Re: [Drizzle-discuss] Flexviews (materialized views for MySQL) in detail - PART 3, view materialization walkthrough, multiple table views

[Brian Aker]

Hi!

I'm going to point out some alternative ways to make this happen with  
today's system.

On Jul 18, 2009, at 2:44 PM, Jay Pipes wrote:

1) Ability for plugins to add new tokens and grammar rules to the  
parser in order to add new syntax (for instance, for the  
MATERIALIZED and VIEW keywords).

Without additions to the parser, flexviews would have to be  
implemented as an awkward set of UDFs...

Or you could send messages to the system via variables. I've thought  
before that it might be handy/nice to have a way to send a particular  
message to a plugin, but even today you can use vars to send messages.

A bit of an aside...the Drizzle parser, though less than a third the  
size of MySQL's, is still a bit of a rat's nest of code.  To make it  
extensible, we need to add hooks that enable a plugin's own parser  
methods to be called when the kernel's parser hits a token it does  
not understand.  So, instead of bombing a YYABORT(), it needs to  
call plugins' registered parse routines, passing in the token stream.

Or do a parser fail through... if parser "A" has an issue with it,  
just see if another parser might handle it. This also allows for you  
to have different types of parsers (you don't want to require  
everything to be Bison).


2) Simple table-specific trigger plugin

Right now, there is only a very simple trigger mechanism which  
allows other plugins to be fed a Command message when *anything*  
changes on *any* table in Drizzle.  This is called the default  
replicator plugin and is set to be merged (hopefully today or  
tomorrow) into trunk.  You can see this plugin (and how ridiculously  
simple it actually is) in my replication branch:

The optimization to this is to register trigger callbacks on Table  
objects (or more likely on the Share). We just go through and fire  
them off, not caring if they are for replication, materialized views,  
etc.. To the kernel is should just be an "event" that we know we would  
process some callbacks for (and really all events longterm should be  
able to do this).

Cheers,
        -Brian


http://code.launchpad.net/~jaypipes/drizzle/replication

Look at

/plugin/default_replicator/default_replicator.cc|.h
/plugin/command_log/command_log.cc|.h

This plugin does exactly what it implies: it replicates all data  
modification and definition events which occur in the kernel to  
*any* registered "Applier" plugin.

The only example of an Applier plugin so far is the command_log  
plugin (also in the same branch above), which takes Command messages  
it receives from the default replicator plugin and  
"applies" (writes) them to a simple log file.

The Command Log plugin can be used as an example of how to work with  
Command messages, which contain all the information that a Flexview  
"trigger" would need in order to process the log of deltas you  
describe in your ML posts.

Cheers!

Jay

Justin Swanhart wrote:
Part 3, the last part, materialized views with joins
I am going to create two new tables for this test:
mysql> create table dim_uid (uid_id int auto_increment primary key ,
                                          url_hash char(32),
                                          site_code char(2)
           ) engine=innodb;
Query OK, 0 rows affected (0.03 sec)
mysql> create table fact(fact_id int auto_increment primary key,
                                    uid_id int not null,
                                    imp_count int,
                                    click_count int,
                                    cpc decimal(10,6)
            ) engine=innodb;
Query OK, 0 rows affected (0.08 sec)
*Materialized view logs are created on both tables.*
mysql> show tables where Tables_in_test like 'fact%' or  
Tables_in_test like
'dim_uid%';
+----------------+
| Tables_in_test |
+----------------+
| dim_uid        |
| dim_uid_mvlog  |
| fact           |
| fact_mvlog     |
+----------------+
4 rows in set (0.00 sec)
*Data initially in the tables:*
mysql> select * from dim_uid;
+--------+----------------------------------+-----------+
| uid_id | url_hash                         | site_code |
+--------+----------------------------------+-----------+
|      1 | 0bfe2e352a363bdeaeee3323d6465b1c | XA        |
|      2 | 07cd93d5d23a60e4e825769783b11b9d | XA        |
|      3 | cc8a8d7b824bd7f5a5eb16bddc35de62 | XB        |
|      4 | 050ef350ac164f18e26ed19ced9370dd | XC        |
+--------+----------------------------------+-----------+
4 rows in set (0.00 sec)
mysql> select * from fact;
+---------+--------+-----------+-------------+---------- 
+------------+
| fact_id | uid_id | imp_count | click_count | cpc      |  
the_date   |
+---------+--------+-----------+-------------+---------- 
+------------+
|       7 |      1 |        10 |           2 | 0.000001 |  
2008-01-02 |
|       8 |      1 |        25 |          15 | 0.000200 |  
2008-01-02 |
|       9 |      2 |        10 |           5 | 0.000500 |  
2008-01-01 |
|      10 |      3 |     75682 |        1750 | 4.242321 |  
2008-01-01 |
|      11 |      3 |    114232 |       15323 | 4.123212 |  
2008-02-01 |
|      12 |      4 |       512 |          64 | 0.000210 |  
2008-01-01 |
|      13 |      4 |      1024 |         256 | 0.000330 |  
2008-01-02 |
+---------+--------+-----------+-------------+---------- 
+------------+
7 rows in set (0.00 sec)
*I am going to materialize a view for the following SQL:*
SELECT f.the_date click_date,
      d.site_code site_code,
      count(*) site_count,
      sum(f.imp_count) total_imp,
      sum(f.click_count) total_clicks,
      sum(f.click_count * f.cpc) click_revenue
 FROM dim_uid d
 JOIN fact f on d.uid_id = f.uid_id
WHERE f.cpc > 0.0001
GROUP BY f.the_date, site_code
ORDER BY click_revenue desc;
+------------+-----------+------------+-----------+-------------- 
+---------------+-----+
| click_date | site_code | site_count | total_imp | total_clicks |
click_revenue | CNT |
+------------+-----------+------------+-----------+-------------- 
+---------------+-----+
| 2008-02-01 | XB        |          1 |    114232 |        15323 |
63179.977476 |   1 |
| 2008-01-01 | XB        |          1 |     75682 |         1750 |
7424.061750 |   1 |
| 2008-01-02 | XC        |          1 |      1024 |          256 |
0.084480 |   1 |
| 2008-01-01 | XC        |          1 |       512 |           64 |
0.013440 |   1 |
| 2008-01-02 | XA        |          1 |        25 |           15 |
0.003000 |   1 |
| 2008-01-01 | XA        |          1 |        10 |            5 |
0.002500 |   1 |
+------------+-----------+------------+-----------+-------------- 
+---------------+-----+
6 rows in set (0.01 sec)
*
API calls to build the view:*
mysql> call flexviews.create('test','example2','INCREMENTAL');
Query OK, 1 row affected (0.03 sec)
mysql> set @mvid=last_insert_id();
Query OK, 0 rows affected (0.00 sec)
mysql> call flexviews.add_table(@mvid, 'test','dim_uid', 'd', NULL);
Query OK, 1 row affected (0.01 sec)
mysql> call flexviews.add_table(@mvid, 'test','fact', 'f', ' ON  
f.uid_id =
d.uid_id ');
Query OK, 1 row affected (0.01 sec)
mysql> call flexviews.add_expr(@mvid, 'GROUP', 'f.the_date',  
'click_date');
Query OK, 0 rows affected (0.03 sec)
mysql> call flexviews.add_expr(@mvid, 'GROUP', 'd.site_code',  
'site_code');
Query OK, 0 rows affected (0.00 sec)
mysql> call flexviews.add_expr(@mvid, 'COUNT', '*', 'site_count');
Query OK, 0 rows affected (0.01 sec)
mysql> call flexviews.add_expr(@mvid, 'SUM', 'f.imp_count',  
'total_imp');
Query OK, 0 rows affected (0.00 sec)
mysql> call flexviews.add_expr(@mvid, 'SUM', 'f.click_count',
'total_clicks');
Query OK, 0 rows affected (0.00 sec)
mysql> call flexviews.add_expr(@mvid, 'SUM', 'f.click_count * f.cpc',
'click_revenue');
Query OK, 0 rows affected (0.01 sec)
mysql> call flexviews.add_expr(@mvid, 'WHERE', 'f.cpc > .0001',
'fact_cpc_where1');
Query OK, 0 rows affected (0.01 sec)
mysql> call flexviews.enable(@mvid);
Query OK, 0 rows affected (0.10 sec)
mysql> select * from example2 order by click_revenue desc;
+------------+-----------+------------+-----------+-------------- 
+---------------+-----+
| click_date | site_code | site_count | total_imp | total_clicks |
click_revenue | CNT |
+------------+-----------+------------+-----------+-------------- 
+---------------+-----+
| 2008-02-01 | XB        |          1 |    114232 |        15323 |
63179.977476 |   1 |
| 2008-01-01 | XB        |          1 |     75682 |         1750 |
7424.061750 |   1 |
| 2008-01-02 | XC        |          1 |      1024 |          256 |
0.084480 |   1 |
| 2008-01-01 | XC        |          1 |       512 |           64 |
0.013440 |   1 |
| 2008-01-02 | XA        |          1 |        25 |           15 |
0.003000 |   1 |
| 2008-01-01 | XA        |          1 |        10 |            5 |
0.002500 |   1 |
+------------+-----------+------------+-----------+-------------- 
+---------------+-----+
6 rows in set (0.00 sec)
*-- Fix the date transposition error, and add rows to tables
*mysql> update fact
         set the_date = '2008-01-02'
       where the_date = '2008-02-01';
Query OK, 1 row affected (0.04 sec)
Rows matched: 1  Changed: 1  Warnings: 0
--Add a row to the dimension table.
mysql> insert into dim_uid (uid_id, url_hash, site_code) values (5,
md5(now()), 'AA');
Query OK, 1 row affected (0.00 sec)
--Add rows to the fact table
mysql> insert into fact values (null, 5, 100, 75, .005,  
'2008-01-01');
Query OK, 1 row affected (0.00 sec)
mysql> insert into fact values (null, 5, 250, 100, .00752,  
'2008-01-02');
Query OK, 1 row affected (0.01 sec
*What is in the materialized view logs?
*mysql> select * from dim_uid_mvlog;
+----------+--------+--------+---------------------------------- 
+-----------+
| dml_type | uow_id | uid_id | url_hash                         |  
site_code
|
+----------+--------+--------+---------------------------------- 
+-----------+
|        1 |     82 |      5 | 0c9a2a45a83d776b5bb0887e9a3cee7e | AA
|
+----------+--------+--------+---------------------------------- 
+-----------+
1 row in set (0.00 sec)
mysql> select * from fact_mvlog;
+----------+--------+---------+--------+-----------+------------- 
+----------+------------+
| dml_type | uow_id | fact_id | uid_id | imp_count | click_count |  
cpc
| the_date   |
+----------+--------+---------+--------+-----------+------------- 
+----------+------------+
|       -1 |     81 |      11 |      3 |    114232 |       15323 |  
4.123212
| 2008-02-01 |
|        1 |     81 |      11 |      3 |    114232 |       15323 |  
4.123212
| 2008-01-02 |
|        1 |     83 |      19 |      5 |       100 |          75 |  
0.005000
| 2008-01-01 |
|        1 |     84 |      20 |      5 |       250 |         100 |  
0.007520
| 2008-01-02 |
+----------+--------+---------+--------+-----------+------------- 
+----------+------------+
4 rows in set (0.01 sec)
*
Here are the compensation queries for calculating the delta records:*
mysql> call flexviews.refresh(2,'COMPUTE');
Query OK, 1 row affected, 2 warnings (0.61 sec)
mysql> select * from flexviews.refresh_log where message like  
'%INSERT%'\G
*************************** 1. row ***************************
tstamp: 2009-07-17 02:43:46
  usec: NULL
message: Query:
INSERT INTO test.example2_delta
SELECT (d.dml_type * 1) as dml_type,d.uow_id as uow_id,
      (f.the_date) as `click_date`,
      (d.site_code) as `site_code`,
      SUM((d.dml_type * 1)) as `site_count`,
      SUM((d.dml_type * 1) * f.imp_count) as `total_imp`,
      SUM((d.dml_type * 1) * f.click_count) as `total_clicks`,
      SUM((d.dml_type * 1) * f.click_count * f.cpc) as  
`click_revenue`,
      SUM((d.dml_type * 1)) as `CNT`
FROM  test.dim_uid_mvlog as d
JOIN  test.fact as f  ON f.uid_id = d.uid_id
WHERE f.cpc > .0001
 AND d.uow_id >58
 AND d.uow_id <=67
 AND (d.dml_type * 1 = 1)
GROUP BY (f.the_date), (d.site_code)
UNION ALL
SELECT
     (d.dml_type * 1) as dml_type,d.uow_id as uow_id,
     (f.the_date) as `click_date`,
     (d.site_code) as `site_code`,
     SUM((d.dml_type * 1)) as `site_count`,
     SUM((d.dml_type * 1) * f.imp_count) as `total_imp`,
     SUM((d.dml_type * 1) * f.click_count) as `total_clicks`,
     SUM((d.dml_type * 1) * f.click_count * f.cpc) as  
`click_revenue`,
     SUM((d.dml_type * 1)) as `CNT`
FROM test.dim_uid_mvlog as d
JOIN test.fact as f  ON f.uid_id = d.uid_id
WHERE f.cpc > .0001
 AND d.uow_id >58
 AND d.uow_id <=67
 AND (d.dml_type * 1 = -1)
GROUP BY (f.the_date), (d.site_code)
*************************** 2. row ***************************
tstamp: 2009-07-17 02:43:46
  usec: NULL
message: Query:
INSERT INTO test.example2_delta
SELECT (f.dml_type * -1) as dml_type,
      LEAST(d.uow_id,f.uow_id) as uow_id,
      (f.the_date) as `click_date`,
      (d.site_code) as `site_code`,
      SUM((f.dml_type * -1)) as `site_count`,
      SUM((f.dml_type * -1) * f.imp_count) as `total_imp`,
      SUM((f.dml_type * -1) * f.click_count) as `total_clicks`,
      SUM((f.dml_type * -1) * f.click_count * f.cpc) as  
`click_revenue`,
      SUM((f.dml_type * -1)) as `CNT`
 FROM  test.dim_uid_mvlog as d
 JOIN  test.fact_mvlog as f  ON f.uid_id = d.uid_id
WHERE f.cpc > .0001
  AND d.uow_id >58
  AND d.uow_id <=67
  AND f.uow_id >67
  AND f.uow_id <=68
  AND (f.dml_type * -1 = 1)
GROUP BY (f.the_date), (d.site_code)
UNION ALL
SELECT (f.dml_type * -1) as dml_type,
      LEAST(d.uow_id,f.uow_id) as uow_id,
      (f.the_date) as `click_date`,
      (d.site_code) as `site_code`,
      SUM((f.dml_type * -1)) as `site_count`,
      SUM((f.dml_type * -1) * f.imp_count) as `total_imp`,
      SUM((f.dml_type * -1) * f.click_count) as `total_clicks`,
      SUM((f.dml_type * -1) * f.click_count * f.cpc) as  
`click_revenue`,
      SUM((f.dml_type * -1)) as `CNT`
 FROM test.dim_uid_mvlog as d
 JOIN  test.fact_mvlog as f  ON f.uid_id = d.uid_id
WHERE f.cpc > .0001
  AND d.uow_id >58
  AND d.uow_id <=67
  AND f.uow_id >67
  AND f.uow_id <=68
  AND (f.dml_type * -1 = -1)
GROUP BY (f.the_date), (d.site_code)
*************************** 3. row ***************************
tstamp: 2009-07-17 02:43:46
  usec: NULL
message: Query:
INSERT INTO test.example2_delta
SELECT (f.dml_type * 1) as dml_type,
      f.uow_id as uow_id,
      (f.the_date) as `click_date`,
      (d.site_code) as `site_code`,
      SUM((f.dml_type * 1)) as `site_count`,
      SUM((f.dml_type * 1) * f.imp_count) as `total_imp`,
      SUM((f.dml_type * 1) * f.click_count) as `total_clicks`,
      SUM((f.dml_type * 1) * f.click_count * f.cpc) as  
`click_revenue`,
      SUM((f.dml_type * 1)) as `CNT`
 FROM test.dim_uid as d   JOIN  test.fact_mvlog as f  ON f.uid_id =
d.uid_id
WHERE f.cpc > .0001
  AND f.uow_id >58
  AND f.uow_id <=67
  AND (f.dml_type * 1 = 1)
GROUP BY (f.the_date), (d.site_code)
UNION ALL
SELECT (f.dml_type * 1) as dml_type,
      f.uow_id as uow_id, (f.the_date) as `click_date`,
      (d.site_code) as `site_code`,
      SUM((f.dml_type * 1)) as `site_count`,
      SUM((f.dml_type * 1) * f.imp_count) as `total_imp`,
      SUM((f.dml_type * 1) * f.click_count) as `total_clicks`,
      SUM((f.dml_type * 1) * f.click_count * f.cpc) as  
`click_revenue`,
      SUM((f.dml_type * 1)) as `CNT`
FROM  test.dim_uid as d
JOIN  test.fact_mvlog as f  ON f.uid_id = d.uid_id
WHERE f.cpc > .0001
 AND f.uow_id >58
 AND f.uow_id <=67
 AND (f.dml_type * 1 = -1)
GROUP BY (f.the_date), (d.site_code)
*************************** 4. row ***************************
tstamp: 2009-07-17 02:43:46
  usec: NULL
message: Query:
INSERT INTO test.example2_delta
SELECT (d.dml_type * -1) as dml_type,
      LEAST(d.uow_id,f.uow_id) as uow_id,
      (f.the_date) as `click_date`,
      (d.site_code) as `site_code`,
      SUM((d.dml_type * -1)) as `site_count`,
      SUM((d.dml_type * -1) * f.imp_count) as `total_imp`,
      SUM((d.dml_type * -1) * f.click_count) as `total_clicks`,
      SUM((d.dml_type * -1) * f.click_count * f.cpc) as  
`click_revenue`,
      SUM((d.dml_type * -1)) as `CNT`
FROM  test.dim_uid_mvlog as d
JOIN  test.fact_mvlog as f  ON f.uid_id = d.uid_id
WHERE f.cpc > .0001
 AND d.uow_id >58
 AND d.uow_id <=70
 AND f.uow_id >58
 AND f.uow_id <=67
 AND (d.dml_type * -1 = 1)
GROUP BY (f.the_date), (d.site_code)
UNION ALL
SELECT (d.dml_type * -1) as dml_type,
      LEAST(d.uow_id,f.uow_id) as uow_id,
      (f.the_date) as `click_date`,
      (d.site_code) as `site_code`,
      SUM((d.dml_type * -1)) as `site_count`,
      SUM((d.dml_type * -1) * f.imp_count) as `total_imp`,
      SUM((d.dml_type * -1) * f.click_count) as `total_clicks`,
      SUM((d.dml_type * -1) * f.click_count * f.cpc) as  
`click_revenue`,
      SUM((d.dml_type * -1)) as `CNT`
 FROM test.dim_uid_mvlog as d
 JOIN  test.fact_mvlog as f  ON f.uid_id = d.uid_id
WHERE f.cpc > .0001
  AND d.uow_id >58
  AND d.uow_id <=70
  AND f.uow_id >58
  AND f.uow_id <=67
  AND (d.dml_type * -1 = -1)
GROUP BY (f.the_date), (d.site_code)
4 rows in set (0.00 sec)
*Which produce the following rows in the materialized view delta log:
**
mysql> select * from example2_delta;
*
+----------+--------+------------+-----------+------------ 
+-----------+--------------+---------------+-----+
| dml_type | uow_id | click_date | site_code | site_count |  
total_imp |
total_clicks | click_revenue | CNT |
+----------+--------+------------+-----------+------------ 
+-----------+--------------+---------------+-----+
|        1 |     82 | 2008-01-01 | AA        |          1 |       100
|           75 |      0.375000 |   1 |
|        1 |     82 | 2008-01-02 | AA        |          1 |       250
|          100 |      0.752000 |   1 |
|        1 |     83 | 2008-01-01 | AA        |          1 |       100
|           75 |      0.375000 |   1 |
|        1 |     84 | 2008-01-02 | AA        |          1 |       250
|          100 |      0.752000 |   1 |
|        1 |     81 | 2008-01-02 | XB        |          1 |    114232
|        15323 |  63179.977476 |   1 |
|       -1 |     81 | 2008-02-01 | XB        |         -1 |   -114232
|       -15323 | -63179.977476 |  -1 |
|       -1 |     82 | 2008-01-01 | AA        |         -1 |      -100
|          -75 |     -0.375000 |  -1 |
|       -1 |     82 | 2008-01-02 | AA        |         -1 |      -250
|         -100 |     -0.752000 |  -1 |
+----------+--------+------------+-----------+------------ 
+-----------+--------------+---------------+-----+
8 rows in set (0.00 sec)*
Then the delta log is applied to the view:
*mysql> call flexviews.refresh(2, 'APPLY')
   -> ;
Query OK, 1 row affected, 1 warning (0.08 sec)
*
which runs the following SQL:
*
DELETE test.example2_delta.*,
      test.example2.*
 FROM test.example2_delta
 JOIN test.example2
USING(click_date, site_code)
WHERE example2.CNT + test.example2_delta.CNT=0
INSERT INTO test.example2
SELECT * FROM
 (SELECT click_date,
         site_code,
         site_count,
         total_imp,
         total_clicks,
         click_revenue,
         CNT
    FROM test.example2_delta
   WHERE uow_id > 58
     AND uow_id <= 71) x_select_
ON DUPLICATE KEY UPDATE
`click_revenue` = example2.`click_revenue` +  
x_select_.`click_revenue`
,`CNT` = example2.`CNT` + x_select_.`CNT`
,`site_count` = example2.`site_count` + x_select_.`site_count`
,`total_clicks` = example2.`total_clicks` + x_select_.`total_clicks`
,`total_imp` = example2.`total_imp` + x_select_.`total_imp`
*After refresh:
real query:
*
SELECT f.the_date click_date,
      d.site_code site_code,
      count(*) site_count,
      sum(f.imp_count) total_imp,
      sum(f.click_count) total_clicks,
      sum(f.click_count * f.cpc) click_revenue
 FROM dim_uid d
 JOIN fact f on d.uid_id = f.uid_id
WHERE f.cpc > 0.0001
GROUP BY f.the_date, site_code
ORDER BY click_revenue desc;
+------------+-----------+------------+-----------+-------------- 
+---------------+
| click_date | site_code | site_count | total_imp | total_clicks |
click_revenue |
+------------+-----------+------------+-----------+-------------- 
+---------------+
| 2008-01-02 | XB        |          1 |    114232 |        15323 |
63179.977476 |
| 2008-01-01 | XB        |          1 |     75682 |         1750 |
7424.061750 |
| 2008-01-02 | AA        |          1 |       250 |          100 |
0.752000 |
| 2008-01-01 | AA        |          1 |       100 |           75 |
0.375000 |
| 2008-01-02 | XC        |          1 |      1024 |          256 |
0.084480 |
| 2008-01-01 | XC        |          1 |       512 |           64 |
0.013440 |
| 2008-01-02 | XA        |          1 |        25 |           15 |
0.003000 |
| 2008-01-01 | XA        |          1 |        10 |            5 |
0.002500 |
+------------+-----------+------------+-----------+-------------- 
+---------------+
8 rows in set (0.00 sec)
*materialized view result:
*mysql> select * from example2 order by click_revenue desc;
+------------+-----------+------------+-----------+-------------- 
+---------------+-----+
| click_date | site_code | site_count | total_imp | total_clicks |
click_revenue | CNT |
+------------+-----------+------------+-----------+-------------- 
+---------------+-----+
| 2008-01-02 | XB        |          1 |    114232 |        15323 |
63179.977476 |   1 |
| 2008-01-01 | XB        |          1 |     75682 |         1750 |
7424.061750 |   1 |
| 2008-01-02 | AA        |          1 |       250 |          100 |
0.752000 |   1 |
| 2008-01-01 | AA        |          1 |       100 |           75 |
0.375000 |   1 |
| 2008-01-02 | XC        |          1 |      1024 |          256 |
0.084480 |   1 |
| 2008-01-01 | XC        |          1 |       512 |           64 |
0.013440 |   1 |
| 2008-01-02 | XA        |          1 |        25 |           15 |
0.003000 |   1 |
| 2008-01-01 | XA        |          1 |        10 |            5 |
0.002500 |   1 |
+------------+-----------+------------+-----------+-------------- 
+---------------+-----+
8 rows in set (0.00 sec)
Tada.  View materialization with joins, and you can change fact and
dimension tables.  That was simple wasn't it? :D
------------------------------------------------------------------------
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