columnar | Manticore Columnar Library | Search Engine library
kandi X-RAY | columnar Summary
kandi X-RAY | columnar Summary
️ PLEASE NOTE: This library is currently in beta and should be used in production with caution! The library is being actively developed and data formats can and will be changed.
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QUESTION
%{
#define FUNCT 300
#define IDENTIFIER 301
#define ASSGN 302
#define INTEGER 303
#define PRINT 304
#define TEXT 305
#define INPUT 306
#define CONTINUE 307
#define RETURN 308
#define IF 309
#define THEN 310
#define ENDIF 311
#define ELSE 312
#define WHILE 313
#define DO 314
#define ENDDO 315
#define END 316
#include
#include
#include
#define MAX_SYM 200
int found;
void initialize();
void create(char *lexeme, int scope, char type, char usage);
int readsymtab(char *lexeme, int scope, char usage);
%}
%%
[\t ]+ {}
= {int found = readsymtab(yytext,0,'L');
if(found == -1)
{
create(yytext,0,'S','L');
};
return(ASSGN) ;}
print {int found = readsymtab(yytext,0,'L'); //line 39
if(found == -1)
{
create(yytext,0,'S','L');
};
return(PRINT) ;}
input {int found = readsymtab(yytext,0,'L');
if(found == -1)
{
create(yytext,0,'S','L');
};
return(INPUT) ;}
continue {int found = readsymtab(yytext,0,'L');
if(found == -1)
{
create(yytext,0,'S','L');
};
return(CONTINUE) ;}
return {int found = readsymtab(yytext,0,'L');
if(found == -1)
{
create(yytext,0,'S','L');
};
return(RETURN) ;}
if {int found = readsymtab(yytext,0,'L');
if(found == -1)
{
create(yytext,0,'S','L');
};
return(IF) ;}
then {int found = readsymtab(yytext,0,'L');
if(found == -1)
{
create(yytext,0,'S','L');
};
return(THEN) ;}
endif {int found = readsymtab(yytext,0,'L');
if(found == -1)
{
create(yytext,0,'S','L');
};
return(ENDIF) ;}
else {int found = readsymtab(yytext,0,'L');
if(found == -1)
{
create(yytext,0,'S','L');
};
return(ELSE) ;}
while {int found = readsymtab(yytext,0,'L');
if(found == -1)
{
create(yytext,0,'S','L');
};
return(WHILE) ;}
do {int found = readsymtab(yytext,0,'L');
if(found == -1)
{
create(yytext,0,'S','L');
};
return(DO) ;}
enddo {int found = readsymtab(yytext,0,'L');
if(found == -1)
{
create(yytext,0,'S','L');
};
return(ENDDO) ;}
end {int found = readsymtab(yytext,0,'L');
if(found == -1)
{
create(yytext,0,'S','L');
};
return(END);
exit(0); ;}
funct {int found = readsymtab(yytext,0,'L');
if(found == -1)
{
create(yytext,0,'S','L');
};
return(FUNCT) ;}
[0-9]+ {int found = readsymtab(yytext,0,'L');
if(found == -1)
{
create(yytext,0,'I','L');
};
return(FUNCT) ;}
[a-zA-Z]+ {int found = readsymtab(yytext,0,'I');
if(found == -1)
{
create(yytext,0,'S','I');
};
return(IDENTIFIER) ;}
\"[^\"\n]+|[\\n]+\" {int found = readsymtab(yytext,0,'L'); //line130
if(found == -1)
{
create(yytext,0,'S','L');
};
return(TEXT) ;}
. {return(yytext[0]) ;}
%%
//new variable declaration
int num;
int scope;
struct symbtab
{
char Lexeme [18];
int Scope;
char Type;
char Usage;
int Reference;
};
struct symbtab arr_symtab[200]; //data structure in which the symbol table entries are stored
void print_fn() //function which actually prints the symbol tabel in columnar form
{
int rows;
printf("Row No Lexeme Scope Type Usage Reference\n");
for (rows=0; rows<=num; rows++){
printf("%6d %-16s %-7d %-7c %-7c %-7d \n",rows, arr_symtab[rows].Lexeme,arr_symtab[rows].Scope,arr_symtab[rows].Type,arr_symtab[rows].Usage,arr_symtab[rows].Reference);
}
}
void initialize() //function which enteres the initial value into the symbol table
{
num = -1;
int scope = 0;
char lexeme[18]= "FRED";
char type = 'I';
char usage = 'L';
create(lexeme,scope,type,usage);
}
void create(char *lexeme, int scope, char type, char usage) //function which creates a new entry in the symbol table
{
int reference;
if(type=='I' && usage =='L')
reference = atoi(lexeme);
else
reference = -1;
num = num+1;
strcpy(arr_symtab[num].Lexeme, lexeme);
arr_symtab[num].Scope = scope;
arr_symtab[num].Type = type;
arr_symtab[num].Usage = usage;
arr_symtab[num].Reference = reference;
}
int readsymtab(char *lexeme, int scope, char usage) //function which checks if the entry is already in the table or not and the takes the required action
{
for(int i=num; i>=0; i--){
int comp = strcmp(arr_symtab[i].Lexeme, lexeme);
if(comp==0 && arr_symtab[i].Scope==scope && arr_symtab[i].Usage==usage)
{
return i;
}
else
{
return -1;
}
}
}
int main()
{
//other lines
printf("\n COURSE: CSCI50200 NAME: Aryan Banyal NN: 01 Assignment #: 04 \n");
initialize();
yylex();
print_fn();
printf("End of test.\n");
return 0;
}
int yywrap ()
{
return 1;
}
ANSWER
Answered 2022-Mar-26 at 00:26You have (at least) three (somewhat) unrelated problems.
Using the lexical scannerYour code stops after reading a single token because you only call yylex() once (and ignore what it returns). yylex() returns a single token every time you call it; if you want to scan the entire file, you need to call it in a loop. It will return 0 when it encounters the end of input.
The pattern \"[^\"\n]+|[\\n]+\" has an | in the middle; that operator matches either of the patterns which surround it. So you are matching \"[^\"\n]+ or [\\n]+\". The first one matches a single double quote, followed by any number of characters (but at least one), which cannot be a quote or a new line. So that matches "aryan banyal without the closing quote but including the open quote. The second half of the alternative would match any number of characters (again, at least one) all of which are either a backslash or the letter n, and then a single double quote.
(I don't understand the thinking behind this pattern, and it is almost certainly not what you intended. Had you called yylex again after the match of "aryan banyal, the closing quote would not have been matched, because it would be the immediate next character, and the pattern insists that it be preceded by at least one backslash or n. (Maybe you intended that to be a newline, but there is not one of those either.)
I think you probably wanted to match the entire quoted string, and then to keep only the part between the quotes. If you had written the pattern correctly, that's what it would have matched, and then you would need to remove the double quotes. I'll leave writing the correct pattern as an exercise. You might want to read the short description of Flex patterns in the Flex manual; you probably also have some information in your class notes.
Selecting just a part of the matchIt's easy to remove the quote at the beginning of the token. All that requires is adding one to yytext. To get rid of the one at the end, you need to overwrite it with a \0, thereby terminating the string one character earlier. That's easy to do because Flex provides you with the length of the match in the variable yyleng. So you could set yytext[yyleng - 1] = '\0' and then call your symbol table function with yytext + 1.
If the above paragraph did not make sense, you should review any introductory text on string processing in C. Remember that in C, a string is nothing but an array of single characters (small integers) terminated with a 0. That's makes some things very easy to do, and other things a bit painful (but never mysterious).
QUESTION
I have three divs nested inside a parent div that look something like this:
...ANSWER
Answered 2022-Mar-17 at 07:00Add this inside head tag.
QUESTION
I looked at the standard documentation that I would expect to capture my need (Apache Arrow and Pandas), and I could not seem to figure it out.
I know Python best, so I would like to use Python, but it is not a strict requirement.
ProblemI need to move Parquet files from one location (a URL) to another (an Azure storage account, in this case using the Azure machine learning platform, but this is irrelevant to my problem).
These files are too large to simply perform pd.read_parquet("https://my-file-location.parquet"), since this reads the whole thing into an object.
I thought that there must be a simple way to create a file object and stream that object line by line -- or maybe column chunk by column chunk. Something like
...ANSWER
Answered 2021-Aug-24 at 06:21This is possible but takes a little bit of work because in addition to being columnar Parquet also requires a schema.
The rough workflow is:
Open a parquet file for reading.
Then use iter_batches to read back chunks of rows incrementally (you can also pass specific columns you want to read from the file to save IO/CPU).
You can then transform each
pa.RecordBatchfromiter_batchesfurther. Once you are done transforming the first batch you can get its schema and create a new ParquetWriter.For each transformed batch call write_table. You have to first convert it to a
pa.Table.Close the files.
Parquet requires random access, so it can't be streamed easily from a URI (pyarrow should support it if you opened the file via HTTP FSSpec) but I think you might get blocked on writes.
QUESTION
I am getting some execution plans in json format.
...ANSWER
Answered 2022-Feb-24 at 13:55Sure, you can parse and modify any JSON object in memory, but that has nothing to do with Spark. Related: What JSON library to use in Scala?
Any modifications you make wouldn't be persisted within the execution plan itself.
QUESTION
I am trying to glob a couple of performance data csv files from Open Hardware Monitor.
I can successfully and glob csv files with the following code:
...ANSWER
Answered 2022-Feb-24 at 13:51IIUC, try with skiprows=1 as parameter of pd.read_csv:
QUESTION
I want to know which way is better to model TDE with MarkLogic.
We have XML documents with many different DateTime fields. Most of the time (99.99%), the timestamp part is of no biz use. I guess the remaining 0.01% use case is for data problem investigation like when this happened.
TDE is neat and easy to expose document data to external BI tools via ODBC. All the columnar-type of modem BI tools (Power BI) prefer separating the Date and Timestamp fields from one single Datetime field. That will improve the BI tool performance significantly.
There are two options to do that.
Create two different fields in TDE from the same field. See the below screenshot. Most of the time, use Date type TDE field only.
Create only one DateTime field in TDE and use type casting in OPTICS API or SQL (ML version of favour)
Which way is better?
...ANSWER
Answered 2022-Feb-21 at 21:55I would say model the data as you plan to use it. In your case, adding the extra TDE field. A few points:
It should compress well.. Only one unique value per day per forest.
MarkLogic is a clustered database. Queries are resolved per forest, then Per node and then on the evaluator node. You should always be careful about filter, sort, join on any dynamic value since sometimes to resolve the items, more data has to be pushed to the evaluator node. Storing the data as you plan to use it helps minimize the risk of suboptimal queries in general, but even more so on a clustered database.
QUESTION
When I run queries on external Parquet tables in Snowflake, the queries are orders of magnitude slower than on the same tables copied into Snowflake or with any other cloud data warehouse I have tested on the same files.
Context:
I have tables belonging to the 10TB TPC-DS dataset in Parquet format on GCS and a Snowflake account in the same region (US Central). I have loaded those tables into Snowflake using create as select. I can run TPC-DS queries(here #28) on these internal tables with excellent performance. I was also able to query those files on GCS directly with data lake engines with excellent performance, as the files are "optimally" sized and internally sorted. However, when I query the same external tables on Snowflake, the query does not seem to finish in reasonable time (>4 minutes and counting, as opposed to 30 seconds, on the same virtual warehouse). Looking at the query profile, it seems that the number of records read in the table scans keeps growing indefinitely, resulting in a proportional amount of spilling to disk.
The table happens to be partitioned but it those not matter on the query of interest (which I tested with other engines).
What I would expect:
Assuming proper data "formatting", I would expect no major performance degradation compared to internal tables, as the setup is technically the same - data stored in columnar format in cloud object store - and as it is advertised as such by Snowflake. For example I saw no performance degradation with BigQuery on the exact same experiment.
Other than double checking my setup, I see don't see many things to try...
This is what the "in progress" part of the plan looks like 4 minutes into execution on the external table. All other operators are at 0% progress. You can see external bytes scanned=bytes spilled and 26G!! rows are produced. And this is what it looked like on a finished execution on the internal table executed in ~20 seconds. You can see that the left-most table scan should produce 1.4G rows but had produced 23G rows with the external table.
This is a sample of the DDL I used (I also tested without defining the partitioning column):
...ANSWER
Answered 2022-Jan-18 at 12:20Probably Snowflake plan assumes it must read every parquet file because it cannot tell beforehand if the files are sorted, number of unique values, nulls, minimum and maximum values for each column, etc.
This information is stored as an optional field in Parquet, but you'll need to read the parquet metadata first to find out.
When Snowflake uses internal tables, it has full control about storage, has information about indexes (if any), column stats, and how to optimize a query both from a logical and physical perspective.
QUESTION
Using PySpark, I am attempting to convert a spark DataFrame to a pandas DataFrame using the following:
ANSWER
Answered 2022-Feb-01 at 19:59It turned out the older version of Spark I was on was the problem. Upgrading Spark resolved the issue for me. You could use the SPARK_HOME env variable to try different version(s):
QUESTION
I am looking for suggestions to overcome the problem I am facing. To provide context, I am trying to develop a tool for monitoring our in-house HPC clusters. Since we use slurm workload scheduling, I have made use of the provided commands from them.
I am running the following command:
squeue -h -t R -O Partition,NumCPUs,tres-per-node which is used to tell for a partition CPUs allocated for the job and the resources like GPU. However, the partition names that we have are long which causes the columnar output to be treated as one value.
Output:
...ANSWER
Answered 2022-Jan-26 at 15:30The -O, --Format allows specifying a column width with :. So you can try
QUESTION
I have a dataframe with one of the columns being a list like so:
RefID Ref Ref1 (baby, 60, 0) Ref2 (something, 90, 2)I wanted to extract this list as separate fields, as in this code:
...ANSWER
Answered 2022-Jan-23 at 21:40You can apply(pd.Series). This will unpack the items as columns:
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