SQLite – read tokenizer.c

You can view the source code of tokenizer.c from Github. I highly suggest you download that file to your computer and view it in a legit C development IDE in order to easily navigate through and potentially fold or unfold some big blob of code for easier readability. I am not going to compile/debug/run the code in this post. So tools like sublime will suffice. (Also, I am not a C programmer by any means and only took 2 courses back in college, so do not feel intimated at all because we are probably on the same boat).

The code starts with disclaims that is always a good practice to articulate the license and responsibility for using the code. After that, it state the high level purpose of the code:

*************************************************************************
** An tokenizer for SQL
**
** This file contains C code that splits an SQL input string up into
** individual tokens and sends those tokens one-by-one over to the
** parser for analysis.
*/

So now we must expect the code will take a string as input, and split into tokens, and call some parsing function to analyze them one by one.

/* Character classes for tokenizing
**
** In the sqlite3GetToken() function, a switch() on aiClass[c] is implemented
** using a lookup table, whereas a switch() directly on c uses a binary search.
** The lookup table is much faster. To maximize speed, and to ensure that
** a lookup table is used, all of the classes need to be small integers and
** all of them need to be used within the switch.
*/
#define CC_X 0 /* The letter ‘x’, or start of BLOB literal */
#define CC_KYWD 1 /* Alphabetics or ‘_’. Usable in a keyword */

#define CC_ILLEGAL 27 /* Illegal character */

Then this blob of code defined several constants, which wherever the identifier like “CC_X”, “CC_ILLEGAL”, or actually any constants defined here starting with prefix “CC_”, will all be substituted  by the token-string which is the small integer 0 to 27. This will increase the readability in the code and guarantee the optimized performance when it is actually executed. To learn more about why using small integers is faster, check out this article written by Zuoliu Ding.

static const unsigned char aiClass[] = {
#ifdef SQLITE_ASCII
/* x0 x1 x2 x3 x4 x5 x6 x7 x8 x9 xa xb xc xd xe xf */
/* 0x */ 27, 27, 27, 27, 27, 27, 27, 27, 27, 7, 7, 27, 7, 7, 27, 27,
/* 1x */ 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27,
/* 2x */ 7, 15, 8, 5, 4, 22, 24, 8, 17, 18, 21, 20, 23, 11, 26, 16,
/* 3x */ 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 5, 19, 12, 14, 13, 6,
/* 4x */ 5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
/* 5x */ 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 9, 27, 27, 27, 1,
/* 6x */ 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
/* 7x */ 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 27, 10, 27, 25, 27,
/* 8x */ 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
/* 9x */ 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
/* Ax */ 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
/* Bx */ 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
/* Cx */ 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
/* Dx */ 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
/* Ex */ 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
/* Fx */ 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2
#endif

When I first saw this paragraph of code, I just found it to be so well formatted that anyone who has the minimum amount of patience and understand it perfectly. It is a lookup table which can quickly map the first FF (16 * 16 = 256) characters to its corresponding character class. Then there is this discussion of ASCII EBCDIC which we will omit because the code has a condition group “#ifdef” which will be initialized only based on the underlying coding format.

Then there is a basic normalization which is to convert upper case to lower case which is a bit more complex in EBCDIC. This also explains why sqlite is not case sensitive for certain tokens like keywords, you can use “SELECT”, “select” or even “Select” which makes no difference.

/*
** The sqlite3KeywordCode function looks up an identifier to determine if
** it is a keyword. If it is a keyword, the token code of that keyword is
** returned. If the input is not a keyword, TK_ID is returned.
**
** The implementation of this routine was generated by a program,
** mkkeywordhash.c, located in the tool subdirectory of the distribution.
** The output of the mkkeywordhash.c program is written into a file
** named keywordhash.h and then included into this source file by
** the #include below.
*/
#include “keywordhash.h”

Here is a link to the genrated keywordhash.h.

idchar

Again, depending on the platform, if we are using ASCII encoding format. This paragraph of code is the same as below, much shorter right?

#define IdChar(C) ((sqlite3CtypeMap[(unsigned char)C]&0x46)!=0)
#ifndef SQLITE_OMIT_COMPILEOPTION_DIAGS
int sqlite3IsIdChar(u8 c){ return IdChar(c); }
#endif

That is plenty of preparation work that we just went through. Now let’s take a look at this exciting function “sqlite3GetToken” that will

/*
** Return the length (in bytes) of the token that begins at z[0].
** Store the token type in *tokenType before returning.
*/

for example, if z is a string “select * from table1”, then z[0] is of course “s”, and this sqlite3GetToken will return two things:

  1. length of the token: 6 is clearly the length of the first token “select”
  2. token type is clearly a keyword which is CC_KYWD

Then you can imagine it will start looping this type of execution from the left to the right.

/* line 177 */int sqlite3GetToken(const unsigned char *z, int *tokenType){
int i, c;
switch( aiClass[*z] ){ /* Switch on the character-class of the first byte
** of the token. See the comment on the CC_ defines
** above. */
case CC_SPACE: {
testcase( z[0]==’ ‘ );
testcase( z[0]==’\t’ );
testcase( z[0]==’\n’ );
testcase( z[0]==’\f’ );
testcase( z[0]==’\r’ );
for(i=1; sqlite3Isspace(z[i]); i++){}
*tokenType = TK_SPACE;
return i;
}
case CC_MINUS: {

case CC_KYWD: {
for(i=1; aiClass[z[i]]<=CC_KYWD; i++){}
if( IdChar(z[i]) ){
/* This token started out using characters that can appear in keywords,
** but z[i] is a character not allowed within keywords, so this must
** be an identifier instead */
i++;
break;
}
*tokenType = TK_ID;
return keywordCode((char*)z, i, tokenType);
}
case CC_X: {
#ifndef SQLITE_OMIT_BLOB_LITERAL
testcase( z[0]==’x’ ); testcase( z[0]==’X’ );
if( z[1]==’\” ){
*tokenType = TK_BLOB;
for(i=2; sqlite3Isxdigit(z[i]); i++){}
if( z[i]!=’\” || i%2 ){
*tokenType = TK_ILLEGAL;
while( z[i] && z[i]!=’\” ){ i++; }
}
if( z[i] ) i++;
return i;
}
#endif
/* If it is not a BLOB literal, then it must be an ID, since no
** SQL keywords start with the letter ‘x’. Fall through */
}
case CC_ID: {
i = 1;
break;
}
default: {
*tokenType = TK_ILLEGAL;
return 1;
}
}
while( IdChar(z[i]) ){ i++; }
*tokenType = TK_ID;
return i;
/*line 448*/ }

If you look at the source code of this function, it accounts for (448-177) / 598 ~ 45% which is almost half of the tokenizer.c. And looking at the structure of the code, it is nothing other than a giant switch case statement. As you can see from the code, “i” is a very important variable that stores the length of the current token and return it when it matches.

For example, looking at the first case,

case CC_SPACE: {
testcase( z[0]==’ ‘ );
testcase( z[0]==’\t’ );
testcase( z[0]==’\n’ );
testcase( z[0]==’\f’ );
testcase( z[0]==’\r’ );
for(i=1; sqlite3Isspace(z[i]); i++){}
*tokenType = TK_SPACE;
return i;
}

So first, it checks if the “first” character is whitespace or not, then it goes through a for loop where the number of loops is returned by “sqlite3Ispace(z[i])”. So if there are 4 space contiguously and i was initialized to be 1, then sqlite3Ispace(z[1]) will check if the second element is space or not, which holds true, then i got incremented to be 2, and it goes to the second loop, and this will keep iterating till i = 4, which z[4] is actually the fourth element which is not a space any more. then the for loop statement will not hold true and break which left i to be 4. The tokenType will be returned to be TK_SPACE and the length of the token will be 4.

As you can see in the source code, many of the case statements were implemented in a looping format of either for loop or while loop meant to iterate through all the characters and complete this token.

Now we understand how this function sqlite3GetToken works, the last function which is sqlite3RunParser fits together with sqlite3GetToken.

There are several group conditions defined in the rest of the code. I took a screenshot of a while loop which I personally think best describes the functionality of how a tokenizer operates.

tokenwhile1

First, a few variables were defined a few lines above this screenshot where zSql is a pointer to the input SQL statement, for example, zSql[0] is the very first character “s” if the input statement is “select * from table1”. Then sqlite3GetToken function will take the input of the sql statatement and return the length of the token, and meanwhile updates the pointer of tokenType to refer to the right tokenType for the token. Hopefully this is not too confusing after the explanation of the study of sqlite3GetToken first. Next, there is a variable mxSqlLen has we first need to understand how it is initialized. It was defined as a int first and then initialized to be the value of “db->aLimit[SQLITE_LIMIT_SQL_LENGTH]”. db is the connection to the sqlite database and let’s take a look at the rest. Here is a link which listed a few SQLite run time limits which SQLITE_LIMIT_SQL_LENGTH is actually an identifier whose value is 1. At the same time, aLimit is a structure which stores all the limits and the value of index 1 which is the second element in the structure stores the maximum sql statement length. In the end, we know that mxSqlLen was initialized to be the possible maximum sql statement length. For every loop, sqlite3GetToken will retrieve the next token length, and this variable will subtract the length of that token. The following if statement also showed that as we tokenize the SQLstatement, if the length is too long, it will exceed the limit and error out and break. This is a good break condition check but probably the code is rarely executed for most of the scenarios. The else statement describes the scenario when it is reaching to the end of  the statement. In the end of the else statement is to subtract n from zSql, so the pointer zSql now should point to the next token of the input sql statement.

tokenizer_c_parse_or_not

This 20 lines of code is probably the most important part. Here is the decision tree where it determines if we need to pass the token to be parsed or not. In the if statement, it listed the scenario that if the token type if interrupt or illegal, then exit by either interrupt or error out, otherwise, it is a space, it will just update the zSql pointer to point to the next token by skipping the space token. In the else statement, it means it a valid a token that need to be passed to the parser. It will hand over the pointer and length of the next token to the parser.

The rest of tokenizer.c are mostly group condition which are suggested to read if you want to explore all conditions when specific flags are set up, etc.

In this tokenizer.c, we have covered two functions very extensively, sqlite3GetToken and sqlite3RunParser, hopefully it gave you a rough idea of how a tokenizer is implemented and how it is hooked up with the next step in the workflow – Parsing.

In the next post, we will look into how the parser are generated by lemon and potentially a lot of information around how parser works in general.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

SQLite – Tokenizer Requirements

A simple Google search led me to Wikipedia who defined Tokenization  as the process of “breaking a stream of text up into words, phrases, symbols and other meaning elements called tokens”. The next step is of course, to pass the list of tokens as the inputs for further processing like parsing as indicated in the architecture of SQLite.

The SQLite tokenizer is implemented in the src/tokenizer.c file of its source code, for easy access, we will use its Github repo as the future master place when we discuss code. That file was written in 2001 ( 16 years ago!) and has only a few hundred lines of C code including comments. But before, we even start going through the code, we want to have a better understanding of the purpose for this SQLite tokenizer. “what should my code do?”

As we have articulated above, this tokenizer.c should taken in a stream of characters, and then do something to break them into tokens, and categorize them into different types of tokens “words”, “phrases”, .etc. So there are a few key questions that we have to agree on or define before we move on.

What is the definition of a “token” within the realm of SQLite, that might include several sub questions like what should be included in a token, and what should not be included in a token, and what is allowed as being the “separator”, .etc. Then the second big topic will be what are the key categories of tokens, variable? SQL keywords? separators? I found this great “draft” that we can follow in order to better answer those questions.

Character Classes

In this draft documentation, it first lay down the foundation of defining all the characters (actually unicode) into 6 fundamental classes, whitespace, alphabetic, numeric, alphanumeric, hexadecimal, special.

There are two classes that I want to highlight and discuss more.

First is the definition of whitespace, based on the definition from a unicode lookup website.

  • u0009 character tabulation
  • u000a line feed (LF)
  • u000c form feed (FF)
  • u000d carriage return (CR)
  • u0020 space

I think if you were born after the typewriter era, please check out this stackoverflow question and this wikipedia page to better understand what is the difference forms of switching to a new line. Also, a picture borrowed from Stackexchange can probably explained in a much better way.

e4xm6

Second is the interesting definition of alphanumeric, or actually alphabetic essentially, it is not only A-Za-z  but also include “_” and any other character larger than u007f, so it includes all the “weird” characters including emojis like white smiling face and foreign languages .etc.  Since the definition of alphabetic is so wide hence the definition of special has actually been narrowed down to only the limited number of “weird” characters at the beginning of the unicode world.

Token Categorization

After understanding how character classes are defined, the draft listed all the requirements related to token. There they categorized tokens into several categories:

  • Whitespace tokens
  • Identifier tokens
  • Literals : string, integer literals .etc.
  • Variables :  Variables are used as placeholders in SQL statements for constant values that are to be bound at start-time
  • Operator tokens: “-“, “=”, “(“, etc.
  • Keyword tokens: “AFTER”, “SELECT”, “JOIN”, etc.

The tokenizer requirements are extremely helpful to read through just to get a sense of how the code is “supposed” to behave like in a plain English way. For example, we will go through one or two tokenizer requirements listed in the draft as an exercise:

The first requirement there is described as below:

Processing is left-to-right. This seems obvious, but it needs to be explicitly stated.

H41010: SQLite shall divide input SQL text into tokens working from left to right.

In the draft, all the requirements come in this format where a description comes first and then there is a more concise form in the tone of”shall”  starting with an identified in the format of “Hddddd”.  It is good that the formats are consistent but actually this format is not the “ideal state”. There is an official SQLite requirements page which documented ALL the requirements in one place. You are more than welcome to read through only the first section “About SQLite Requirements” just to get a sense of why the tokenizer requirements document is only a draft 🙂

OK, that is quite a high level overview of the tokenizing process along with some of the requirements. However, before jump into reading the source code, it will be great if there is a way where we can play with the “SQLite Tokenizer” directly in order to have a hands-on and intuitive understanding of what a tokenizer does by “writing code”! Sadly, a quick Google search did not provide any fast way of really hooking up the SQLite tokenizer directly to a tool that end user can easily use, there are tools out there like Python sqlite3 which can interact with SQLite pretty fast from the application perspective, but not as deep as the level we want.

So maybe we can go through the source code of tokenizer.c and see if there is a good place where we can inject some print statement to print out the output of the tokenizer before it feed into the parser. Since the source code is a few hundred of lines and we might even want to discuss and delve into the details. We will write up another post and go through the source code.

Please click this link to visit that post.

 

 

 

 

 

 

 

 

 

 

 

SQLite – Architecture

I am planning to spend sometime learning the internals about a open source database, and SQLite has been recommended to me as the “light” and well written tool to get started. Thanks for the community of SQLite, there are several good tutorials, articles and thorough technical documentations available on SQLite’s documentation page.

I started with the “Architecture of SQLite” and it provided a very good high level overview of how the components of SQLite pieces together.

arch2

Here is a great graph highlighting the ecosystem of SQLite and its major components. To me, there are two major components that I am specifically interested in at first glance, first is the SQLCompiler, I have never taken any true computer science class which I am assuming “compiler” is a core class that focus on this. I think better understanding of a SQL compiler works will be a good starting point. Second is the Backend which is the true “meat” of any database, how the data are allocated to the disk efficiently, and how the search happens, how you can optimized the allocations using canonical data structures and algorithms to provide outstanding performance is extremely interesting to me.

I am not going to reiterate too much what this article has already stated in this post. I will start with Tokenizer and hopefully have a better understanding of what a tokenizer is and how it is implemented in SQLite in the next chapter.

 

inode – metadata about LinuxVFS file

In Linux virtual file system, every file / directory is mapped to an object named “inode”. It is a data structure that stored the necessary metadata about the file, instead of content of the file itself.
Below is a great video tutorial from Youtube made by “theurbanpenguin” that was super helpful for me to learn what inode is.

A few handy commands:

ln file1 file2  # this will create a hard link file2 linked to the content of file1
stat f1    # this command will get the inode information

Theano Installation on Windows

I am trying to install Theano on my windows desktop and be able to use GPU there.

I am following this tutorial and IT DOES NOT WORK out of box!

I don’t think it is a complete waste of time, but it is sort data mining problem going through that detail tutorial. I only found less than 5 lines of code helpful which is included below.

I have already set up CUDA in visual studio before, so please refer to this post for the work that has already been done.

what I did only for theano:

  1. Install Anaconda Python
  2. pip install theano
  3. conda install mingw libpython
  4. Download Microsoft 10 SDK and install
  5. Add the following system environment (thanks to this NVIDIA post)
    1. LIB=C:\Program Files (x86)\Windows Kits\10\Lib\10.0.14393.0\um\x64;C:\Program Files (x86)\Windows Kits\10\Lib\10.0.14393.0\ucrt\x64
    2. INCLUDE=C:\Program Files (x86)\Windows Kits\10\Include\10.0.14393.0\ucrt
  6. .theanorc.txt[global]
    device = gpu
    floatX = float32[nvcc]
    flags=-LC:\Anaconda\libs
    compiler_bindir=C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\bin

    # Thanks to this Stackoverflow post

 

And here is a screenshot of it is working!

gpu

 

Pay attention to the time.

CUDA – By Example – Julia set

I bought a book “CUDA by example” written by Jason Sanders and Edward Kandrot. It was a pretty well written book where they start from hello world and really get into how CUDA C works, for someone who has not touched C for more than 5 years after college, it is a good choice.

Without purchasing that book, you can also start exploring the CUDA capability by downloading the examples from the book’s website.

Following is one of the examples from Chapter where you parallel program GPU to calculate and plot the Julia set.

However, time flies so you might need a bit tinker around since the source code from the author is not compatible with the latest CUDA toolkit.

Here are a few things on my side that I have to do:

  1. add __device__ to the cuComplex constructor which was missing before.
  2. use <<<N, 1>>> instead of <<N,1>>
  3. tweak the environment variable in VisualStudio Community Express thanks to this stackoverflow post.
  4. in the end, I still have to manually drop the glut32.dll into the debug folder where the .exe file resides.
julia

Juliaset plotted using GPU

TODO:

I think I got the idea of parallel processing from this example, however, I was trying to tweak the parameters and hopefully the performance difference between CPU and GPU might become obvious, however, I did not manage to get a satisfying result since all the jobs all finish really fast regardless of how large I code N to be or how big I change the for loop from 200. need to do more work.

Nvidia GeForce GTX 1050 Ti

I purchased this EVGA GTX 1050 TI and recently it just got delivered. The installation was pretty straight forward, nothing more complex than open the case and plug in the new card. After you install the driver, development kit and then you should be good to go.

I confirmed that my installation is correct by running the deviceQuery.exe and also by creating a project in VisualStudio.

cuda_visualstudiodevicequery

 

More about GPU programming coming in the future!