MongoDB aggregation pipeline groups data by GRP, sorts by seq within group, sums score fields, and writes sum into x field in last row of each group.

MongoDB Aggregation Pipeline groups adjacent states by combining sort, group, project, unwind, and group stages.

MySQL database has tables for bags & item relationships. Finding supersets requires complex SQL queries with JOINs & EXISTS. SPL simplifies this using 2-layer loops & intuitive set operations.

SPL simplifies SQL complexity for grouped subsets & positional references. Use `GROUP BY` without aggregation, then search within a 3-record interval (`[-1:1]`) for specified `Cardboard_Number`.

Database table describes payment cycles for multiple projects with regular & closing months. SQL uses window functions to identify payment months & calculate total amounts, but SPL offers a more straightforward approach.

SQL Server database requires indirect implementation using nested subqueries & window functions to associate tables & add SplitAmount field according to rules. SPL code can be more natural with grouped subsets.

Write code to calculate 24 from 4 cards using SPL. Enumerate permutations & operator combinations. Use eval to calculate expressions with different operation orders.

Big data products' performance claims often exaggerated. "Queries over trillion-row tables in seconds" means using indexes for target-search tasks, not full traversal. Efficient algorithms & resources key to performance, not just big data handling.

Recognizing handwritten Arabic numbers simplified with machine learning. Implemented from scratch without third-party libraries using esProc, achieving 91.1% accuracy in just over 10 lines of code.

SPL (Structured Programming Language) boasts table sequences, ordered computing & flow processing for efficient data manipulation & complex computations. Interpreted code & stored procedures reduce maintenance costs & system coupling.

SQL solution fails due to cumbersome window functions & lack of natural sequence numbers. SPL retains grouped subsets, provides natural seq nums & direct referencing, making complex multi-step calculations easier.

SPL outperforms SQL in aggregation, filtering, grouping & joining with 2000+ times speedup in group insurance queries & 100-concurrency self-service analysis

SPL outshines SQL with its set-oriented design, discreteness & orderliness, making it easier to write efficient & readable code for complex business logic.

SQL solution is cumbersome, SPL provides natural sequence numbers & position related calculations, making data processing easier.

SPL outshines SQL in data analysis with simpler syntax, efficient execution & powerful debugging tools. Say goodbye to complex code & hello to faster query times!

SQL solution for calculating asset balances: `WITH Accounts AS (SELECT DISTINCT Name FROM trans), Months AS (SELECT DATEADD(MONTH, n, '2021-01-01') AS MonthStart ...

SQL solution for multi-level self-association structure: use recursive CTEs to find hierarchy and zones. SPL provides direct functions for recursive references.

Pivot data from rows to columns using SPL: Query Postgresql, use pivot function with grouping column & Key column, convert values to new column names & values.

Need to find Oracle database records matching multiple strings in fields. Use JDBC, split param by spaces & filter records with superset match.

Need to add sequence number column "Seq" to MS SQL table: increment by 1 for same account within 1hr, reset to 1 after 1hr

Split MS SQL comma-separated string into 5 fields using SPL: A1) Query DB, A2) Split items field, A3) Create new table with Srllno1-Srllno5 fields.

Added "session id" column in Big Query: set first event as checkpoint, increment ID if > 7 days since last event. SPL code provided.

Computing highest average score in MongoDB: unwind Scores, group by StudentID, calculate avg_score, sort & get top records. esProc SPL also simplifies this task with a single script.

Extracting data from XML file: Read xml.xml as string, use xml function to get form layer data & export to 2D table in result.txt using esProc script p1.dfx.

Converting multilevel JSON to 2D CSV: use esProc script p1.dfx to split & promote identities fields, export as json.csv. See SPL source & integration guide.

Sorting CSV data by unique index: Read csv.csv, sort by upc14 column, export to result.txt using esProc script p1.dfx.

Cross-database join query using JDBC: Connect MySQL_A & B, read employee & family tables, join through EID, get RELATION between ENAME & FNAME.

Missing records found by comparing expected & actual data. Script p1.dfx calculates differences using esProc, integrating with Java for missing record detection.

Need to execute SQL with WEEKOFYEAR function in MySQL without rewriting. Use esProc script p1.dfx to translate and query database, then close connection.

Scores table used to find top 2 students with highest scores. SPL script connects to database, queries student scores and returns top 2 rankings.

Calculate user sessions: 10min inactivity or 5min logout. Count players scoring 3x in a row within 1min. Track daily active users & next-day retention rate of new users.

Joining MySQL persons with Oracle orders tables using esProc script. Full join on id_p lists all desired data. Read more: Download esProc script and integrate with Java.

Concatenate sales data from Oracle (2013) and MySQL (2014) using esProc script p1.dfx. Translate SQL functions for specific databases and combine results. Read more on SPL open source address.

Split huge CSV file into smaller chunks using esProc script: Create cursor, loop through rows, export to sample[n].csv. Read 100000 rows at a time due to memory constraints.

Divide CSV records into two tables: EMP (newest employees) and EMP_HIS (historical records). Use esProc script p1.dfx to group by EMPID, get latest UPDATEDATE values, and update or insert records into database.

We have a txt file with N+1 rows. Repeat row N M times and get an altogether N*M rows. Write a script in esProc to achieve this, then integrate it with a Java program using @i option for sequence return.

Merging two txt files through ID field using esProc: Import data1.txt (A1), data2.txt (A2). Join A1 & A2 on ID. Create result table sequence. Export to mergedFile.txt.

Problem: Convert text file blocks into strings. Solution: Use esProc script (p1.dfx) with @i import, A2 grouping & A3 export.

Remove duplicates from sorted text file: write script p1.dfx in esProc, import data with @i option, perform distinct on ordered sequence & export result.

Find member with ID 5: Connect to db (A1), retrieve Category table (A2) & set ID as PK. Use pfind() to find record with PK value 5.

Get max record with SPL: Retrieve Olympic medal count table, sort by game & total medals desc, get 1st record per game, group by nation, find longest group.

Converting categorized CSV to multilevel data: import CSV, group by metric, convert to JSON using esProc script.

Replacing duplicates: Here ____ ____ we go! So they're finally ____, performing for you. Script p1.dfx in esProc removes duplicate words with underlines.

Get min value record: Use minp() to find ID(s) of students with lowest math score in class 1. @a option returns all records with min value.

Text file analysis: handle null fields, empty rows & calculate sales. Script p1.dfx in esProc: loop through sequence, calculate Quantity * Unit Price, output errors or total sales.

Reverse row order in txt.txt: Write `esProc` script p1.dfx to reverse rows, outputting result to result.txt.

We have table SAMPLE in Sybase. Need to calculate REP difference between current & previous date for same SECURITY_ID. SQL solution complicated due to lack of window functions. esProc SPL makes it easy with direct support for ordered sets.

Get transaction info for Merchants Bank (600036) when closing price > 25 yuan in 2017 using SPL script.

Find class one students with math score 90+ using SPL script: Connect to db, retrieve Scores table & get records where math score is 90 or above.

Find student scores >= 90: A1 Connect to db; A2 Retrieve Scores table; A3 Get records where SCORE is 90 or above using select() with @1 option.

Find ordinal numbers of student records with SCORE 90+ in Scores table using SPL script: A1 Connect to db; A2 Retrieve Scores table; A3 Get ordinal numbers where SCORE is 90 or above.

Split large CSV file into smaller files using Java: use SPL code to divide file into parts with ordinal numbers like Orders1.csv, each part <5M.

Handle records with NULL column3 values: delete until first non-NULL appears, then modify each NULL value to its previous one. Repeat for new non-NULL values.

Compute Final Grade using Lab Exercise * 30% + Long Quiz * 30% + Alternative Assessment * 40%. Use Java with T()function & derive() function for a new csv file.

Here are two csv files (A and B) with same structure. Use Java to compare them in three ways: 1. Equal primary keys with different values. 2. Records existing only in A. 3. Records existing only in B. Write results to new csv files.

Reorganize csv file with Java using SPL script: import data.csv as 2D table, handle carriage returns & quotes, modify table with run() function.

Group temp_data by year & LETTER, count records, align to cross product of year & letter; arrange years chronologically & letters by LETTER_SEQ.

Group PostgreSQL tmp table by source_id & sort by event_date. Add SERIES_ID to number sub-groups under each source_id using SPL code.

Retrieve MSSQL data, sort by turn. Group by stop, reset cum weight > 1000kg. Get last record's name in each group.

Parsing BigQuery JSON strings in SPL: A1 runs SQL to retrieve JSON, A2 parses multilayer strings to extract values under "values.value" field.

List flight connections for each group. Use SPL code to split ROUTE field by dash, remove duplicates & concatenate.

Retrieve data from SAS, sort by Visit_code & Date. Group by Id, create new table with Office_Visit=1 if consultation after surgery, SX_past_6mo=1 if 6 months later.

In PostgreSQL, use SPL code: new() function & array() to find median of all column values in each row.

Create computed column Flag in MSSQL: group rows by Serial_Number, record "Y" for duplicate Last_update_date values, otherwise "N".

Inserting records from NEWTABLE into OLDTABLE based on RANK values, ensuring continuity and updating ranks accordingly. Solution involves using esProc script p1.dfx to perform SQL queries and insertions.

We need to group STAKEHOLDER table by CLASS, combining column values into one row. Solution: Write an SPL script (p1.dfx) using esProc, grouping by CLASS and joining multiple columns.