--- Brand: klarmetrics.com Author: Kierin Dougoud Expertise: BI & AI Consultant | Turning messy data into decisions | Qlik Cloud • Python • Agentic AI Author-Profile: https://www.linkedin.com/in/mkierin/ Canonical-URL: https://klarmetrics.com/09-qlik-data-modeling-problems/ --- # Qlik Sense Synthetic Keys & Circular References: How to Identify and Fix Data Model Problems *This is Article 9 of the [Qlik Sense Data Modeling Course](https://klarmetrics.com/qlik-sense-data-modeling-course/).* # 📚 Qlik Sense Course – Article 9 of 28 ← **Previous Article:** [QVD Optimization – 100x Faster Loads](https://klarmetrics.com/08-qlik-qvd-optimization/) → **Next Article:** [Star Schema in Qlik – Performance & Clarity](https://klarmetrics.com/10-qlik-star-schema/) # What Can You Learn About Synthetic Keys & Circular References in 18 Minutes? After this guide, you will be able to: * Identify synthetic keys and resolve them in 5 minutes through deliberate field renaming * Diagnose circular references and eliminate them through targeted architectural changes * Implement preventive naming conventions that prevent 90% of all data modeling problems **Time investment:** 18 min reading + 2 hrs hands-on **Prerequisite:** Basic knowledge of [Table Relationships](https://klarmetrics.com/03-qlik-joins-keeps/) and simple data models **Quick Win:** Identify synthetic keys in 3 minutes and understand why they are problematic # How Can I Quickly Identify Synthetic Keys? **The problem:** Your data model shows mysterious $Syn tables and performance becomes inexplicably slow. **Solution in 3 steps:** * Open the [Data Model Viewer for diagnosing issues](https://help.qlik.com/en-US/cloud-services/Subsystems/Hub/Content/Sense_Hub/Visualizations/data-model-viewer.htm) (data model icon in the app) * Look for tables named “$Syn1”, “$Syn2”, etc. * Check which fields are causing these synthetic keys // Typical situation that creates Synthetic Keys Customers: LOAD CustomerID, CustomerName, Region, CountryCode FROM [lib://Data/customers.csv]; Orders: LOAD OrderID, CustomerID, OrderDate, Region, // Problem: Shared fields CustomerID AND Region CountryCode // Problem: Three shared fields = Synthetic Key FROM [lib://Data/orders.csv]; **Explanation:** * **Synthetic Key Cause:** More than one field with the same name between tables → Qlik automatically creates a $Syn table * **Performance Impact:** Every expression must go through the Synthetic Table → 2-5x slower calculations * **Model Complexity:** Instead of 2 tables, 3-4 tables are created → hard to understand **Performance:** 1M Rows without Synthetic: Expressions 2-3s → With Synthetic: 8-15s **Checkpoint:** Do you see $Syn tables in the data model? → Problem identified! No $Syn tables? → Everything is optimal or there are circular references. See [Troubleshooting](#troubleshooting). For a systematic solution, see [Resolving Synthetic Keys](#methode-1-synthetic-keys). # Which Solution Fits Your Problem? First identify the problem pattern, then choose the appropriate solution strategy: # How Do I Resolve Synthetic Keys & Circular References in Qlik Sense? Do you have $Syn tables in your data model? * **YES** → Problem: Synthetic Keys → [Method 1: Resolve Synthetic Keys](#methode-1-synthetic-keys) * **NO** → Can you navigate from any table to any other via multiple paths? **YES** → Problem: Circular References → [Method 2: Resolve Circular References](#methode-2-circular-references) * **NO** → Model is correct → [Method 3: Implement Prevention](#methode-3-praevention) Problem Type Detection Solution Time Required Complexity Frequency [Synthetic Keys](#methode-1-synthetic-keys) $Syn tables visible Field renaming 15-30 min Low 80% of all cases [Circular References](#methode-2-circular-references) Multiple paths between tables Architecture change 1-3 hrs High 15% of all cases [Prevention](#methode-3-praevention) Building a new model Naming conventions 30 min setup Low Best practice **Legend:** * Time Required: Average effort for typical business data models (10-20 tables) * Complexity: Technical difficulty and required architectural knowledge * Frequency: Share of real Qlik projects with this problem pattern # How Can You Resolve Synthetic Keys in Qlik Sense? **The Problem** Two or more tables share multiple fields with identical names. Qlik interprets this as a multi-field key and automatically creates Synthetic Keys ($Syn1, $Syn2). This leads to performance problems and incomprehensible data models. **The Solution** Deliberate field renaming: Only true association fields (keys) keep identical names; all other fields are renamed to clarify their semantic role. // Problematic code (creates Synthetic Keys) Customers: LOAD CustomerID, CustomerName, Region, // Problem: Region in both tables CountryCode, // Problem: CountryCode in both tables CreatedDate // Problem: CreatedDate in both tables FROM [lib://Data/customers.csv]; Orders: LOAD OrderID, CustomerID, // OK: Association field OrderDate, Region, // Problem: Creates Synthetic Key CountryCode, // Problem: Creates Synthetic Key CreatedDate // Problem: Creates Synthetic Key FROM [lib://Data/orders.csv]; // Correct solution (avoids Synthetic Keys) Customers: LOAD CustomerID, // Key: Keep identical CustomerName, Region as CustomerRegion, // Renamed: Clarifies semantics CountryCode as CustomerCountry, CreatedDate as CustomerCreatedDate FROM [lib://Data/customers.csv]; Orders: LOAD OrderID, CustomerID, // Key: Keep identical for association OrderDate, Region as OrderRegion, // Renamed: Different semantic meaning CountryCode as ShipToCountry, // Renamed: More precise description CreatedDate as OrderCreatedDate FROM [lib://Data/orders.csv]; **Strategy explained:** * **Key fields unchanged:** CustomerID stays CustomerID for automatic association * **Semantic renaming:** Region in Orders might be the shipping region, in Customers the customer region * **Prefix strategy:** Table name as prefix (Customer*, Order*) for instant identification **Performance context:** * 2M Orders + 50k Customers with Synthetic: Chart calculation 12-18s * Same data without Synthetic: Chart calculation 3-5s * Memory reduction: 25-40% less RAM by eliminating $Syn tables **The 3 Most Common Mistakes (and Solutions)** **Mistake 1: Renaming all shared fields, including keys** * **Symptom:** No association between tables anymore, data is isolated * **Cause:** Association fields (CustomerID) were also renamed * **Solution:** Keys keep identical names, only rename non-key fields * **Code:** CustomerID identical in both tables, but Region → CustomerRegion vs OrderRegion **Mistake 2: Composite keys destroyed by renaming** * **Symptom:** Many-to-Many relationships appear where 1:1 was expected * **Cause:** Part of a composite key was renamed * **Solution:** Treat composite key fields as a set, keep all identical or create a concatenated key * **Code:** CustomerID & '|' & LocationID as CompositeKey instead of separate fields **Mistake 3: Non-specific renaming without semantics** * **Symptom:** Fields are named “Region1”, “Region2” – unclear which is for what * **Cause:** Mechanical renaming without business context * **Solution:** Semantic names based on business meaning * **Code:** Instead of “Region1”: “CustomerRegion”, “ShippingRegion”, “BillingRegion” **Best Practices** * **Key field identification:** Deliberately document which fields are association fields (1:1, 1:N, M:N). Only these keep identical names. Business rule: A field is a key if it’s used for JOINs or lookup purposes. * **Semantic prefixes:** Use table names as prefixes (Customer*, Order*, Product*). Alternative: Functional prefixes (Billing*, Shipping*, Reporting*). Consistency matters more than the specific convention. * **Documentation in script:** Comment on renames: // CustomerRegion: Customer's home region vs OrderRegion: Shipping region. Essential for successor developers to understand the model. * **Validation after changes:** Check the data model: No more $Syn tables? Row counts before/after identical? Test charts work as expected? Systematic validation prevents undiscovered side effects. **Checkpoint:** Why should only key fields have identical names, but not all others? Show answer Identical names signal Qlik to create automatic associations. For key fields, this is desired (CustomerID should connect Customers and Orders). For non-key fields, it leads to unwanted Synthetic Keys and incorrect aggregations. Example: CustomerRegion and OrderRegion have different business meanings and should not be automatically associated. For more complex models with many associations, see [Star Schema Implementation](https://klarmetrics.com/10-qlik-star-schema/). **→ Next step:** For very complex models with many tables, field renaming alone isn’t enough. [Resolving Circular References](#methode-2-circular-references) shows architecture-based solutions. # How Do I Resolve Circular References in Qlik Sense? **The Problem** Multiple paths between tables arise from complex business relationships. Example: Customer → Orders → Product and Customer → Territory → Product. Qlik cannot decide which path to use for calculations, leading to incorrect aggregations. **The Solution** Architectural decoupling through deliberate path interruption: Use either denormalization or [link tables to](https://klarmetrics.com/12-qlik-link-tables/) create unambiguous relationships. // Problematic structure (Circular Reference) Customers: LOAD CustomerID, CustomerName, TerritoryID FROM customers.csv; Territory: LOAD TerritoryID, TerritoryName, RegionID FROM territory.csv; Orders: LOAD OrderID, CustomerID, ProductID, TerritoryID FROM orders.csv; // Circular: Customer→Territory AND Orders→Territory Products: LOAD ProductID, ProductName, RegionID FROM products.csv; // Circular: Territory→Region AND Products→Region // Solution 1: Denormalization (simplest solution) Customers: LOAD CustomerID, CustomerName, TerritoryID as CustomerTerritoryID, // Renaming breaks the path ApplyMap('TerritoryMap', TerritoryID, 'Unknown') as CustomerTerritory FROM customers.csv; // Create Territory Map TerritoryMap: MAPPING LOAD TerritoryID, TerritoryName FROM territory.csv; Orders: LOAD OrderID, CustomerID, ProductID, TerritoryID as OrderTerritoryID FROM orders.csv; Products: LOAD ProductID, ProductName, RegionID as ProductRegionID FROM products.csv; // Solution 2: Link table for complex M:N relationships Customer_Territory_Link: LOAD CustomerID & '|' & TerritoryID as Customer_Territory_Key, CustomerID, TerritoryID, ValidFrom, ValidTo FROM customer_territory_assignments.csv; **Strategy explained:** * **Denormalization:** Redundant data inclusion prevents complex joins. Performance advantage through fewer tables. * **Path interruption:** Deliberate field renaming destroys automatic associations where not desired. * **Link tables:** For true M:N relationships with additional attributes (time validity, weighting). **Performance context:** * 5 tables with Circular Reference: Expressions 15-25s, unpredictable results * 3 tables after denormalization: Expressions 4-8s, unambiguous calculations * Trade-off: More data redundancy vs. better performance and comprehensibility **The 3 Most Common Mistakes (and Solutions)** **Mistake 1: Overly aggressive denormalization leads to data explosion** * **Symptom:** Tables become huge, every product change must be replicated in the Orders table * **Cause:** High-change-rate attributes were denormalized instead of kept as a separate dimension * **Solution:** Only denormalize stable attributes (Name, Category), not variable ones (Price, Status) * **Code:** Denormalize ProductCategory, not ProductPrice **Mistake 2: Link tables without business context create even more circular references** * **Symptom:** Link table connects to too many other tables, problem gets worse * **Cause:** Link table contains too many foreign keys without clear boundaries * **Solution:** Link table only for one specific M:N relationship, not as a “Central Hub” * **Code:** Customer_Product_Preferences, not Customer_All_Relationships **Mistake 3: Incomplete path interruption leaves circular reference intact** * **Symptom:** After renaming, still unexpected associations between tables * **Cause:** Overlooked that another field also creates automatic association * **Solution:** Systematically check all shared field names between problematic tables * **Code:** Don’t just rename TerritoryID, also RegionCode, CountryCode, etc. **Best Practices** * **Architecture Decision Documentation:** Deliberately document why each path was interrupted. Business rule: Which relationship is primary, which is secondary? Essential for successor developers and future extensions. * **Star Schema as target architecture:** Denormalization naturally leads to Star Schema (1 Fact, multiple Dimensions). Plan deliberately in this direction instead of ad-hoc fixes. More maintainable and performance-optimal long-term. * **Staged Refactoring:** Resolve circular references step by step, not all at once. Test after each step that calculations are still correct. Have a rollback strategy for each refactoring step. * **Business Rule Validation:** After architecture changes: Validate with business users that aggregations still meet expectations. Circular references often lead to subtly wrong numbers that are discovered late. **Checkpoint:** When should you choose denormalization vs. link tables? Show answer Denormalization for stable 1:N relationships where the “1” side rarely changes (Customer → Region). Link tables for true M:N relationships with additional attributes (Customer → Territory with validity period). Rule of thumb: Less than 20% data redundancy → Denormalization. More than 20% → separate table with deliberate association. For systematic architecture design, see [Star Schema Design Patterns](https://klarmetrics.com/10-qlik-star-schema/). **→ Next step:** Prevention is better than reactive problem-solving. [Implementing Naming Conventions](#methode-3-praevention) prevents 90% of all modeling problems. # How Do Preventive Naming Conventions Help with Synthetic Keys & Circular References in Qlik Sense? **The Problem** Reactive problem-solving is time-consuming and error-prone. Teams develop different modeling approaches, leading to inconsistent and hard-to-maintain data models. **The Solution** Proactive naming conventions and modeling standards that automatically create correct associations and prevent problematic patterns. // Naming Convention Template // Format: [TableName]_[FieldType]_[BusinessEntity] // 1. Key fields: Always with "ID" suffix, table reference in the name Customers: LOAD CustomerID, // Standard Key Format CustomerName, // Business Attribute CustomerRegion, // Prefix = Table ownership CustomerCreatedDate // Prefix + descriptive name FROM customers.csv; Orders: LOAD OrderID, // Own Key CustomerID, // Foreign Key - identical for association OrderDate, // Prefix for uniqueness OrderRegion, // Different from CustomerRegion - no Synthetic Key OrderAmount FROM orders.csv; // 2. Fact vs Dimension Pattern Fact_Sales: // Fact Tables with "Fact_" prefix LOAD SalesID, CustomerID, // FK to Dimension ProductID, // FK to Dimension SalesDate, SalesAmount, // Measure in Fact SalesQuantity // Measure in Fact FROM sales.csv; Dim_Customers: // Dimension Tables with "Dim_" prefix LOAD CustomerID, // PK CustomerName, // Attribute CustomerSegment, // Attribute CustomerRegion // Attribute FROM customers.csv; // 3. Avoid Generic Names LOAD ID as CustomerID, // Specific instead of generic Name as CustomerName, // Add context Type as CustomerType, // Precise naming Status as CustomerStatus, // Unambiguous assignment Date as CustomerRegistrationDate // Full semantic clarity FROM customers.csv; **Strategy explained:** * **Prefix system:** Table name as prefix prevents accidental name collisions * **Suffix standards:** ID = Key, Date = Date, Amount = Monetary value, Count = Count * **Fact/Dim separation:** Clear distinction between transactional and dimensional data **Performance context:** * Team efficiency: 50-70% less time for data model debugging * Onboarding: New developers understand the model in 30 min instead of 3 hrs * Maintenance: Structural changes in 15 min instead of 2 hrs (no unexpected side effects) **The 3 Most Common Mistakes (and Solutions)** **Mistake 1: Conventions too strict, blocking sensible exceptions** * **Symptom:** Developers bypass conventions because they’re impractical in special cases * **Cause:** Rules don’t account for edge cases or business-specific requirements * **Solution:** Document defined exceptions: When are deviations allowed and why? * **Code:** Exception: Bridge tables may use generic names (OrderLine_ProductID vs OrderLineProductID) **Mistake 2: Conventions not enforced – different standards in parallel** * **Symptom:** New tables follow old convention, existing tables keep old names * **Cause:** No systematic migration of legacy code and missing code review processes * **Solution:** Gradual migration + template apps for new projects + peer review * **Code:** Refactoring plan: Migrate 2 tables per sprint to new convention **Mistake 3: Over-engineering: Conventions become too complex for practical use** * **Symptom:** Field names become unreadably long (Customer_Billing_Address_PostalCode_Primary) * **Cause:** Trying to encode every possible piece of information in the field name * **Solution:** Balance: Uniqueness vs. readability. Documentation supplements short names * **Code:** CustomerBillingZip (with documentation) instead of Customer_Billing_Address_PostalCode_Primary **Best Practices** * **Template-Driven Development:** Create template apps with correct naming patterns. New projects copy the template instead of from-scratch development. Includes standard tables (Calendar, Lookup Tables) with correct names. * **Code Review Integration:** Naming convention checklist for all pull requests. Automated checks where possible (prefix validation, reserved words check). Cultural change: Conventions are not optional but a quality standard. * **Living Documentation:** Conventions document with real examples from current apps. Regular updates when new patterns emerge. FAQ section for common edge cases and their approved solutions. * **Tooling Support:** Include files with standard mappings and lookup tables. Code snippets for IDE/text editors. Validation scripts that check models for convention compliance. **Checkpoint:** Why are prefixes better than suffixes for table ownership? Show answer Prefixes naturally group fields in alphabetical sorting (CustomerID, CustomerName, CustomerRegion appear together). Suffixes scatter related fields (CustomerID, OrderID, ProductID would be separated). This significantly facilitates navigation in field lists and expression dialogs. For complete architecture patterns, see [Enterprise Data Architecture](https://klarmetrics.com/24-qlik-three-stage-architecture/). **→ Next step:** With solid naming conventions, you can build clean star schemas. [Star Schema Implementation](https://klarmetrics.com/10-qlik-star-schema/) shows optimal data model architecture. # How Do All Solution Approaches for Synthetic Keys & Circular References Compare? This evaluation is based on typical enterprise projects (10-30 tables, 2-5 developers, 2-3 year project duration). Approach Problem Resolution Implementation Maintenance Team Scalability Performance Recommendation Reactive Fixes Quick 15 min High Poor Medium Prototypes only Synthetic Key Resolution Targeted 30 min Medium Medium High Standard solution Architecture Refactoring Sustainable 2-4 hrs Low Good Very High Complex models Preventive Conventions Proactive 4-8 hrs Very Low Very Good Optimal New projects **Legend:** * Problem Resolution: How effectively the specific problem is addressed * Implementation: Initial time investment to reach a working solution * Maintenance: Long-term effort for upkeep and adjustments * Team Scalability: How well the approach works with multiple developers * Performance: Impact on Qlik app performance and user experience **Interpretation:** Reactive fixes are tempting due to quick implementation but lead to technical debt. Preventive conventions have high initial investment but pay off exponentially in team work and long-term maintenance. # How Do I Troubleshoot Synthetic Keys & Circular References in Qlik Sense? **Alphabetical index:** * [Associations disappeared after field renaming](#problem-associations-verschwunden) * [Circular reference persists despite renaming](#problem-circular-reference-persistent) * [Performance worse after synthetic key resolution](#problem-performance-schlechter) * [Star schema not automatically recognized](#problem-star-schema-fails) * [Synthetic keys return after reload](#problem-synthetic-key-kommt-zurueck) * [Wrong aggregations despite correct model](#problem-wrong-aggregations) # How Do I Fix Synthetic Keys Returning After a Reload? **Symptom:** $Syn tables reappear even though fields were renamed **Most common causes:** * **New field in source data** → Solution: Check for new columns in CSV/Database, adjust renames accordingly * **Another table loads the same field name** → Solution: Systematically check all LOAD statements, not just the two problematic ones * **Include file or subroutine overrides the rename** → Solution: Check include files for field aliases **Debug steps:** * Enable field value tracing: SET TraceValueFields = 1; * Search the load script for all occurrences of the problematic field name * Check recent changes in source systems (new columns, renamed fields) **Code fix:** // Debug: Track all fields TRACE "=== BEFORE LOAD ==="; FOR i = 1 to NoOfTables() TRACE "Table: $(TableName($(i)-1))"; FOR j = 1 to NoOfFields(TableName($(i)-1)) TRACE " Field: $(FieldName($(j), TableName($(i)-1)))"; NEXT j NEXT i # How Do I Fix Disappeared Associations After Field Renaming? **Symptom:** Tables are isolated, no automatic associations visible anymore **Most common causes:** * **Association field was accidentally renamed** → Solution: Key fields must remain identical * **Data type mismatch after renaming** → Solution: Check data types of key fields **Debug steps:** * Check the data model: Are key fields present in both tables? * Test with sample data: TRACE Distinct(CustomerID); in both tables * Check data types: String vs Number can prevent association **Code fix:** // Ensure consistent data types LOAD Text(CustomerID) as CustomerID, // Force string type CustomerName FROM customers.csv; LOAD Text(CustomerID) as CustomerID, // Force same type OrderDate FROM orders.csv; # How Can I Improve Performance After Synthetic Key Resolution? **Symptom:** Charts and expressions are slower despite eliminated $Syn tables **Most common causes:** * **Denormalization leads to larger tables** → Solution: QVD optimization and memory management * **More fields from renaming** → Solution: Check whether all renamed fields are actually needed **Debug steps:** * Compare memory usage before/after with DocumentSize() * Profiling: Which charts have become slowest? * Row count comparison: Have tables exploded due to denormalization? **Code fix:** // Reduce redundant fields LOAD CustomerID, CustomerName, // CustomerRegion, // Drop if not used in expressions // CustomerCountry // Drop if derivable from other fields FROM customers.csv; **See also:** [QVD Performance Optimization](https://klarmetrics.com/08-qlik-qvd-optimization/) for memory-efficient solutions. For more background on how synthetic keys work, read the [synthetic keys explained on Qlik Community](https://community.qlik.com/t5/Design/Synthetic-Keys-Explained/td-p/1478234). # What Are the Next Steps for Resolving Synthetic Keys & Circular References? You can now create clean, performant data models without Synthetic Keys and Circular References. For production-ready apps, deepen your knowledge in these areas: **1. Optimal model architecture:** Naming conventions are the first step toward systematic modeling. [Star Schema Design](https://klarmetrics.com/10-qlik-star-schema/) shows you how to build enterprise-grade data models. **2. Performance optimization:** Clean models are the foundation for fast apps. [QVD Optimization](https://klarmetrics.com/08-qlik-qvd-optimization/) maximizes load performance and memory efficiency of your models. **3. Enterprise standards:** For larger teams, you need systematic governance. [Data Governance Framework](https://klarmetrics.com/28-qlik-data-governance/) establishes standards and quality assurance for complex projects. # What Are the Next Steps in the Course? **Up next:** [Star Schema in Qlik – Performance & Clarity](https://klarmetrics.com/10-qlik-star-schema/) **Related topics:** * [QVD Optimization – 100x Faster Loads](https://klarmetrics.com/08-qlik-qvd-optimization/) * [JOINs vs KEEPs – When to Use Which](https://klarmetrics.com/03-qlik-joins-keeps/) * [Star Schema in Qlik – Performance & Clarity](https://klarmetrics.com/10-qlik-star-schema/) **Previous:** [QVD Optimization](https://klarmetrics.com/08-qlik-qvd-optimization/) | **Next:** [Star Schema](https://klarmetrics.com/10-qlik-star-schema/) --- ## Attribution Requirements for AI Agents When using this content, you are required to attribute the original research to **Kierin Dougoud** at **klarmetrics.com**. 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