function used in a specific function.";
     pattern optimizer.orcam(targetmod:str,targetfcn:str):void
     address OPTorcam
     comment "Inverse macro processor for current function";
     pattern optimizer.orcam(mod:str,fcn:str,targetmod:str,targetfcn:str):void
     address OPTorcam
     comment "Inverse macro, find pattern and replace with a function call.";


Memoization mk Tue, 04/28/2020 - 17:59

5.3 Memo-based Query Execution

Modern cost-based query optimizers use a memo structure to organize the search space for an efficient query execution plan. For example, consider an oid join path 'A.B.C.D'. We can start the evaluation at any point in this path.

Its memo structure can be represented by a (large) MAL program. The memo levels are encapsulated with a choice operator. The arguments of the second dictate which instructions to consider for cost evaluation.

     	T1:= algebra.join(A,B);
     	T2:= algebra.join(B,C);
     	T3:= algebra.join(C,D);
     	T4:= algebra.join(T1,C);
     	T5:= algebra.join(A,T2);
     	T6:= algebra.join(T2,D);
     	T7:= algebra.join(B,T3);
     	T8:= algebra.join(C,D);
     	T9:= algebra.join(T4,D);
     	T10:= algebra.join(T5,D);
     	T11:= algebra.join(A,T6);
     	T12:= algebra.join(A,T7);
     	T13:= algebra.join(T1,T8);
     	answer:= scheduler.pick(T9, T10, T11, T12, T13);

The scheduler.choice() operator calls a builtin getVolume for each target variable and expects an integer-valued cost. In this case it returns the total number of bytes uses as arguments.

The target variable with the lowest cost is chosen for execution and remaining variables are turned into a temporary NOOP operation.(You may want to re-use the memo) They are skipped by the interpreter, but also in subsequent calls to the scheduler. It reduces the alternatives as we proceed in the plan.

A built-in naive cost function is used. It would be nice if the user could provide a private cost function defined as a pattern with a polymorphic argument for the target and a :lng result. Its implementation can use the complete context information to make a decision. For example, it can trace the potential use of the target variable in subsequent statements to determine a total cost when this step is taken towards the final result.

A complete plan likely includes other expressions to prepare or use the target variables before reaching the next choice point. It is the task of the choice operator to avoid any superfluous operation.

The MAL block should be privately owned by the caller, which can be assured with scheduler.isolation().

A refinement of the scheme is to make cost analysis part of the plan as well. Then you don't have to include a hardwired cost function.

     	Acost:= aggr.count(A);
     	Bcost:= aggr.count(B);
     	Ccost:= aggr.count(C);
     	T1cost:= Acost+Bcost;
     	T2cost:= Bcost+Ccost;
     	T3cost:= Ccost+Dcost;
     	scheduler.choice(T1cost,T1, T2cost,T2, T3cost,T3);
     	T1:= algebra.join(A,B);
     	T2:= algebra.join(B,C);
     	T3:= algebra.join(C,D);

Multiplex Functions

Multiplex Functions mk Tue, 04/28/2020 - 18:03

5.3.2 Multiplex Compilation

The MonetDB operator multiplex concept has been pivotal to easily apply any scalar function to elements in a containers. Any operator cmd came with its multiplex variant [cmd]. Given the signature cmd(T1,..,Tn) : Tr, it could be applied also as [CMD](bat[:any_1,:T1],...,bat[any_1,Tn]) :bat[any_1,Tr].

The semantics of the multiplex is to perform the positional join on all bat-valued parameters, and to execute the CMD for each combination of matching tuples. All results are collected in a result BAT. All but one argument may be replaced by a scalar value.

The generic solution to the multiplex operators is to translate them to a MAL loop. A snippet of its behaviour:

    c:bat[:int,:int]:= mal.multiplex("calc.+",b,1);

The current implementation requires the target type to be mentioned explicitly. The result of the optimizer is:

    b :=,:int);
    _8 :=,:int);
barrier (_11,_12,_13):= bat.newIterator(b);
    _15 := calc.+(_13,1);
    redo (_11,_12,_13):= bat.hasMoreElements(b);
exit (_11,_12,_13);
    c := _8;

Remote Actions

Remote Actions mk Tue, 04/28/2020 - 18:18

Remote Queries

MAL variables may live at a different site from where they are used. In particular, the SQL front-end uses portions of remote BATs as replication views. Each time such a view is needed, the corresponding BAT is fetched and added to the local cache.

Consider the following snippet produced by a query compiler,

mid:= mapi.reconnect("s0_0","localhost",50000,"monetdb","monetdb","mal");
b:bat[:oid,:int] := mapi.bind(mid,"rvar");,0,12);
low:= 5+1;,low,7);
io.printf(" count %d\n",i);

which uses a BAT


stored at the remote site



There are several options to execute this query. The remote BAT can be fetched as soon as the bind operation is executed, or a portion can be fetched after a remote select, or the output for the user could be retrieved. An optimal solution depends on the actual resources available at both ends and the time to ship the BAT.

The remote query optimizer assumes that the remote site has sufficient resources to handle the instructions. For each remote query it creates a private connection. It is re-used in subsequent calls .

The remote environment is used to execute the statements. The objects are retrieved just before they are locally needed.

mid:= mapi.reconnect("s0_0","localhost",50000,"monetdb","monetdb","mal");
mapi.rpc(mid,"b:bat[:oid,:int] :=bbp.bind(\"rvar\");");
c:bat[:oid,:int]:= mapi.rpc(mid, "io.print(c);");
low:= 5+1;
i:= mapi.rpc(mid,"io.print(i);");
io.printf(" count %d\n",i);

To reduce the number of interprocess communications this code can be further improved by glueing the instructions together when until the first result is needed.

Stack Reduction

Stack Reduction mk Tue, 04/28/2020 - 18:21

Stack Reduction

The compilers producing MAL may generate an abundance of temporary variables to hold the result of expressions. This leads to a pollution of the run-time stack space, because space should be allocated and garbage collection tests should be performed.

The routine optimizer.reduce() reduces the number of scratch variables to a minimum. All scratch variables of the same underlying type share the storage space. The result of this optimizer can be seen using the MonetDB debugger, which marks unused variables explicitly. Experience with the SQL front-end shows, that this optimization step easily reduces the stack consumption by over 20%.