Infor LN Performance Tuning: Deep Dive Optimization Guide
Performance issues in Infor LN manifest as slow session response times, long batch processing windows, excessive database wait times, and user complaints about "the system is slow." Unlike simple web applications, LN performance depends on the interaction between four layers: the BShell application code, the DAL (Data Access Layer), the underlying database (Oracle or SQL Server), and the network/infrastructure. Optimizing a single layer often shifts the bottleneck to another layer, making systematic profiling essential before applying any fix.
Profiling and Identifying Bottlenecks
Before tuning anything, you must measure. LN provides built-in profiling tools that identify exactly where time is spent in each session execution. The SQL Trace facility captures every database query with execution time. The BShell Trace captures every line of code execution with timing. The Session Performance Monitor provides aggregate statistics per session over time. Use all three together to build a complete picture of where performance time is consumed across the application, DAL, and database layers.
- Enable SQL Trace: navigate to SQL Trace Configuration (ttadv9600m000); enable tracing for a specific session or user; the trace captures every SQL statement with execution time, row count, and execution plan; sort by duration to find the slowest queries—the top 10 queries typically account for 80% of database time
- Enable BShell Trace: run the target session with 'bshell -t sessionname'; the trace file shows every BShell line executed with wall-clock timing; look for loops that execute db.select/db.fetch inside another db.select/db.fetch loop—these N+1 query patterns are the most common BShell performance anti-pattern
- Session Performance Monitor: navigate to Performance Dashboard (ttadv9700m000); this shows per-session average response time, peak response time, and execution count over the last 7 days; identify the sessions with the highest total time (response time x execution count) for maximum optimization impact
- Database-level profiling: for SQL Server, use sys.dm_exec_query_stats and sys.dm_exec_requests to identify currently running queries and their wait types; for Oracle, use V$SQL and V$SESSION_WAIT; the wait type (PAGEIOLATCH = disk I/O, LCK_M_X = lock wait, SOS_SCHEDULER_YIELD = CPU) tells you exactly what the bottleneck is
- Infrastructure baseline: measure and document baseline metrics before any tuning: server CPU utilization (should be <70% sustained), memory utilization (SQL Server buffer cache hit ratio >99%), disk I/O latency (<10ms for SSD, <20ms for HDD), and network latency (<5ms within data center)
Database Optimization for LN
Database optimization provides the highest ROI for LN performance tuning because every user interaction and batch process ultimately translates to SQL queries. The three primary optimization levers are indexing (ensuring every query has an efficient access path), query optimization (rewriting slow queries to use set-based operations), and database configuration (memory allocation, parallelism settings, statistics maintenance). These optimizations apply to both Oracle and SQL Server LN deployments.
- Index optimization: identify missing indexes from the SQL Trace; for SQL Server, query sys.dm_db_missing_index_details to see indexes the query optimizer wants; for LN, create indexes through the Data Dictionary (ttadv4500m000) by adding secondary keys to tables—this ensures indexes survive LN upgrades, unlike indexes created directly in the database
- Statistics maintenance: LN tables with frequent inserts/updates need regular statistics updates for the query optimizer; schedule a weekly job: UPDATE STATISTICS [schema].[table] WITH FULLSCAN for high-volume tables (tdsls400, tdsls401, tcibd001, whwmd400); stale statistics cause the optimizer to choose table scans over index seeks
- Memory configuration: for SQL Server, allocate 70-80% of server memory to SQL Server and enable LPIM (Lock Pages in Memory) to prevent the OS from paging SQL Server buffer pool; for Oracle, configure SGA to 60-70% of physical memory with automatic memory management enabled
- Parallelism: set SQL Server MAXDOP (Max Degree of Parallelism) to 4 for LN databases; higher values cause thread contention; set Cost Threshold for Parallelism to 25 to prevent small queries from going parallel; for Oracle, set PARALLEL_MAX_SERVERS based on CPU cores
- TempDB optimization: LN batch processes heavily use TempDB for sorting and intermediate results; create 4-8 TempDB data files (one per CPU core, up to 8) with equal initial size; place TempDB on the fastest available storage (NVMe SSD); this alone can reduce MRP runtime by 20-30%
BShell Code and Session Optimization
Application-level optimization focuses on BShell code patterns that cause unnecessary database round-trips, excessive memory allocation, and redundant computation. The most impactful optimizations are eliminating N+1 query patterns (fetching related data row-by-row inside a loop), caching frequently accessed reference data in memory, and reducing the data transferred between the application server and the client. These optimizations are applied by modifying BShell code in custom sessions and DAL hooks.
- Eliminate N+1 queries: replace the pattern 'while db.fetch(tableA); db.select(tableB, key, fieldA.value); endwhile' with a single joined query using dal.query() or a custom stored procedure that returns both tables' data in one round-trip; this can reduce a 10-second operation to 100ms
- Cache reference data: for frequently accessed lookup tables (currencies, units of measure, tax codes), load the data into BShell arrays at session start using a single db.select loop, then read from the array during processing; use the global.data.area for cross-session caching that persists for the user's entire login session
- Reduce form data transfer: for Type 2 (grid) sessions, limit the number of columns retrieved to only those displayed in the grid; remove hidden columns from the IDO/DAL query; each unnecessary column adds to the data transfer between server and client, multiplied by every row in the result set
- Optimize string operations: BShell string concatenation using '&' creates a new string on each operation; for building large output strings in report loops, use the string.buffer library functions that pre-allocate memory and append in place—this reduces garbage collection pressure by 90%
- Session startup optimization: audit the before.display and after.display triggers of slow-starting sessions; move non-essential initialization (loading dropdown choices, pre-fetching related data) to lazy-load triggers that execute only when the user navigates to the relevant tab or field group
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