SQL developer in MQL: MQL recipes

Recipes

Register a local H2 database connection
Create a table and load rows safely
Transactions: commit, rollback, all or nothing
One value, one row, distinct values
Query to JSON, then enrich the document
Read a large result set by blocks
Run MQL on each row with sql parse
Export a query to CSV, Excel and PDF files
CSV, XML and HTML for three partners
sql encode or sql encode_std: the backslash trap

executed on MentDB

Register a local H2 database connection

Before writing any SQL, a developer declares the connection once in the connection manager: here an embedded H2 database in tmp/, built with the db.h2.embedded.config.get script. The recipe checks the registration and runs a first query, without ever printing the password.

Pipeline

01 · registerBuild and store the connectioncm set h2_embedded→02 · checkType and SCRUD availabilitycm exist→03 · inspectNon secret fields onlyjson fields→04 · queryFirst query on the databasesql value

Script

admin

#1. Build the H2 connection with the config script and register it;
cm set "tuto_db_h2" {execute "db.h2.embedded.config.get"
	"[database]" "./tmp/tuto_db_h2/shop"
	"[user]" "tuto"
	"[password]" "tuto_pwd"
;};
#2. Check it: registered, typed db, usable by SCRUD;
-> "[exists]" (cm exist "tuto_db_h2" "db");
-> "[as_file]" (cm exist "tuto_db_h2" "file");
-> "[scrud]" (string contains (cm show_scrud) "\"tuto_db_h2\"");
#3. Read the non secret fields only;
json load "tuto_db_cm" (cm get "tuto_db_h2");
-> "[info]" (concat (json select "tuto_db_cm" /subType) " " (json select "tuto_db_cm" /jdbc) ", properties: " (string regexp_replace (json fields "tuto_db_cm" /properties) "\s+" ""));
json unload "tuto_db_cm";
#The update script holds the password: count its lines, never print it;
-> "[gen_lines]" (string count (cm generate_update "tuto_db_h2") "json iobject");
#4. First query on the new database;
sql connect "tuto_db_s1" {cm get "tuto_db_h2";};
-> "[version]" (sql value "tuto_db_s1" "SELECT H2VERSION()");
-> "[sessions]" (sql show);
sql disconnect "tuto_db_s1";
concat "Registered as db: " [exists] ", as file: " [as_file] ", SCRUD ready: " [scrud] [_n_] [info] [_n_] "cm generate_update gives " [gen_lines] " field lines to edit" [_n_] "H2 version: " [version] ", open sessions: " (string regexp_replace [sessions] "\s+" "");

Result

mentdb

Registered as db: 1, as file: 0, SCRUD ready: 1
H2 jdbc:h2:./tmp/tuto_db_h2/shop, properties: ["DATABASE_TO_LOWER","password","MODE","user"]
cm generate_update gives 12 field lines to edit
H2 version: 2.1.214, open sessions: ["tuto_db_s1"]

Go further

List every declared connection of a type with cm show "db" or cm show_obj.
For MySQL, PostgreSQL or Oracle the same pattern applies with cm set mysql, cm set postgresql, cm set oracle.

executed on MentDB

Create a table and load rows safely

Create the orders table and load six orders that arrive as a JSON array. Every value goes through sql encode, so a customer called O'Brien cannot break the query or open an SQL injection.

Pipeline

01 · ddlDrop and create the tablesql dml→02 · loopLoop over the JSON rowsjson parse_array→03 · encodeEscape each valuesql encode→04 · checkCount the rowssql value

Script

admin

#1. Open a session on the local H2 database;
sql connect "tuto_db_s1" {cm get "tuto_db_h2";};
#2. Recreate the table so that the recipe can be replayed;
sql dml "tuto_db_s1" "DROP TABLE IF EXISTS orders";
sql dml "tuto_db_s1" "CREATE TABLE orders (id INT PRIMARY KEY, customer VARCHAR(40) NOT NULL, product VARCHAR(40), qty INT, price DECIMAL(10,2), created DATE)";
#3. The rows to insert come as a JSON array;
json load "tuto_db_rows" "[
 {\"id\": 1, \"customer\": \"Dupont\", \"product\": \"Keyboard\", \"qty\": 2, \"price\": 49.90, \"created\": \"2026-10-02\"},
 {\"id\": 2, \"customer\": \"O'Brien\", \"product\": \"Monitor 27\", \"qty\": 1, \"price\": 289.00, \"created\": \"2026-10-05\"},
 {\"id\": 3, \"customer\": \"Dupont\", \"product\": \"Mouse\", \"qty\": 3, \"price\": 19.50, \"created\": \"2026-10-09\"},
 {\"id\": 4, \"customer\": \"Nakamura\", \"product\": \"Laptop\", \"qty\": 1, \"price\": 1249.00, \"created\": \"2026-10-12\"},
 {\"id\": 5, \"customer\": \"Silva\", \"product\": \"Mouse\", \"qty\": 5, \"price\": 19.50, \"created\": \"2026-10-20\"},
 {\"id\": 6, \"customer\": \"O'Brien\", \"product\": \"Keyboard\", \"qty\": 1, \"price\": 49.90, \"created\": \"2026-10-28\"}
]";
#4. One INSERT per row, every value goes through sql encode;
-> "[inserted]" 0;
json parse_array "tuto_db_rows" "/" "[row]" {
	json load "tuto_db_row" [row];
	-> "[inserted]" (+ [inserted] (sql dml "tuto_db_s1" (concat "INSERT INTO orders (id, customer, product, qty, price, created) VALUES ("
		(sql encode (json select "tuto_db_row" /id)) ", "
		(sql encode (json select "tuto_db_row" /customer)) ", "
		(sql encode (json select "tuto_db_row" /product)) ", "
		(sql encode (json select "tuto_db_row" /qty)) ", "
		(sql encode (json select "tuto_db_row" /price)) ", "
		(sql encode (json select "tuto_db_row" /created)) ")"
	)));
};
json unload "tuto_db_row";
json unload "tuto_db_rows";
#5. Check what the table now holds;
-> "[count]" (sql value "tuto_db_s1" "SELECT COUNT(*) FROM orders");
-> "[obrien]" (sql value "tuto_db_s1" (concat "SELECT COUNT(*) FROM orders WHERE customer=" (sql encode "O'Brien")));
sql disconnect "tuto_db_s1";
concat "Rows inserted: " [inserted] [_n_] "Rows in the table: " [count] [_n_] "Orders of O'Brien: " [obrien] [_n_] "The quote is escaped as: " (sql encode "O'Brien");

Result

mentdb

Rows inserted: 6
Rows in the table: 6
Orders of O'Brien: 2
The quote is escaped as: 'O''Brien'

Go further

Wrap the loop in a transaction with sql auto_commit false and sql commit to load thousands of rows faster.
Read the rows from a CSV file with csv parse instead of a JSON array.

executed on MentDB

Transactions: commit, rollback, all or nothing

With auto-commit off, a test order is inserted then cancelled with rollback; then a batch of two inserts fails on a duplicate key and is rolled back as a whole; finally a valid batch is committed.

Pipeline

01 · manualManual commit modesql auto_commit→02 · cancelUndo the test ordersql rollback→03 · guardBatch in try, rollback on errortry→04 · commitValidate the good batchsql commit

Script

admin

#1. Open a session and switch to manual commit;
sql connect "tuto_db_s1" {cm get "tuto_db_h2";};
sql auto_commit "tuto_db_s1" false;
-> "[before]" (sql value "tuto_db_s1" "SELECT COUNT(*) FROM orders");
#2. A test order is inserted then cancelled with rollback;
sql dml "tuto_db_s1" (concat "INSERT INTO orders (id, customer, product, qty, price, created) VALUES (100, " (sql encode "Test") ", 'Cable', 1, 9.90, CURRENT_DATE)");
-> "[inside]" (sql value "tuto_db_s1" "SELECT COUNT(*) FROM orders");
sql rollback "tuto_db_s1";
-> "[after_rollback]" (sql value "tuto_db_s1" "SELECT COUNT(*) FROM orders");
#3. All or nothing: the second INSERT reuses id 1, so both are rolled back;
try {
	sql dml "tuto_db_s1" "INSERT INTO orders (id, customer, product, qty, price, created) VALUES (7, 'Silva', 'Webcam', 1, 59.00, '2026-10-30')";
	sql dml "tuto_db_s1" "INSERT INTO orders (id, customer, product, qty, price, created) VALUES (1, 'Silva', 'Headset', 1, 79.00, '2026-10-30')";
	sql commit "tuto_db_s1";
	-> "[batch]" "committed";
} {
	sql rollback "tuto_db_s1";
	-> "[batch]" (concat "rolled back, cause: " (string regexp_extract [err] "([A-Z][a-z ]+violation)" 1) ", SQLSTATE " (string regexp_extract [err] "\[(\d{5})-" 1));
} "[err]";
#4. A valid batch is committed;
sql dml "tuto_db_s1" "INSERT INTO orders (id, customer, product, qty, price, created) VALUES (7, 'Silva', 'Webcam', 1, 59.00, '2026-10-30')";
sql commit "tuto_db_s1";
-> "[final]" (sql value "tuto_db_s1" "SELECT COUNT(*) FROM orders");
sql auto_commit "tuto_db_s1" true;
sql disconnect "tuto_db_s1";
concat "Before: " [before] " | inside the transaction: " [inside] " | after rollback: " [after_rollback] [_n_] "Batch with a duplicate key: " [batch] [_n_] "After the valid commit: " [final];

Result

mentdb

Before: 6 | inside the transaction: 7 | after rollback: 6
Batch with a duplicate key: rolled back, cause: Unique index or primary key violation, SQLSTATE 23505
After the valid commit: 7

Go further

Read the SQLSTATE code of an error with string regexp_extract to decide whether to retry.
Put the batch in a script and run it through the stack with stack to retry it automatically.

executed on MentDB

One value, one row, distinct values

The three quick reads of every API: a single value (October turnover), a single row (the biggest order) and the distinct values of a column (customers who bought a mouse).

Pipeline

01 · valueTurnover of the monthsql value→02 · rowBiggest order as JSONsql row→03 · distinctDistinct customerssql col_distinct→04 · readField of the rowjson select

Script

admin

#1. Open a session;
sql connect "tuto_db_s1" {cm get "tuto_db_h2";};
#2. One value: the turnover of October;
-> "[turnover]" (sql value "tuto_db_s1" "SELECT SUM(qty * price) FROM orders WHERE created BETWEEN '2026-10-01' AND '2026-10-31'");
#3. One row: the biggest order;
-> "[top]" (sql row "tuto_db_s1" "SELECT id, customer, product, qty * price AS amount FROM orders ORDER BY qty * price DESC LIMIT 1");
#4. The distinct values of a column: who bought a mouse;
-> "[buyers]" (sql col_distinct "tuto_db_s1" (concat "SELECT customer FROM orders WHERE product=" (sql encode "Mouse")));
sql disconnect "tuto_db_s1";
#5. Read one field of the row;
json load "tuto_db_top" [top];
-> "[top_customer]" (json select "tuto_db_top" /customer);
json unload "tuto_db_top";
#6. col_distinct returns an object whose fields are the distinct values;
json load "tuto_db_buyers" [buyers];
-> "[buyer_list]" (string regexp_replace (json fields "tuto_db_buyers" /) "\s+" "");
json unload "tuto_db_buyers";
concat "Turnover October: " [turnover] [_n_] "Biggest order: " (string regexp_replace [top] "\s+" " ") [_n_] "Customer of the biggest order: " [top_customer] [_n_] "Mouse buyers: " [buyer_list];

Result

mentdb

Turnover October: 1902.70
Biggest order: { "product": "Laptop", "amount": "1249.00", "id": "4", "customer": "Nakamura" }
Customer of the biggest order: Nakamura
Mouse buyers: ["Silva","Dupont"]

Go further

Pass user input to the WHERE clause only through sql encode.
Return the row directly from a get script to build a detail endpoint.

executed on MentDB

Query to JSON, then enrich the document

The orders of a customer are read as a JSON document, then each line gets a computed amount field and the document gets the customer name: the typical body of a REST answer.

Pipeline

01 · queryRows as JSONsql to json→02 · navigateCount and read linesjson count→03 · computeExact amountbd*→04 · enrichAdd fieldsjson iobject

Script

admin

#1. Open a session and get the orders of a customer as JSON;
sql connect "tuto_db_s1" {cm get "tuto_db_h2";};
json load "tuto_db_res" (sql to json "tuto_db_s1" "orders" (concat "SELECT id, product, qty, price FROM orders WHERE customer=" (sql encode "Dupont") " ORDER BY id"));
sql disconnect "tuto_db_s1";
#2. Navigate in the result;
-> "[nb]" (json count "tuto_db_res" /data);
-> "[cols]" (json select "tuto_db_res" /columns);
-> "[first]" (json select "tuto_db_res" /data[0]/product);
#3. Add a computed field to each line;
for (-> "[i]" 0) (< [i] [nb]) (++ "[i]") {
	-> "[amount]" (bd* (json select "tuto_db_res" (concat "/data[" [i] "]/qty")) (json select "tuto_db_res" (concat "/data[" [i] "]/price")));
	json iobject "tuto_db_res" (concat "/data[" [i] "]") "amount" [amount] NUM;
};
json iobject "tuto_db_res" / "customer" "Dupont" STR;
-> "[out]" (json doc "tuto_db_res");
json unload "tuto_db_res";
[out];

Result

mentdb

{
  "data": [
    {
      "product": "Keyboard",
      "amount": 99.8,
      "price": "49.90",
      "qty": "2",
      "id": "1"
    },
    {
      "product": "Mouse",
      "amount": 58.5,
      "price": "19.50",
      "qty": "3",
      "id": "3"
    }
  ],
  "columns": [
    "id",
    "product",
    "qty",
    "price"
  ],
  "table": "orders",
  "customer": "Dupont"
}

Go further

Sort the lines with json sort before returning them.
Use sql to xml for a partner that expects XML.

executed on MentDB

Read a large result set by blocks

A cursor is opened on the table and read three rows at a time, as a batch would do on millions of rows without loading everything in memory. Each block is summed and logged.

Pipeline

01 · openOpen the cursorsql block open→02 · fetchNext block of 3 rowssql block to_json→03 · loopUntil the block is emptywhile→04 · closeClose the cursorsql block close

Script

admin

#1. Open a cursor on the whole table;
sql connect "tuto_db_s1" {cm get "tuto_db_h2";};
sql block open "tuto_db_s1" "orders" "SELECT id, customer, qty, price FROM orders ORDER BY id";
#2. Read it 3 lines at a time, as a batch job would do with millions of rows;
-> "[page]" 0;
-> "[total]" 0;
-> "[log]" "";
-> "[nb]" 1;
while (> [nb] 0) {
	json load "tuto_db_blk" (sql block to_json 3);
	-> "[nb]" (json count "tuto_db_blk" /data);
	if (> [nb] 0) {
		++ "[page]";
		-> "[ids]" "";
		for (-> "[i]" 0) (< [i] [nb]) (++ "[i]") {
			-> "[ids]" (concat [ids] (json select "tuto_db_blk" (concat "/data[" [i] "]/id")) " ");
			-> "[total]" (bd+ [total] (bd* (json select "tuto_db_blk" (concat "/data[" [i] "]/qty")) (json select "tuto_db_blk" (concat "/data[" [i] "]/price"))));
		};
		-> "[log]" (concat [log] "Block " [page] ": " [nb] " rows, ids " [ids] [_n_]);
	};
};
#3. Close the cursor and the session;
sql block close;
json unload "tuto_db_blk";
sql disconnect "tuto_db_s1";
concat [log] "Total amount: " [total];

Result

mentdb

Block 1: 3 rows, ids 1 2 3
Block 2: 3 rows, ids 4 5 6
Block 3: 1 rows, ids 7
Total amount: 1902.70

Go further

Write each block to a file with file append to export a huge table.
Commit every block in a target database to copy a table.

executed on MentDB

Run MQL on each row with sql parse

sql parse calls an MQL block for every row, with each column in a T_ variable. The recipe flags the orders above 200 and accumulates the amount per customer in a JSON report.

Pipeline

01 · parseMQL action per rowsql parse→02 · flagBig orders listjson iarray→03 · sumAmount per customerjson uobject→04 · returnReportjson doc

Script

admin

#1. Run an MQL action on each row, each column arrives as a T_ variable;
sql connect "tuto_db_s1" {cm get "tuto_db_h2";};
json load "tuto_db_report" "{\"big\": [], \"by_customer\": {}}";
-> "[nb]" 0;
sql parse "tuto_db_s1" "T" (concat "SELECT customer, product, qty, price FROM orders ORDER BY id") {
	++ "[nb]";
	-> "[amount]" (bd* [T_qty] [T_price]);
	#2. Flag the orders above 200;
	if (> [amount] 200) {
		json iarray "tuto_db_report" /big (concat [T_customer] ": " [T_product] " for " [amount]) STR;
	};
	#3. Accumulate the amount per customer;
	-> "[prev]" (json select "tuto_db_report" (concat "/by_customer/" [T_customer]));
	if (is null [prev]) {-> "[prev]" 0;};
	json uobject "tuto_db_report" /by_customer [T_customer] (bd+ [prev] [amount]) NUM;
};
sql disconnect "tuto_db_s1";
json iobject "tuto_db_report" / "rows" [nb] INT;
-> "[out]" (json doc "tuto_db_report");
json unload "tuto_db_report";
[out];

Result

mentdb

{
  "big": [
    "O\u0027Brien: Monitor 27 for 289.00",
    "Nakamura: Laptop for 1249.00"
  ],
  "rows": 7,
  "by_customer": {
    "Silva": 156.5,
    "Dupont": 158.3,
    "Nakamura": 1249.0,
    "O\u0027Brien": 338.9
  }
}

Go further

Call another script per row with execute to keep the action reusable.
Compare with sql block to_json when the action needs several rows at once.

executed on MentDB

Export a query to CSV, Excel and PDF files

The same query is exported to CSV, XLS, XLSX and PDF in a work folder, then each file is read back to check it: CSV lines, Excel sheet and last cell, PDF pages and text. The folder is deleted at the end.

Pipeline

01 · timeoutQuery timeoutsql set_timeout→02 · exportFour file formatssql to excelx→03 · checkRead the Excel file backexcelx cell get→04 · checkPDF pages and textpdf extract_text→05 · cleanDelete the folderfile delete

Script

admin

#1. Prepare the output folder and a query timeout;
-> "[dir]" "tmp/tuto_db_export";
file mkdir [dir];
sql set_timeout 30;
sql connect "tuto_db_s1" {cm get "tuto_db_h2";};
-> "[q]" "SELECT id, customer, product, qty, price, qty * price AS amount FROM orders ORDER BY id";
#2. The same query in four file formats;
sql to csv_file "tuto_db_s1" (concat [dir] "/orders.csv") [q] ";" "\"";
sql to excel "tuto_db_s1" "orders" [q] (concat [dir] "/orders.xls");
sql to excelx "tuto_db_s1" "orders" [q] (concat [dir] "/orders.xlsx");
sql to pdf "tuto_db_s1" "orders" [q] (concat [dir] "/orders.pdf");
sql disconnect "tuto_db_s1";
#3. Read the files back to check them;
-> "[csv_lines]" (file count_lines (concat [dir] "/orders.csv"));
excelx load "tuto_db_xl" (concat [dir] "/orders.xlsx");
-> "[sheets]" (excelx sheet show "tuto_db_xl");
-> "[sheet]" (json load select "tuto_db_sh" [sheets] /[0]);
-> "[last_row]" (excelx sheet max_row "tuto_db_xl" [sheet]);
-> "[cell]" (excelx cell get "tuto_db_xl" [sheet] [last_row] 1);
excelx close "tuto_db_xl";
json unload "tuto_db_sh";
-> "[pages]" (pdf page_count_file (concat [dir] "/orders.pdf"));
-> "[pdf_has_laptop]" (string contains (pdf extract_text (concat [dir] "/orders.pdf")) "Laptop");
-> "[xls_ok]" (file exist (concat [dir] "/orders.xls"));
#4. Clean up;
file delete [dir];
concat "CSV lines: " [csv_lines] [_n_] "Excel sheet: " [sheet] ", last row " [last_row] ", customer of that row: " [cell] [_n_] "PDF pages: " [pages] ", contains Laptop: " [pdf_has_laptop] [_n_] "XLS created: " [xls_ok] [_n_] "Folder still there: " (file exist [dir]);

Result

mentdb

CSV lines: 8
Excel sheet: orders, last row 7, customer of that row: Silva
PDF pages: 1, contains Laptop: 1
XLS created: 1
Folder still there: 0

Go further

Send the files with mail send or drop them on an SFTP server.
Generate a styled PDF from your own HTML with pdf from html.

executed on MentDB

CSV, XML and HTML for three partners

One query feeds three partners: accounting gets a CSV file (re-read with csv parse to check the total), the ERP gets XML, the sales manager gets the HTML table converted into a PDF.

Pipeline

01 · csvRows as CSV textsql to csv→02 · xmlRows as XMLsql to xml→03 · rereadCheck the CSV filecsv parse→04 · pdfHTML table to PDFpdf from html

Script

admin

#1. One query, three text formats for three partners;
sql connect "tuto_db_s1" {cm get "tuto_db_h2";};
-> "[q]" "SELECT id, customer, qty, price FROM orders WHERE created >= '2026-10-10' ORDER BY id";
-> "[csv]" (sql to csv "tuto_db_s1" "orders" [q] ";" "\"");
-> "[xml]" (sql to xml "tuto_db_s1" "orders" [q]);
-> "[html]" (sql to html "tuto_db_s1" "orders" [q]);
sql disconnect "tuto_db_s1";
#2. Accounting gets the CSV file, re-read here line by line with csv parse;
file mkdir "tmp/tuto_db_share";
file create "tmp/tuto_db_share/orders.csv" [csv] "UTF-8";
-> "[check]" 0;
csv parse (mql "R") (mql "tmp/tuto_db_share/orders.csv") (mql ";") (mql "\"") (mql "") {
	-> "[check]" (bd+ [check] (bd* [R_qty] [R_price]));
};
#3. The HTML table becomes a PDF for the sales manager;
pdf from html [html] "tmp/tuto_db_share/orders.pdf";
-> "[pages]" (pdf page_count_file "tmp/tuto_db_share/orders.pdf");
file delete "tmp/tuto_db_share";
#4. The XML goes to the ERP: count its items;
concat "CSV header: " (atom get [csv] 1 [_n_]) [_n_] "Amount re-read from the CSV file: " [check] [_n_] "XML rows for the ERP: " (string count [xml] "<customer>") [_n_] "PDF pages: " [pages];

Result

mentdb

CSV header: id;customer;qty;price
Amount re-read from the CSV file: 1455.40
XML rows for the ERP: 4
PDF pages: 1

Go further

Write the CSV straight to disk with sql to csv_file.
Parse the XML with the XML family before sending it.

executed on MentDB

sql encode or sql encode_std: the backslash trap

A Windows path with a quote is stored in H2 twice. sql encode also doubles backslashes, which is right for MySQL only; sql encode_std doubles only the quotes, which is right for H2, PostgreSQL, Oracle or SQL Server.

Pipeline

01 · mysqlMySQL style escapingsql encode→02 · standardStandard SQL escapingsql encode_std→03 · storeInsert bothsql dml→04 · compareRead back and compareequal

Script

admin

#1. A value with a quote and backslashes, as typed by a user;
-> "[path]" "C:\exports\l'ete.csv";
-> "[mysql_style]" (sql encode [path]);
-> "[std_style]" (sql encode_std [path]);
#2. Store it twice in a work table of the H2 database;
sql connect "tuto_db_s1" {cm get "tuto_db_h2";};
sql dml "tuto_db_s1" "CREATE TABLE IF NOT EXISTS tuto_notes (id INT PRIMARY KEY, txt VARCHAR(100))";
sql dml "tuto_db_s1" "DELETE FROM tuto_notes";
sql dml "tuto_db_s1" (concat "INSERT INTO tuto_notes VALUES (1, " [mysql_style] ")");
sql dml "tuto_db_s1" (concat "INSERT INTO tuto_notes VALUES (2, " [std_style] ")");
#3. Read back and compare with the original;
-> "[back1]" (sql value "tuto_db_s1" "SELECT txt FROM tuto_notes WHERE id=1");
-> "[back2]" (sql value "tuto_db_s1" "SELECT txt FROM tuto_notes WHERE id=2");
-> "[null_sql]" (sql encode null);
sql dml "tuto_db_s1" "DROP TABLE tuto_notes";
sql disconnect "tuto_db_s1";
concat "sql encode     : " [mysql_style] " -> stored " [back1] " -> same: " (equal [back1] [path]) [_n_] "sql encode_std : " [std_style] " -> stored " [back2] " -> same: " (equal [back2] [path]) [_n_] "A null value becomes: " [null_sql];

Result

mentdb

sql encode     : 'C:\\exports\\l''ete.csv' -> stored C:\\exports\\l'ete.csv -> same: 0
sql encode_std : 'C:\exports\l''ete.csv' -> stored C:\exports\l'ete.csv -> same: 1
A null value becomes: null

Go further