Knowledge Base


SQL Joins -- The Long and The Short of It

Paul Kent, SAS Institute Inc.


This talk discusses joining tables with PROC SQL. When joining small tables one can just "go with the defaults" and not worry about performance; these defaults do not always scale well to large tables. The issue is clouded even further when one or more of the tables reside in an external DBMS.

Understanding the choices made by the PROC SQL query optimizer helps people decide on a course of action they might embark on to improve the join performance of their queries. This talk presents useful information on the SQL Query Optimizer, and includes cases studies that demonstrate a "cookbook" strategy for improving SQL Join performance.


The conceptual model of an SQL join is easy to understand. Simply compute all combinations of the rows from the contributing datasets and then eliminate the rows that don't "match" the WHERE clause. This allows users to specify what they want in the SQL query, without becoming overly involved in how to get it.

In effect the user allows PROC SQL to decide on the most appropriate processing strategy to satisfy the request -- contrast this with less modern SAS code that is chock full of procedural details like PROC SORT steps.

Most SQL joins are ones that have an equality in the WHERE clause specifying which columns must match. PROC SQL can process these joins more efficiently than the general case. Joins that do have an equality like this in the WHERE clause are referred to as Equi-joins; those that do not are called Cartesian joins.

Cartesian Joins

In the general case, PROC SQL has no choice but to compare each row from one table with all rows from the others. It must:

  • read rows from one table into memory
  • compare each row from the other table with those in memory to decide if the where clause is satisfied.

PROC SQL can be more efficient that the DATA STEP code shown below, but it does provide a picture of the processing involved. Instead of reading one row at a time from the table_B and processing each row of table_A for that row from table_B, we attempt to load as much of table_A into memory (an array if you will), before starting to see if rows from table_B match the WHERE clause.

     data result;
       set table_B;
       do i = 1 to aobs;
         set table_A nobs=aobs point=i;
         <does this satisfy where clause?>


PROC SQL can solve queries that specify an equals match between variables from both tables in a number of optimized ways. The SQL Query Optimizer must choose between:

  • Sorting the tables and performing a match merge. (Of course, we can avoid the sort for tables that have a known order that is useful)
  • Accessing the rows of one table sequentially, and fetching the matching rows from the other table via an index defined on that table,
  • Loading the rows of the smaller table into memory and processing the rows from the other table sequentially, using table-lookup techniques to locate matching rows.

Not all Equijoins are one-to-many joins -- SQL Join semantics require that all rows with a specific join key are compared with all rows from the other table having the same join key. The presence of an equijoin predicate in the WHERE clause just means that PROC SQL might have to consider many smaller Cartesian products instead of one large one.

Processing a few small Cartesian products is cheaper than processing the one large one. Assume:

  • Two tables M and N with Mr and Nr rows respectively
  • A join key K with Kv unique values

A complete Cartesian product of M and N involves Mr x Nr compares.

Several (Kv) smaller Cartesian products (one for each unique value of K) involve Kv x ((Mr / Kv) x (Nr / Kv)) Compares. This Simplifies to (Mr x Nr) / Kv compares.

Attaching some real numbers to this may help. Suppose two tables with 100 rows and 10 key values. A full Cartesian product involves 100 x 100 rows (10,000 compares). A Cartesian products on a set of records with the same key value involves 10 x 10 rows, but you have to do 10 of them (one for each group) -- This nets out to 1000 compares and is less work than the 10,000 compares required for the full Cartesian product.

Sort-Merge Joins

The input datasets are processed in sort-group sized chunks. SQL does not need to consider each combination of record matches, just the matches within rows of the same sort-group. This method requires that the input data be sorted in sort-group order -- or be marked as sorted in the SAS Dataset header information. Sort Merge Joins are good performers when the bulk of the dataset is being joined.

Indexed Joins

If one of the input datasets has an index on the variables used as the join key, it is possible to access the matching records directly via that index. This method requires that you define an index and performs well when accessing a small fraction of the total number of records in a dataset.

Creating an index is not without cost, but at least it is a fixed cost that can be amortized over many queries.

In-memory Joins

The smaller input dataset may fit into memory. In this case, PROC SQL can load the rows into an in memory table that provides fast access to the matching row given the join key values. A disadvantage of this method is that it requires that you have enough memory to store the smaller dataset.

So Many Choices...

How does PROC SQL choose from all these methods? We calculate the relative sizes of the input datasets and make an "educated guess" using these heuristics. We choose:

  • Indexed If there are any candidate indexes
  • Merge If one or both of the datasets are already sorted in

    a convenient sort order

  • Hash If one of the datasets will fit into memory. Actually

    if 1% of the dataset will fit in a single memory extent whose size is the sql buffersize (64000 bytes)

  • otherwise PROC SQL will choose to sort the incoming datasets and

    perform the Sort Merge algorithm to process the join.

What did SQL choose?

There is no simple way to tell. Other Data Base Management Software has features that explain the query strategy chosen by the DBMS. We hope to implement this in PROC SQL in a future release.

The SAS System option msglevel=i will display an informatory note when an index is selected for WHERE clause or join processing. There is also an undocumented _method option on the PROC SQL statement that will display an internal form of the query plan by showing the hierarchy of processing that will be performed. The module codes used in this display are:

sqxcrta Create table as Select
sqxslct Select
sqxjsl Step Loop Join (Cartesian)
sqxjm Merge Join
sqxjndx Index Join
sqxjhsh Hash Join
sqxsort Sort
sqxsrc Source Rows from table
sqxfil Filter Rows
sqxsumg Summary Statistics (with GROUP BY) sqxsumn Summary Statistics (not grouped) sqxuniq Distinct rows only

For example, if one of the contributing datasets is sorted on key already, then the SQL:

     proc sql _method;
       select *
         from a,b
        where a.key = b.key;

would display the query plan below in the SAS Log file. The SELECT module (sqxslct) gets its input records from the Merge Join Module (sqxjm), which gets its input from two sources. The first source is passed through a sorting process, while the second is not -- it is already sorted in a useful order.

     NOTE: SQL execution methods...

If neither contributing dataset is sorted, but one of them does fit into memory, PROC SQL will choose to process the query with a Hash Join (sqxjhsh), as shown below:

NOTE: SQL execution methods...


Indexes -- Not always a Silver Bullet

Many people are surprised by the poor performance of indexes when the number of records being extracted via the index is a significant fraction of the total. To help understand why this is so, I like to present the analogy of the postman delivering the mail. Instinct tells us that he sorts the mail in his mail bag into delivery address order and proceeds to deliver the mail in street order -- he knows that the houses on his route have an order, and so by arranging his mail bag in the same order he can deliver the mail in sequence, and in a timely fashion.

If the mailman were to use an index (the street address) to avoid doing a sort of the mail bag, delivering the mail might go like this:

  • Take the next envelope from the mail bag
  • Locate the house by consulting a map (lookup up the record address in the index)
  • Walk to that house (read the record)
  • Deliver the envelope (output a matched row)
  • Repeat until mail bag is empty (or until very tired!)

It is the cost of walking to the house for each envelope that makes this an unworkable method of delivering mail -- it would only be practical for a very small mail bag (say on every light mail day).

The postman probably uses the known sort order of the mail bag to his advantage to optimize his delivery. If he has just delivered mail to 101 main street, and the next mail is for 301 main street, he knows how many houses he can skip in the meantime. He may even be able to skip entire streets if he has just delivered mail to street A, and the next mail in the (sorted) mail bag is to street C -- voila! no need to visit street B today.

Cookbook Questions to ask?

In order to Improve the performance of your SQL queries, you should examine these situations and see if any of the "remedies" can be applied to your problem.

Questions to ask of the larger table

Can I make an index? This may help if you are joining a small table with the large indexed one. However, after a certain threshold (around 15%) of the large table accessed, it becomes more expensive to access via the indirection of the index than to simply process the entire large dataset.

Can I indicate an order? You may be able to sort the large dataset -- This will help if you make more than one query against it. If the dataset is not sorted, PROC SQL will have to sort it into an internal temporary file for each query that accesses it.

You may be extracting the data from an external file system whose order is well known. You should tell SAS of this order (with the SORTEDBY= dataset option) so that we do not perform redundant sorting inside PROC SQL.

Is this large table in a DBMS? There are many SUGI Papers that deal with this issue. Try [KENT], [LOREN], [SCOTT] for papers that focus on this subject. The member SQLJMAC from the SAS Sample Library implements one approach for extracting a list of keys to pass to the DBMS in a where clause. After much experimentation I think the best approach is to have a SAS program generate another SAS program -- this approach is shown in the examples towards the end of this paper.

Questions to ask of the smaller table

Can I build a list of unique keys? If you can, you may be able to use a technique that uses these keys to write an extended WHERE clause. This technique can be especially profitable if the larger table is in a DBMS.

Will the small table fit into memory? If it does, PROC SQL will select a hash join -- you will know this by the presence of a sqxjhsh in the output generated by the _method option. We will not select an hash join if our estimate shows that approximately 1% of the rows and columns from the dataset will not fit in one SQL buffer.

If your SAS job has lots of virtual memory available, you may be able to get PROC SQL to select hash join for larger tables by specifying the (heretofore undocumented) buffersize= SQL option on the PROOC SQL statement - the default buffer size is 64000 bytes of memory.

Questions to ask of the WHERE Clause?

Can I fully specify the predicates? This may allow PROC SQL more freedom in determining the order in which to evaluate joins.

However, in Release 6.11 of SAS Software PROC SQL will perform these transformations automatically. For example, the test for A.X= 20 can be extended through the equijoin predicate to imply that B.Y= 20 must also be true.

     WHERE A.X=B.Y
       AND A.X=20
       /* internally added: AND B.Y=20 */

Can I avoid a Cartesian product of the entire table? It may be possible to re-specify the query to avoid crossing all the rows. Sometimes this can be accomplished by preprocessing one of the tables to put it in a more suitable form. Sometimes the addition of another table to the query may result in less work needed to accomplish the solution.

Examples of Equi-joins

Small table joined to Large table

In this example, we explore improving the performance of the equijoin on a common variable KEY. The small table has about 800 records which is a very favorable ratio to the number of records (1 million) in the large table and should lead us to create an index on the key column of the large table.

First some test data and the obvious solution:

     data small(drop=data) large;
       do key=1 to 1000000;
         length data $12;
         data=put(key, words12.);
         if mod(key,1234)=0 then output small;
         output large;

     proc sql _method;
       create table result as
       select large.key,
         from small, large
        where small.key=large.key;

/* The _method output is: */


This indicates that PROC SQL will load the small table into memory and process each row of the large table doing an in-memory table lookup to see if there is a match. On my HP735 with 64MB of memory, this query took 38.6 CPU seconds.

Creating an index is the solution here. The same query runs in 1.4 CPU seconds and the _method output is:


Not so Small table joined to Large table

If the small table were larger than 800 rows you might see that PROC SQL had to resort to a sort-merge join, when it determines that the smaller table no longer fits into a hash table (that is, into memory). This would he quite expensive as it would have to sort the million row table as well as the small one. If we create a small table with 17500 observations (only a 1.75% subset) we see that the hash join is no longer selected.

     data work.small4(drop=data);
       set large;
       if mod(key,57)=0;

     proc sql _method stimer;

       create table result as
       select large.key,
         from work.small4, large
        where small4.key=large.key;

     /* methods chosen */

PROC SQL has chosen to sort both tables (on key) into temporary work space and sort merge them. This was expensive! -- it took 138.3 CPU seconds to process this query.

It may be that you know the order of the large file even though SAS does not. In our case we generated the large file with a do loop but more likely you have just read it from a flat file that has an order. If this is the case by all means let SAS know using the sortedby= dataset option.

     create table result as
     select large.key,
       from work.small4, large(sortedby=key)
      where small4.key=large.key;

     /* methods chosen */

This query took 47.9 CPU seconds. We spent a large fraction of the time sorting the large table unnecessarily in the previous case!

There is still room for improvement. A hit-rate of 1.75% between the small table and the large one should prompt us to, investigate creating an index.

create index key on large(key);
create table result as
select large.key,
from work.small4, large
where small4.key=large.key;
drop index key from large;

/* methods chosen */


This query took 18.7 CPU seconds (although it did take 185.8 CPU seconds to create the index). Obviously if this query is run only once against the large dataset then we have wasted resources by creating the index. If we run many different "small" tables against the large one, then creating an index is a win.

Just as an experiment, I upped the SQL buffersize to 256K (from its default of 64K). This allows the small table to fit into a hash-table in memory.

reset buffersize=256000;
create table result as
select large.key,
from work.small4, large
where small4.key=large.key;

/* methods chosen */


This query took 37 CPU seconds. You may be able to up the buffersize parameter in your SQL queries to allow tables to fit into memory but you should benchmark this on your computer -- sometimes you'll just swamp the paging ability of your system and not achieve the gains demonstrated here.

The hash join is faster than the sort-merge even though the sort could have completed in memory. This is due to the overhead in interfacing to SAS sort method (which includes the hooks to allow the user to substitute a host sort routine).

Small table joined to Large table in DBMS

Use the same tables as in the previous example but assume that the larger table lives in a DBMS supported by SAS/ACCESS Software. This SAS code will "write another SAS program " that contains the key values from the small table as hard coded values in WHERE clauses for the large table.

Experience shows that doing this in "chunks" of 100 or so keys at a time is the best approach - many DBMS optimizers will not select an index if the IN clause contains more than that many values. I have left the "chunk" size as a macro variable to make it easy for you to experiment with this -- and experiment you should! The optimal number will vary from DBMS to DBMS, from table to table, and will be influenced by the length of the key being matched on.

This code just sets up a list of unique key values.

%let chunk=105;

     proc sql;
       create view uniq as
       select unique key
         from small
        order by key;

     data _null_;
       file temp;
       set uniq end=end;

When processing the first chunk we want to use a create table statement to create the result so our program (which is writing a SAS program) outputs:

     if _n_ = 1 then do;
       put "create table result as"
         / "  select key,data"
         / "  from connection to dbms"
         / " (select key,data"
         / " from large where key in("
         / key;

When we get to the end of a "chunk" of keys we close out that statement. If we need to start another "chunk" (i.e. we are not at then end of the keys) we want to insert the next batch of returned rows into the result that is already being accumulated.

     else if mod(_n_, &chunk) = 0
       and not end then do;
       put "));" //;

       put "insert into result"
        / " select key, data"
        / " from connection to dbms"
        / "(select key,data"
        / "from large where key in("
        / key;


     else if end then do;
        put key "));" //;

Usually, however, we are in the middle of a chunk of keys so, all we add to the program under construction is the value of this key in a form that the IN clause is expecting. If you adopt this example and have a character variable as your key, don't forget to place quotes around the values.

     else put key ",";

Finally we run the program that we have built.

     proc sql;
       connect to <DBMS> as dbms;
       %inc temp;

Examples of Useful Cartesian Joins

Cartesian Joins often solve real world problems in elegant ways. The challenge is to recast the SQL to solve them efficiently. I have collected these examples from my contacts with SAS users:

Nearest Neighbors problem

A researcher at UNC said: I have a query about DATA steps that is strange enough to have baffled not only the SAS tech support folks, but also Sally Muller ;->. I am writing a macro to perform adaptive bandwidth kernelling, and need to perform a large number of calculations of the form F(Xi - Xj) where F is some function, X is my variable and i & j subscript observations. I do not need to compute this function for all possible i & j combinations, only those where |Xi - Xj| < 2.3 (X is sorted into increasing order). My general question is how best to set this up: this kernelling is part of a complex bootstrapping problem, so it will run 50,000 - 75,000 times with 15 to 5000 observations each time.

This sounds like a good application of SQL. Finding all combinations of rows such that the difference in variable X is less than 2.3 sounds like the Cartesian product of two copies of the same table. Luckily PROC SQL allows you just to use the same table name twice and not generate 2 copies. First some test data:

     data testdata;
       do i=1 to 5000;
          x= 100*ranuni(0);

     proc sort;
       by x;

The genesis of the final solution is due to Howard Schreier - He notes the seeming paradox that making the query more complicated seems to be a good way to reduce its runtime.

Let us start with the obvious SQL formulation. We don't have to consider the cases where X1 is less than X2 (by as much as 2.3) as the Cartesian product considers both Xi with Xj as well as Xj with Xi.

proc sql _method stimer;

       create table results as
       select x1.i as i1,
              x1.x as x1,
              x2.i as i2,
              x2.x as x2
         from testdata x1,
              testdata x2
        where x1.x - x2.x
      between 0 and 2.3;

Whew! that was a lot of work. It took 537 CPU seconds on my (nice fast) HP735. How can we reduce the Cartesian product requirement from 5000 * 5000 (25 million compares)?. The answer lies in augmenting the LESS THAN predicate with an equijoin that allows us to test fewer combinations - the equijoin predicate does not need to be exact we can always "fine tune" the result set with the existing WHERE clause.

The clue to the solution is that records must be no more than 2.3 apart in X value. If we convert X to an integer we only have to consider 4 cases X1 = X2-3 thru X1 = X2. We can use a Cartesian join to create these four values and then compare for equality. This factors out to 5000 * 4 compares to compute the four potential offsets followed by a 5000 * 20000 equijoin.

First we output a table of the offsets:

data offset;
do offset =0 to 3;



Then we select using this table. PROC SQL will form an intermediate result for the x2, offset join, and join that intermediate result with x1.

proc sql _method stimer;

create table work.results as
select x1.i as i1,

           x1.x as x1,
           x2.i as i2,
           x2.x as x2
      from testdata x1,
           testdata x2,
     where x1.x - x2.x
    between 0 and 2.3
     and floor(x1.x) + offset

This ran in 41.2 CPU seconds. The key to this faster solution is to convert the Cartesian join overall rows to an equijoin over slightly more rows, but with a WHERE clause that limits the matching that is required. If the two X values had to be within 100 of each other, it may have become prohibitive to consider all 101 offsets, but you could modify the solution to compute the offset to the nearest 10 using the ROUND, function.

I don't have a name for this kind of optimization, but as a general principle, any time you can reduce the search space of potential answers you can probably reduce the resources required to find the solution.

Date Ranges

A PROC SQL Supporter on SAS-L says:

I have two files I want to match. One is of bills, the other is of discounts:

        Bill  Date    State   Amount
        1     1/1/94  CA      100
        2     12/1/93 CA      200
        3     1/15/94 KY      80

      Start   End     State   discount
      1/1/94  2/1/94  CA      .5
      1/1/94  2.1.94          .25

For each bill, I want to find a matching discount record. First I look for a state match, and if there is no state match, I look for a blank state in the discounts file. The bill date has to be within the start and end dates of the discount record. This billing data has Millions of records.

This is currently done in a batch DBMS job, which uses SQL and some host language. That program checks one record at a time and handles mismatches as they occur, something like:

  • get a discount record where date and state match
  • if OK then newbill = amount * discount; go to next record
  • get a discount record where the date matches
  • if OK then newbill = amount * discount; go to next record
  • oops, error!

We sometimes want to reproduce this program in SAS, but there doesn't seem to be a way to do the exception handling.

I created some test data (included so that you can play with these examples once you are at your computer).

     data billing;
       length state $2;
       input date date7. +1 bill state $ amount;
       format date date7.;
     01jan94 1 CA 100
     01dec93 2 CA 200
     15jan94 3 KY 80
     01jan94 4 NC 1000
     01feb94 5 NC 1000
     01mar94 6 NC 1000
     01apr94 7 NC 1000
     01may94 8 NC 1000

     data discount;
       input start date7. +1 end date7. +1 state $2. discount;
       format start end date7.;
     01jan94 01feb94 CA .5
     15feb94 15mar94 NC .4
     16mar94 15apr94 NC .3
     01jan93 31dec93    .22
     01jan94 01feb94    .25
     02feb94 31dec94    .7

The original solution goes like this.... First we compute discounts for those bills that have no state specific discount

     create table stmatch as
     select distinct *,
          b.amount* as newbill
      from billing b discount d
      where b.state =  d.state
        and  >= d.start
        and  <= d.end;

Then we compute discounts for those bills that had no state specific discount and so the general discount should apply.

     Create table blmatch as
     select distinct *,
            b.amount* as newbill
       from billing b, discount d
       where ' ' = dstate
         and >= d.start
         and <= d.end
         and b.bill not in
           (select bill from stmatch);

       /* display results */
       select * from stmatch
       union all
       select * from blmatch;

The solution is not satisfactory because the second create table has to check and see if the particular bill has matched a state specific discount before matching the general (state is blank) case.

My advice is to turn the discounts dataset upside down, and do the error checking up front. Instead of storing a range for which the discount is valid, store each day and its discount. (yup, you guessed -- turn a Cartesian join into an equijoin). An added benefit is that you can have the state specific discount and the general discount on the same row -- this will help avoid two queries later.

One drawback of my advice (besides it being worth what you paid for it) is that we do need to know the extent of the dates in the billing and discount datasets. Since this is just an example, I'll assume we know them up front :-) We also need the list of unique states, which I get from a pass over the billings dataset, but in practice you'll know this list, won't you?

First we compute a table with all the dates in the range we are considering:

%let mindate='01jan93'd;
%let maxdate='01jan94'd;

data dates;
do date= &mindate to &maxdate;


format date date7.;

Now we extract per-state information from the discount table:

proc sql _method stimer _tree;

  • get the uniq state names; create view stname as select unique state from billing;
  • get the state specific discounts; create view states as select state,

    start as s_start, end as s_end, discount as s_disc from discount where state ^= ' ';

When we calculate the general discounts, we need to "expand" them so that they apply to all states in the billing dataset. Here I join them with the stname view, but in practice you'll probably have a validation table that lists valid states.

*get the general discounts;
create view general as
select s.state,

         start as g_start,
         end   as g_end,
         discount as g_disc

from discount d, stname as s
where d.state= ' ';

So now put that all together with a left join to make sure that you don't lose any of the days in the window that we are considering. The original request needed error validation -- you could check that a general discount was available for every date in the range at this point. In other words, do the error check "up front".

  • create the daily discount lookup table; create table tempdisc as select date, g.state, s.disc, g.disc from dates d left join general g on date between g_start

    and g_end left join states s on date between s_start

    and s_end and g.state=s.state;

Now we have daily discount rates and the problems reverts back to one of joining a large dataset (billings) with a smaller one (discounts) which has daily discount rates. We can use the COALESCE function to, choose an appropriate discount.

Create table results as
select b.*,

             1) as disc,

b.amount * calculated disc as newbill from billings b,

tempdisc t
where =
and b.state = t.state;

The tempdisc data is approximately 37000 observations of say 25 bytes. This will probably fit into an in memory table, so the join is resolved with a single pass over the huge million row billings table.

Fuzzy Logic and SQL.

An excellent paper (Best Contributing Paper, [CORELLE] was presented at SUGI 17. The task at hand was to merge two tables collected by different institutions. There were many "almost" matches but very few exact hits. The authors of this paper identified several matching criteria and consider two records a match if they meet some but not all of the criteria.

They rank some criteria more important than others, and exploit the identity that logical expressions are always 1 or 0 by multiplying the logical expression by the "importance" of the criteria. If an observation meets enough criteria so as to raise its "match score" above a threshold, it is considered a keeper.

They solve the problem with a Cartesian Join whose search space is trimmed down by an equality check:

     select *
       from file1 a, file2 b
        and ( ( ( * 2 )
            + ( ( a.mon=b.mon) * 2 )
            + ( ( a.yr=b.yr  ) * 2 )
            + ( ( a.fnm=b.fnm) * 1 )
            + ( ( a.lnm=b.lnm) * 1 )
            ) >= 6;

In this case the equijoin predicate partitions the solution set into two, groups -- this is not much of a saving in terms of the numbers of records that need to be crossed to evaluate the Cartesian product. PROC SQL will:

  • load a single buffer age of observations from filea
  • load as many observations as can fit into available memory from fileb
  • consider all interactions of the rows loaded

If all the rows from filea could not fit into a single buffer page during the first step above, we will reload that page with more observations and repeat the process .

If you encounter a SQL Join that warns "NOTE: The execution of this query involves performing one or more Cartesian product joins that can not be optimized.", you may be able to improve its performance by increasing the buffersize= PROC SQL option.

A Note on the buffersize option

We have not documented this option for several reasons:

  • It does no validity checks on the value you supply.
  • We have not tested the impact on performance of raising the value across all operating systems and in conjunction with SAS Share servers.

In other words please run some tests on your data to see if it is a useful option to specify in your situation.

PROC SQL will use approximately 1M of memory per hash join with the default buffersize of 64000. If you increase it to buffersize=128000 we'll use approximately 1.6M, and 3.2M for buffersize=256000.

PROC SQL uses buffer pages for other processing too. Increasing the size of a buffer page using this option may mean that you need to increase the total memory available to your SAS Session (using the memsize SAS Invocation option on UNIX and PC Systems, for example).


[CORELLE] Charlotte Corelle, Dean MacLaughlin, Lois Drew and Mary Longacre. "Death Matching: An Algorithm and Comparison of its Implementation in PROC SQL vs. PROC SQL plus a DATA STEP" pp 237 SUGI 18 Proceedings.

[KENT] Paul Kent. "Fun with the SQL Procedure" pp 167 SUGI17 Proceedings or pp. 29 NESUG 93 Proceedings.

[LOREN] Judy Loren and Alan Dickson. "Processing Large SAS and DB2 Files: Close Encounters of the Colossal Kind' pp 1497 SUGI 19 Proceedings or pp 35 NESUG 93 Proceedings.

[SCOTT] Stephen Scott. "Why we replaced DB2 Software with SAS Software as a RDBMS for a 30-Gigabyte User Information System" pp 187 SUGI 18 Proceedings.


SAS, SAS/ACCESS and SAS/SHARE are a registered trademarks of SAS Institute Inc.

Your Turn

The author can be reached care of SAS Institute Inc., or at the Internet E-mail Address

Comments on this tutorial and PROC SQL in general are always welcome.