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.