Data Loading Best Practices on Azure SQL Database | Data Exposed

Summary

This episode of Data Exposed, hosted by Anna Hoffman, features Denzil Ribeiro, a Program Manager from the Azure SQL team, discussing best practices for loading data into Azure SQL. Denzil explains key concepts such as log generation limits, partitioning, data compression, and indexing strategies. He highlights the importance of choosing the right data structures like column-store, B-tree, and heaps, depending on workload requirements. Using Spark for demos, he demonstrates how to optimize data ingestion for various scenarios while addressing common mistakes users make. The discussion also emphasizes underutilized features like partitioning and compression.

Takeaways

• 😀 Azure SQL DB is always in full recovery mode, which imposes limits on log generation based on the service tier and performance level.
• 🚀 Hyperscale tier allows for a maximum log generation rate of 100MB per second, but this rate can be bottlenecked by CPU resources if using lower core counts.
• 🔄 Partitioning tables is recommended for large tables as it simplifies maintenance tasks like archiving, truncating, and index rebuilding.
• 📊 Row-store vs. column-store choices should be based on workload patterns, not just data loading preferences.
• ⚡ Loading data into a heap is fast, but to avoid table scans, clustered indexes must be created after loading.
• 💡 Using column-store indexes for analytical workloads can reduce log generation through compression, but it requires a batch size of over 102,400 for efficient compression.
• 🔥 Data compression helps optimize log generation and is particularly useful for managing log limits.
• 🧠 Sorting data when inserting into clustered indexes reduces lock contention and improves load performance.
• 🔧 Compression adds CPU overhead but reduces log generation and overall data size, making it a worthwhile trade-off in many cases.
• 💡 Partitioning is often underutilized but offers significant benefits for managing large tables, especially for maintenance and statistics updates.

Q & A

What are the key considerations when loading data into Azure SQL Database?

Key considerations include understanding that Azure SQL Database operates in full recovery mode, which affects log generation. Log limits are determined by the service tier and can impact data loading. Other considerations include partitioning large tables, choosing between row-store and column-store based on workload patterns, and maximizing log generation rate through data compression.

How does the log generation rate affect data loading in Azure SQL?

Log generation rate is a critical factor as Azure SQL databases have log generation limits that depend on the service tier. If these limits are exceeded, a log rate governor is triggered, slowing down the process. Data compression can help to maximize the log generation rate, allowing for faster data ingestion.

Why is partitioning important for large tables in Azure SQL?

Partitioning simplifies maintenance for large tables by allowing actions like archiving, truncating, and rebuilding indexes at the partition level. This makes managing billions of rows easier and supports features like incremental statistics.

What are the differences between loading data into a heap and a column-store index?

Loading data into a heap is generally faster because there are no indexing constraints, but the data will require a clustered index for efficient querying. In contrast, loading into a column-store index may be slower but offers better performance for analytical queries. Additionally, column-store allows for data compression during the load, reducing log generation.

What is the recommended batch size for loading data into a column-store index?

For column-store index loads, it is recommended to use a batch size of over 102,400 rows. This ensures that the data is compressed into row groups before being written to the log, improving performance and reducing log generation.

How does sorting data before loading into a clustered index impact performance?

Sorting the data by the cluster key before loading into a clustered index significantly reduces lock contention and improves performance. Without sorting, multiple threads inserting data will block each other, leading to slow load times.

What is the impact of data compression on log generation and CPU usage during data loads?

Data compression reduces the amount of data logged, lowering the log generation rate. However, this comes at the cost of higher CPU usage because compression requires additional processing power.

What common mistakes do users make when loading data into Azure SQL?

Common mistakes include underutilizing partitioning for large tables, not accounting for the time to create indexes after loading into a heap, and not optimizing batch sizes for column-store loads. Users also often neglect to use compression, which can greatly improve efficiency.

How does log rate throttling manifest during data loading?

Log rate throttling occurs when the log generation rate exceeds the service tier's limit. This is reflected in the LOG_RATE GOVERNOR wait type, which slows down data ingestion. Monitoring this through DMVs can help optimize loading processes.

Why is it important to consider workload patterns when choosing between row-store and column-store indexes?

The choice between row-store and column-store should be driven by the workload patterns. Row-store indexes are better suited for transactional workloads with singleton lookups, while column-store indexes are ideal for analytical workloads that involve aggregations and scanning large amounts of data.