Remember the Therac!

Personal opinion warning!!!

Every time I read something like this, I immediately think of the famous Therac-25.

But let's start from the bottom...

"If you are deploying technology in your healthcare organization, ask yourself first if your people are ready. If they are not, you are setting the stage for failure."

This is so generic, Mr. Crounse should win a a prize. I'm sure, there is a prize somewhere for phrases like that.

How about:

"If you are deploying technology in your organization, ask yourself first if your people are ready. If they are not, you are setting the stage for failure."

Almost universally true but the gain of knowledge is essentially zilch.

But what really irks me is stuff like this (on the training of physicians):

"We spend our young adulthood immersed in the scholarly pursuit of a medical degree. We take four or more years in specialty training. [...] It’s all about following a certain process, a definitive kind of workflow"

Call me oldschool, but I expect a physician to be well trained and following certain, proven processes, when I lay my fate into her hands!

IT has already a major role in healthcare. Without advanced signal processing and medical imaging, MRI would be useless. Genome data simply cannot be analyzed without the help of computers. The human body can be simulated to some extent to reduce the need of testing new drugs on animals and human beings alike. Particle therapy needs absolute precision controls, provided by computers. And so on and on...

And what do I read?

"They are embracing cloud technologies to streamline IT resources and focus more of those limited resources on that which healthcare systems are all about—providing care to patients and increasingly, focusing on ways to improve population health and disease prevention."

OK, people are expensive, care is expensive so let's use IT to squeeze more out of the staff we have.

There is no single sentence about medical progress in this article. It's from top to bottom about cost-cutting.

I have to give credit to the overall assumption, that something good could be gained  from the integration of health-care data, though. A lot of knowledge about how diseases work and what weapon works best to fight them with could be gained from a holistic view on all the data available.

Yet, in many countries, there is a strict regulation on medical data. And here I have to give credit again - it's all about people. People building systems, people using systems and people having trust in systems.

In 2010 and 2011, physicians manipulated medical data of their patients waiting for a liver transplant in order to push them higher up the waiting list. This so called Organspendeskandal led to the lowest trust and henceforth lowest number of potential organ donors in Germany since 2002.

I am a registered bone marrow donor at DKMS. Yet, we are only a scant 25 million worldwide.  Already way to few. Guess what would happen, if there was a major security breach in this database? Or in any other large health data database?

"Information is no candy bar" they say in Bombardiers, (hilarious and highly recommended) If you eat it the candy bar its gone, but information can be stolen without notice.

Let's assume somebody would steal all the paper patient files from a hospital, or the insurance company. This could be noticed, because a) they are gone and b) somebody might ask what the four trucks full of paper are all about. Electronic files can be stolen without notice on an USB stick.

We, the people, still cannot build reliable and secure software systems at a large scale. SQL Injection is still #1 at OWASP. As it was 2010. 2007 it was on #2. Unbelievable.

Even in highly specialized and controlled areas, we fail, fail, fail.

So, where does this strict regulation on medical data come from again?

Whenever you work on hard or software for healthcare applications - please remember the Therac.

So much useful knowledge could be gained, but so much trust can be lost.

Personal opinion warning!!!

A working RAMDisk for PostgreSQL on Windows

Aside from the debate if this is useful or dangerous, I tried several free RAMDisks for Windows, Gavotte RamDisk, AMD Radeon RAMDisk and SoftPerfect RAM Disk - and none of them works well with PostgreSQL.

Either PostgreSQL refuses to use them at all for datafiles or it seems to work, but at some point in time the server tells you, that there is something wrong with the datafiles on the ramdrive and data corruption bites.

Apparently most ramdrives on Windows provide drive emulations only good enough to support simple file storage.

ImDisk is the first I tried, that seems to work well with PostgreSQL.

It's free, has a GUI and a command line, and the driver is properly signed for x64 Windows.

There are people := {a,b} for a ≠ b IN (SELECT people FROM world): Those who understand SQL, and those who have to wait.

Couldn't resist. ;->

I just pushed a view on an Oracle Database from 464 seconds down to 3 seconds (~ 157x speedup or 0.0065% of the original execution time) by removing a few pointless ORDER BYs and replacing one expensive JOIN with subqueries.

ToroDB Adventures: Adding unimplemented commands

From

> db.createCollection("test")
{ "ok" : 0, "errmsg" : "Unimplemented command: create", "code" : 1000002 }

to

> db.createCollection("test")
{ "ok" : 1 }

in five simple steps:

1.) Find out how the command is called internally. toroDB tells you that in the message:

"Unimplemented command: create"

so it is "create".

2.) Look up the "create" command. Start in QueryCommandProcessor.java with the QueryCommandGroup enum which contains all enums of all known commands. Since it is an administration command, you'll find it in AdministrationQueryCommand.java.

3.)  "create" does nothing at the moment, so add some code.
I just copied it from "createIndexes" so

create,

becomes now

create {
        @Override
        public void doCall(RequestBaseMessage queryMessage, BSONDocument query, ProcessorCaller caller) throws Exception {
            caller.create(query);
        }
    },

4.) Now, ProcessorCaller needs to know about the new command. It's  enclosed in QueryCommandProcessor.java, so by adding

public void create(@Nonnull BSONDocument document) throws Exception {
queryCommandProcessor.create(document, messageReplier);
}

to ProcessorCaller AND the prototype

public void create(@Nonnull BSONDocument document, @Nonnull MessageReplier messageReplier) throws Exception;

to the enclosing QueryCommandProcessor class, make it known.

5.) Implement the command in ToroQueryCommandProcessor.java, which is the actual implementation of QueryCommandProcessor:

@Override
    public void create(BSONDocument document, MessageReplier messageReplier) {
      Map keyValues = new HashMap();
      keyValues.put("ok", MongoWP.OK);
       
      String collectionName = (String) document.getValue("create");
      Boolean capped = (Boolean) document.getValue("capped");
      Boolean autoIndexId = (Boolean) document.getValue("autoIndexId");
      Boolean usePowerOf2Sizes = (Boolean) document.getValue("usePowerOf2Sizes");
      Double size = (Double) document.getValue("size");
      Double max = (Double) document.getValue("max");
       
      BSONDocument reply = new MongoBSONDocument(keyValues);
      messageReplier.replyMessageNoCursor(reply);
    }

And that's pretty much it. As of now it just reads all allowed values from the command and acknowledges OK. But now everything is set to make it a "real" command if needed.

The case for map/reduce with relational databases

I was at pgconf.eu and held a lightning talk about map/reduce with PostgreSQL. Upfront, I was asked "Why do you want to do that anyway?" and my initial response was like, "Because I can.". :-)

But that got me thinking about the real case behind the idea. What is the heart of map/reduce? Citing from the original paper:

"MapReduce is a programming model and an associated implementation for processing and generating large data sets." Note the word "sets"?

"Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key.", or, expressed in a more formal way

map (k1,v1)                -> list(k2,v2)
reduce (k2,list(v2))    -> list(v2)

Please note, that this is a bit more precise than the initial definition. The paper explains:

"I.e., the input keys and values are drawn from a different domain than the output keys and values. Furthermore, the intermediate keys and values are from the same domain as the output keys and values."

This is important, because the authors are introducing a domain transformation of the input data here. That is, in my opinion, already the heart of map/reduce.

Going back to the initial definition, this is basically what all RDBMS already do when processing parallel queries, be it by builtin ability or bolted on like with PL/Proxy + PostgreSQL: In the first step the input set is broken down to partitions, then the query runs on that partitions in parallel and produces intermediate result sets and finally that intermediate result sets are aggregated to the final result set. But the formal definition above adds a little twist, the domain transformation.

To clarify this, I'll use the canonical example, counting words in a text. The map function converts semi structured data, a text with lines of arbitrary length, into a well structured set of key (a word) and value (its count) tuples. This is the difference and the key to the power of map/redcue.
The ability to handle semi structured data which the relational model usually does not handle very well. (And I won't say unstructured data. Truly unstructured data is statistical noise.)

But modern RDBMS, especially PostgreSQL, often already have functions to transform semi structured data into relations and/or allow for user defined functions to extend their capabilities and that allows for running a map/reduce type job inside a RDBMS. Still, why would somebody want to do this?

1.) Integration
An awful lot of data is stored in relational models and will stay there. Simultaneously, especially for analytical workloads which become more and more important, the need for integrating relational and semi-structured data grows. Why handle them in different systems when one will do?
This decision of course heavily depends on the real world requirements. But rest assured that the datacenter guys who have to run the show will like to operate one database better than 2..n.

2.) Sets
Remember the word "sets" from the initial definition? And now the definition of a "relation" in a RDBMS:

"R is a relation on these n domains if it is a set of elements of the form (d1, d2, ..., dn) where dj ∈ Dj for each j=1,2,...,n." (E. F. Codd (Oct 1972). "Further normalization of the database relational model". "Data Base Systems". Courant Institute: Prentice-Hall. ISBN 013196741X.)

If a relation is a set of tuples with values from some domain D and map/reduce does domain transformation on key/value pairs (aka. tuples) what does that call for? Right, a very efficient set processor. Since relational DBMS are very efficient set processors by nature, they allow for
writing compact map/reduce functions that are also less error prone due to the declarative nature of SQL.

To clarify what I mean take a look at the following map and reduce functions for wordcount written for MongoDB in JavaScript from here:

var map = function() { 
    var summary = this.summary;
    if (summary) {
        // quick lowercase to normalize per your requirements
        summary = summary.toLowerCase().split(" ");
        for (var i = summary.length - 1; i >= 0; i--) {
            // might want to remove punctuation, etc. here
            if (summary[i])  {      // make sure there's something
               emit(summary[i], 1); // store a 1 for each word
            }
        }
    }
};

var reduce = function( key, values ) {   
    var count = 0;   
    values.forEach(function(v) {           
        count +=v;   
    });
    return count;
}

db.so.mapReduce(map, reduce, {out: "word_count"})

PostgreSQL:

For the code, see my previous post.

Well, while it seems to require more code than MongoDB, there is a subtle difference. The most PostgreSQL code is standard boilerplate to write a set returning function and to make PL/Proxy work. Once you got that right, you usually never have to look back. The actual work is done in two lines of SQL:

SELECT TRIM(both from word),count(1) FROM (SELECT regexp_split_to_table(line, E'\\W+') as word FROM kjb) w GROUP BY word

and 

SELECT word,sum(count) FROM map_kjb() AS (word text, count bigint) WHERE word != '' GROUP BY word

IMHO, the ability to express this with extensively tried and tested functions instead of having to implement them yourself combined with strong typing is worth so much, that one should give it a try before looking somewhere else. Granted, raw performance may become so paramount that an alternative technology might be called for, but if you already use PostgreSQL now there is another reason to like and not to leave it.

And this is it, the long answer I didn't already had ready at the conference.

P.S. It also allows to move computation instead of data around.

pgconf.eu 2014

On the worker nodes:

 CREATE TABLE kjb
(
  line text
);


CREATE OR REPLACE FUNCTION map_kjb()
  RETURNS SETOF record AS
$BODY$
DECLARE r record;
BEGIN
FOR r IN (SELECT TRIM(both from word),count(1) FROM (SELECT regexp_split_to_table(line, E'\\W+') as word FROM kjb) w GROUP BY word) LOOP
RETURN NEXT r;
END LOOP;
RETURN;
END;
$BODY$
  LANGUAGE plpgsql VOLATILE
  COST 1000
  ROWS 1000;

On the head node:

CREATE TYPE t_mr_wc AS (word text, count bigint);

CREATE OR REPLACE FUNCTION map_kjb()
  RETURNS SETOF record AS
$BODY$
CLUSTER 'head';
RUN ON ALL;
$BODY$
  LANGUAGE plproxy VOLATILE
  COST 1000
  ROWS 1000;

CREATE OR REPLACE FUNCTION reduce_kjb()
  RETURNS SETOF t_mr_wc AS
$BODY$ DECLARE result public.t_mr_wc; BEGIN
FOR result IN select word,sum(count) from map_kjb() AS (word text,count bigint) where word != '' group by word LOOP RETURN NEXT result; END LOOP; END; $BODY$
  LANGUAGE plpgsql VOLATILE
  COST 100
  ROWS 100;

1. Load data into tables.

2. Get word count:

SELECT * FROM reduce_kjb();

3. Get top 21 word counts: 

SELECT * FROM reduce_kjb() ORDER BY 2 DESC LIMIT 21; 

Using Code::Blocks with the Intel C/C++ compiler on Windows in five minutes

Code::Blocks apparently does not support the latest Intel C/C++ compiler out of the box. Trying to set up the necessary include paths for both the Visual Studio compiler and the Intel compiler leads to madness.

But there is an easier way:

Make two Intel compiler entries in Code::Blocks for the 32 and 64 bit version with the correct base directory, e.g. C:\Program Files (x86)\Intel\Composer XE\bin.
The 32 bit toolchain executables are prefixed ia32\, the 64 bit ones intel64\.

When you have installed the Intel compiler, there is an 'Command Prompt with Intel Compiler' entry in the start menu for 32 and 64 bit targets each. This opens a command prompt with all necessary paths and environment variables set correctly.

Just start codeblocks.exe from this command prompt and it inherits the environment. Then you can select the 32 or 64 bit Intel compiler option in the 'Project->Build options' and it works without further ado.

The only thing you cannot do is switching between 32 and 64 bit targets on the fly, you have to start Code::Blocks with the correct environment first.

NoSQL Matters Conference 2014, Cologne

I made it on the list of presenters at the NoSQL Matters Conference 2014 in Cologne:

Single Point of Entry: Integrating relational and semi-structured data with PostgreSQL

Eclipse compiler produces faster FP code?

While testing this very simple FP code in Java:

package the.plateisbad;

public class Simple {

  public static void main(String[] args) {
    final long arg_flops;
    double i,x = 0.0d, y = 0.0d;
    final long start, end;

    if (args.length != 2) {
      System.out.println("Usage: the.plateisbad.Simple ");
      return;
    }

    arg_flops = Long.parseLong(args[0]);
    y = Long.parseLong(args[1]);

    System.out.println("Thinking really hard for " + arg_flops + " flops...");

    start = System.currentTimeMillis();

    for (i = 0; i < arg_flops; i++) {
      x = i * y;
    }

    end = System.currentTimeMillis();

    System.out.println("We calculated: " + x + " in " +(end-start)+ " ms");
  }
}


I've stumbled over the fact, that it runs considerably faster when compiled with the Eclipse ECJ compiler compared to a standard javac.

With ECJ, executed with JDK 1.7:

java -server the.plateisbad.Simple 1000000000 3

Thinking really hard for 1000000000 flops...
We calculated: 2.999999997E9 in 1964 ms

With javac, executed with JDK 1.7:

java -server the.plateisbad.Simple 1000000000 3

Thinking really hard for 1000000000 flops...
We calculated: 2.999999997E9 in 3514 ms

With the new JDK 1.8, there is no noticeable difference between javac and ECJ:

java -server the.plateisbad.Simple 1000000000 3

Thinking really hard for 1000000000 flops...
We calculated: 2.999999997E9 in 3727 ms

but it is always the slowest of the three. The Bytecode tells me that ECJ builds a tail controlled loop which loops while i is < arg_flops:

  64: invokestatic  #52                 // Method java/lang/System.currentTimeMillis:()J
      67: lstore        9
      69: dconst_0     
      70: dstore_3     
      71: goto          84
      74: dload_3      
      75: dload         7
      77: dmul         
      78: dstore        5
      80: dload_3      
      81: dconst_1     
      82: dadd         
      83: dstore_3     
      84: dload_3      
      85: lload_1      
      86: l2d          
      87: dcmpg        
      88: iflt          74
      91: invokestatic  #52                 // Method java/lang/System.currentTimeMillis:()J

while javac builds a head controlled loop that exits if i >= arg_flops:

      67: invokestatic  #13                 // Method java/lang/System.currentTimeMillis:()J
      70: lstore        9
      72: dconst_0     
      73: dstore_3     
      74: dload_3      
      75: lload_1      
      76: l2d          
      77: dcmpg        
      78: ifge          94
      81: dload_3      
      82: dload         7
      84: dmul         
      85: dstore        5
      87: dload_3      
      88: dconst_1     
      89: dadd         
      90: dstore_3     
      91: goto          74
      94: invokestatic  #13                 // Method java/lang/System.currentTimeMillis:()J

And ECJ uses StringBuffer while javac uses StringBuilder for the String operations, but since these are not in the loop, that should not make any difference.

Does somebody know what is going on here?

UPDATE: This seems to be an anomaly. SciMark 2.0 shows now significant differences between ECJ and javac and jdk1.7 and jdk1.8 - with 1.8 being slightly faster.

It's always good to have more choice

http://www.moldb.net/helium.html

A suggestion to all architects of high-security buildings ;->

Please, don't put the restrooms outside the security gates!

Arbitrary parallel (well, almost) ad-hoc queries with PostgreSQL

Contemporary PostgreSQL lacks the ability to run single queries on multiple cores, nodes etc., i.e. it lacks automatic horizontal scaling. While this seems to be under development, what can be done today?

PL/Proxy allows database partitioning and RUN ON ALL executes the function on all nodes simultaneously. PL/Proxy is limited to the partitioned execution of functions and has good reasons for this design. But PostgreSQL can execute dynamic SQL within functions, so let's see how far we can get.

Worker function (on all worker nodes):

CREATE OR REPLACE FUNCTION parallel_query(statement text)
  RETURNS SETOF record AS
$BODY$
DECLARE r record;
BEGIN
IF lower($1) LIKE 'select%' THEN
FOR r IN EXECUTE $1 LOOP
RETURN NEXT r;
END LOOP;
ELSE
RAISE EXCEPTION 'Only queries allowed';
END IF;
END
$BODY$
  LANGUAGE plpgsql VOLATILE;

Proxy function (on all head nodes):

CREATE OR REPLACE FUNCTION parallel_query(statement text)
  RETURNS SETOF record AS
$BODY$
 CLUSTER 'head'; RUN ON ALL;
$BODY$
  LANGUAGE plproxy VOLATILE;

Table (on all worker nodes):

CREATE TABLE users
(
  username text NOT NULL,
  CONSTRAINT users_pkey PRIMARY KEY (username)
)
WITH (
  OIDS=FALSE
);

With 10000 rows in two nodes, partitioned by username hash (~5000 on each node)

select * from parallel_query('select * from users') as (username text);

returns all 10000 rows. Since the nodes can be databases within the same server, there is no need for additional hardware, server installations etc. But if more performance is required in the future, adding more boxes is possible.

All it takes is logical partitioning and a bit of PL/pgSQL if you really need to run parallel queries.

There are some differences though. Take the following query:

select * from execute_query('select max(username) from users') as (username text);

"user_name_9995"
"user_name_9999"

It now returns two maximums, one for each partition. To get the expected result a second stage is needed:

select max(username) from execute_query('select max(username) from users') as (username text);

"user_name_9999"

The same applies for other aggregation functions like avg() etc.

The proxy function can finally be hidden in a VIEW:

CREATE OR REPLACE VIEW "users" AS select * from parallel_query('select * from users') as (username text);

From palloc() to palloc0()

I've just replaced all palloc()/memset() pairs for getting zeroed-out memory with palloc0(). I doubt that this will show a significant speedup, but it fits better into the PostgreSQL memory model and reduces the lines of code.

E.g.:

retval = (text *) palloc (len + VARHDRSZ);
memset(retval,0x0,len + VARHDRSZ);

becomes:

retval = (text *) palloc0 (len + VARHDRSZ);

The changes have been comitted to Github.