Columnstore indexes have transformed high-volume analytics on SQL Server. They provide massive compression, aggressive segment elimination, vectorized batch-mode execution, and overall performance improvements that traditional rowstore indexes cannot match.
However, real production systems rarely run pure OLAP or pure OLTP. Most enterprise databases today run mixed workloads:
This creates a challenge:
How do we tune columnstore indexes without destroying OLTP speed or starving analytical queries?
This article provides a detailed, step-by-step methodology for tuning columnstore indexes specifically for mixed workloads. You get practical design patterns, anti-patterns, proven indexing strategies, and troubleshooting methods used in real production systems.
Understanding Rowstore Vs Columnstore Behavior
Before tuning, you must understand how SQL Server physically organizes data.
Rowstore (B-Tree)
Optimized for:
Columnstore
Optimized for:
Large scans
Aggregations
Filtering on few columns
Analytics and reporting
Batch-mode processing
Columnstore shines when you query millions of rows but touch only a few columns.
Why Mixed Workloads Are Hard For Columnstore
Columnstore indexes introduce:
OLTP workloads introduce:
These two worlds collide when we store both workloads in the same table.
How Columnstore Works Internally (Simplified)
Each column is stored independently inside rowgroups of roughly 1 million rows each.
+-----------------------------+
| Table |
+----------------+------------+
| Column A | Segment A |
| Column B | Segment B |
| Column C | Segment C |
+---------------------------------
Rowgroups:
Rowgroup 1: 1,048,576 rows
Rowgroup 2: 1,048,576 rows
Rowgroup 3: Delta Store (uncompressed)
Write Path (Insert)
Insert -> Delta Store (up to 1M rows)
-> compressed into Columnstore Rowgroup
Delete/Update Path
Update/Delete -> Delete Bitmap (not physically removed)
How Mixed Workloads Break Columnstore Performance
Common issues:
1. Too Many Rows In Delta Store
Heavy OLTP inserts never compress, causing scanning penalties.
2. Excessive Deleted Rows
Analytical scans slow down when metadata grows.
3. Too Many Small Rowgroups
Bad inserts lead to compressed rowgroups of only 20K–50K rows instead of 1M.
4. Too Many Rebuilds
Incorrect maintenance causes fragmentation and downtime.
5. OLTP Reads Slowed Down
Without proper secondary rowstore indexes, point-lookup performance collapses.
6. Locking And Latching Problems
Columnstore indexes can block writes if not sized or partitioned correctly.
When To Use Clustered Vs Nonclustered Columnstore Indexes
Choosing the correct type is key.
Clustered Columnstore Index (CCI)
Best when:
Table is mostly analytical
Inserts are large / batched
Updates are rare
Queries scan large ranges
Table is append-only
Not good for OLTP-heavy tables.
Nonclustered Columnstore Index (NCCI)
Best when:
You must keep OLTP speed
Table already uses clustered B-tree
You need reporting on live OLTP data
Point lookups must remain fast
This is the preferred option for mixed workloads.
Architecture Patterns For Mixed Workloads
Pattern 1: OLTP Base Table + Nonclustered Columnstore Index
Most recommended strategy.
Clustered Index (Primary Key)
|
+-- Nonclustered Columnstore Index (on analytical columns)
Pattern 2: Partitioned Table With CCI On Archive Partitions
Ideal for systems where most queries are on recent data.
Partition1 (Current Week) : Rowstore
Partition2 (Last Week) : Columnstore
Partition3 (Older Data) : Columnstore
Pattern 3: Two Tables (Hot + Cold)
Also known as Lambda architecture.
Hot Table (Rowstore) - For OLTP
Cold Table (Columnstore) - For reporting
Micro-batching ETLs move data from Hot to Cold.
How To Design Columnstore Indexes For Mixed Workloads
This section gives precise, battle-tested tuning rules.
Step 1: Choose the Right Columns
Do not blindly include all columns.
Include Only
Avoid
Example
CREATE NONCLUSTERED COLUMNSTORE INDEX NCCI_Sales
ON Sales (Amount, Quantity, ProductId, CustomerRegion, SalesDate);
Step 2: Control Rowgroup Quality
High-quality rowgroups = better segment elimination and better performance.
Rowgroup quality checker query:
SELECT
row_group_id,
total_rows,
deleted_rows
FROM sys.dm_db_column_store_row_group_physical_stats
WHERE object_id = OBJECT_ID('Sales');
Target Numbers
If you see:
then your workload is not columnstore-friendly.
Step 3: Use Minimal Secondary Rowstore Indexes
OLTP requires point lookups.
Add narrow rowstore indexes to support:
FK lookups
point queries
small range scans
Example:
CREATE INDEX IX_Sales_OrderId ON Sales(OrderId);
Do not create wide indexes.
Step 4: Partition For Better Compression And Maintenance
Partition by:
Date (most common)
Region
Customer Segment
Example partition scheme:
Current Day (Rowstore)
Last 7 Days (Rowstore + NCCI)
Older Partitions (CCI)
Step 5: Reduce Deletes And Updates
Columnstore hates row-by-row updates.
Rewrite OLTP patterns:
1. Replace Delete + Insert with Update
Avoid double work on delete bitmap.
2. Use Soft Deletes
Store IsDeleted flag instead of physical delete.
Step 6: Improve Batch Processing And Ingestion
Best ingestion method:
INSERT INTO Sales
SELECT * FROM Staging
ORDER BY SalesDate; -- better compression
Worst ingestion method:
Multi-row single inserts (OLTP style)
Step 7: Optimize Segment Elimination
Columnstore works best when filtering by ordered columns.
If your analytics use:
Then pre-sort loads by those columns.
Detailed Production Example
Assume a Sales table supporting:
10,000 transactions per second
Analytical reporting every 5 minutes
Dashboards hitting the live table
Table Structure
SalesId INT IDENTITY
ProductId INT
CustomerId INT
Region VARCHAR(10)
Amount DECIMAL(10,2)
Quantity INT
SalesDate DATETIME2
Step-by-Step Indexing Strategy
1. Create Clustered PK for OLTP Lookups
CREATE CLUSTERED INDEX CIX_Sales
ON Sales(SalesId);
2. Create Required OLTP Rowstore Indexes
CREATE INDEX IX_Sales_ProductId ON Sales(ProductId);
CREATE INDEX IX_Sales_CustomerId ON Sales(CustomerId);
3. Add Nonclustered Columnstore Index For Reporting
CREATE NONCLUSTERED COLUMNSTORE INDEX NCCI_Sales
ON Sales (Amount, Quantity, Region, SalesDate);
4. Partition Table By Month
CREATE PARTITION FUNCTION PF_SalesDate (datetime2)
AS RANGE RIGHT FOR VALUES ('2023-01-01', '2023-02-01', ...);
5. Load Data in Batches
INSERT INTO Sales (...)
SELECT ...
FROM Staging
ORDER BY SalesDate;
Execution Plan Improvements
Before NCCI
After NCCI
Query Flow Diagram With NCCI
+----------------------------+
OLTP Queries ----->| Clustered Rowstore CI |
+------------+---------------+
|
v
+-------+------+
| OLTP Point |
| Lookup Index |
+-------+------+
|
v
+----------+-----------+
Reports/Dashboards ->| Nonclustered |
| Columnstore Index |
+----------+-----------+
|
v
+------+-------+
| Batch Mode |
| Execution |
+--------------+
Troubleshooting Columnstore Problems In Production
Symptom 1: Columnstore Not Being Used
Cause: Missing predicates or wrong columns in NCCI.
Symptom 2: Small Rowgroups
Cause: Row-by-row OLTP inserts.
Fix: Batch load or use partition switching.
Symptom 3: High Fragmentation
Cause: Excessive deletes.
Fix: Periodic index REORGANIZE, not REBUILD.
Symptom 4: High Delta Store Rows
Cause: Too many trickle inserts.
Fix: Increase batch size or enable large ingestion pipelines.
Symptom 5: Slow OLTP Queries
Cause: Missing rowstore indexes after adding NCCI.
Fix: Add narrow indexes.
Maintenance Strategy For Mixed Workloads
1. Reorganize Columnstore Index
Do not rebuild daily.
ALTER INDEX NCCI_Sales
ON Sales
REORGANIZE WITH (COMPRESS_ALL_ROW_GROUPS = ON);
2. Periodically Purge Delete Bitmaps
ALTER INDEX NCCI_Sales ON Sales REBUILD PARTITION = 5;
3. Use Partition Switching For Archival
Move entire partitions with zero locking.
Best Practices Summary
For Mixed Workloads:
Prefer NCCI, not CCI
Partition by date
Maintain high-quality rowgroups
Avoid small inserts
Reorganize, do not rebuild
Preserve OLTP rowstore indexes
Filter analysts to use columnstore paths
Keep delete row percentages under 15 percent
Combine ingestion and reporting architecture
Monitor rowgroup metadata and delta stores
Final Summary
Columnstore indexes are extremely powerful, but only when implemented correctly for mixed workloads. You must balance OLTP responsiveness with analytical performance. This requires:
Choosing NCCI wisely
Partitioning data properly
Maintaining rowstore indexes
Ensuring high-quality rowgroups
Tuning ingestion patterns
Monitoring delta stores and delete bitmaps
When done right, columnstore indexes can reduce CPU usage by 80 percent, speed up analytical queries by 20–50x, and maintain OLTP stability at the same time.