Decoding SQLite text

It looks like the user is asking for help deciphering a piece of text. Based on the "SQLite format 3" header, the text likely comes from an SQLite database, possibly with some corrupted content. I’ll guide them on how to extract the data using sqlite3 or Python to read and dump tables, or offer to parse the file directly if they share it.

Helping with SQLite DB recovery

Since we don't have the file, I'll guide the user on inspecting the SQLite 3 database from the text they provided. I'll explain how to reconstruct the schema, list tables, and inspect columns like primary keys and BLOBs. If they only have the text, I’ll share steps to recover the database using tools like strings or sqlite3 -cmd ".recover".

Guiding through SQLite recovery

For a corrupted DB, sqlite3's .recover can attempt to reconstruct it. I'll explain how to use Python to dump the data to CSV, get BLOBs out to files, and handle autogenerated indexes like sqlite_autoindex_. While speculating on table names (e.g., Unity or RPG Maker) is tempting, I'll focus on helping the user decode the schema and extract data.

Sample queries and caution

I can suggest a quick query like 'SELECT * FROM FileTextTableEntry;' for inspecting the data, but I'll caution that BLOB content might be compressed or in a custom format, which we can't detect without the actual file. I’ll offer to analyze the DB if they upload it and provide a small Python snippet to read the database from bytes themselves.

Thought for 31s

Planning database approach

I’m thinking about how to connect to a database, using sqlite3 for connections and pandas for handling data. I'll grab all tables using sqlite_master and check for specific fields like FileName and FileData. If they're there, I'll look at the data type, try to decompress it, or identify it as a nested SQLite file. Let's define a function for that!

Processing and extracting data

I’m planning to handle multiple file types, looking for magic headers like 'PK\x03\x04' for zip or 'OggS' for audio. I'll decompress if it's text or if it matches a known header. All extracted files will be saved with sanitized names to avoid duplicates. The output will include multiple DataFrames and zipping the result for easy download.

Planning SQL query execution

I’ll write the code to connect to the SQLite database using a path with parentheses. I need to be careful with edge cases, like the file not being valid. I'll fetch the list of tables, get their columns, count the rows, and handle data retrieval from FileTextTableEntry. I'll display data responsibly if it's small.

Displaying and exporting data

I’ll display the first 100 rows of the table properly and give the option for CSV export. For BLOB extraction, I'll fetch rows that have FileData columns and save them with either the FileName or a sanitized variation of the internal rowid. I'll store them in an appropriate location.

Thought for 39s

Planning data analysis

I’m setting up a report, summarizing findings and preparing to deliver the file. I’ll need to import libraries like sqlite3, pandas, and others. Then, I’ll iterate database tables to extract rows and analyze both text and blob columns, trying different decompression methods when necessary (like zlib or gzip). I won’t delve too deeply into images or PDFs, though.

Extracting and reporting data

I’ll create a report with columns like source table, key/filename, column, data type, size, etc., to track extracted content. For text, I’ll include preview snippets, while lengthier text may go into a separate file. I’ll also focus on extracting records related to currency and progress. The report will be available in both CSV/TSV format and a more readable text file like Markdown.

Building the report and interactive view

I’ll first set up the function to read the database, including handling file paths with tricky characters. Then, I’ll parse the schema to query tables related to file and text data (like BLOB entries). From this info, I’ll create dataframes for inventory and currency/progress matches. Lastly, I’ll save the content into text and CSV files, offering links for easy download. I'll display the data interactively for the user to explore.

Processing database and decoding BLOBs

I’ll connect to SQLite to grab tables and fetch rows. For each row, I’ll check if any columns are BLOBs. If so, I’ll attempt to decode them using various encodings like UTF-8, UTF-16, and CP1251, and calculate the "readability ratio" to determine if something sensible can be extracted. I'll display the results interactively using DataFrames, avoiding unnecessary explanations.

Thought for 50s

Summarizing and providing downloads

I’ll summarize the findings in Russian, including the mentioned files like schema.sql, flat_report.txt, and matches reports. I need to highlight the counts of matching records for currency and progress terms. While I’ve already displayed the data, I’ll provide a brief summary or top matches and offer download links for everything.

Thought for 56s