Scalable Bulk Writing to Databricks: write_df_to_delta

Why this is a game-changer for your workflow:

• Extreme Speed: write_df_to_delta() moves 1 million rows (42 MB) in 11 seconds—compared to 30 minutes using DBI::dbWriteTable().
• Proven at Scale: Successfully stress-tested up to 1 billion rows (41 GB). To move this volume of data via DBI::dbWriteTable() would take an estimated 20 days of continuous processing; write_df_to_delta() completes the task in under half an hour.
• Bypasses Payload Limits: Large files often cause REST API uploads to hang or fail. write_df_to_delta() automatically “chunks” your data, navigating the Databricks REST API limit of 5 GB per single file upload. As a result, you can upload gigabytes of data without a hitch.
• Network Resiliency: Moving massive files over the network is prone to transient “blips.” Our built-in auto-retry logic ensures that if a REST request fails, write_df_to_delta() waits and tries again until the job is done.
• Schema Safety: DBI::dbWriteTable() defaults to a 255-character limit for text, that is, it maps strings to Databricks SQL VARCHAR(255). write_df_to_delta() maps character strings to Databricks SQL STRING types, meaning your long text fields can be uploaded without errors.
• Precise Mapping: For advanced users, write_df_to_delta() allows for refined data type mapping (e.g., FLOAT, DECIMAL) via Arrow schemas.

Databricks Permissions

The utility interacts with three different layers of Databricks security. You will need:

• Catalog & Schema: USE CATALOG on the target catalog and USE SCHEMA on the target schema.
• Table Management: CREATE TABLE permissions on the target schema (required if overwrite_table = TRUE) or MODIFY and SELECT permissions on an existing table.
• Staging Volumes: READ VOLUME and WRITE VOLUME on the Unity Catalog Volume used for staging (specified in the volume_dir argument).

R Session Configuration

write_df_to_delta() uses the Databricks REST API for high-speed data transfer. For the API to authenticate, you must have the following variables defined in your .Renviron file:

• DATABRICKS_HOST: The URL of your workspace.
• DATABRICKS_TOKEN: Your personal access token (PAT).

Tip: Before you start, use check_databricks_odbc() to verify that your connection and environment variables are correctly configured.

What happens during execution?

While the function runs, you will see progress updates in the R console. Because of the auto-retry logic, if the function encounters a network “blip” during the upload to the Volume or during the final clean-up/deletion, it will automatically retry the operation, ensuring your R session remains stable and your Volume stays clean.

Tip: If you prefer a silent execution, you can wrap the function in suppressMessages() to hide the progress updates.

Precise Data Type Mapping (Arrow Schemas)

If you have specific requirements, such as ensuring a number is a DECIMAL(18,2) instead of a DOUBLE, you can pass an arrow::schema() directly into the function. This schema is applied during the Parquet conversion step.

library(arrow)

my_custom_schema <- schema(
  transaction_id = int64(),
  amount = decimal128(precision = 18, scale = 2)
)

write_df_to_delta(
  df = financial_data,
  target_table = "finance.audit.transactions",
  db_conn = con,
  volume_dir = "/Volumes/main/default/staging/",
  schema = my_custom_schema
)

Manual Chunking for Memory Management

By default, the function handles data in 5 GB chunks. This value is chosen to safely navigate the Databricks REST API limit while maintaining high throughput.

Adjusting Chunk Size: You can use the chunk_size argument to fine-tune performance based on your specific dataset:

• Decreasing the size (e.g., 1 GB): Use this if your local R session is running out of RAM e.g. during the Parquet conversion step.
• Increasing the size: If you have a very “sparse” dataset, Parquet compression will be extremely high. In this case, you could increase the chunk size to process more data per upload since the resulting Parquet file will be well under the 5 GB REST API limit.

write_df_to_delta(
  df = my_data,
  target_table = "catalog.schema.my_table",
  db_conn = con,
  volume_dir = "/Volumes/main/default/staging/",
  chunk_size = 1 * 1024^3  # 1 GB in bytes
)

The Stability vs. Speed Trade-off: Reducing the chunk size increases the total number of sequential operations performed by the function. This adds significant network overhead. We recommend sticking with the 5 GB default; the function will take longer to finish with smaller chunks.

Overwriting vs. Appending

By default, write_df_to_delta() will append data to an existing table. If you want to overwrite the existing table, you must set overwrite_table = TRUE.

Methodology

Tests were performed on the DfE High Memory Desktop (AVD: 137 GB RAM, 16 Cores). Benchmarks utilised a synthetic dataset comprising integers, numerics, characters, factors, logicals, Dates, and UTC timestamps. This ensures performance results account for the processing overhead associated with diverse SQL data types.

To ensure statistical reliability and account for fluctuations in network traffic or cluster load, we carried out 10 independent runs for each data volume (rows), \(n\), where \(n \in \{10^2, \dots, 10^6\}\). The benchmarks presented below show the median execution time, with the error bars representing the interquartile range (\(25^{th}\) to \(75^{th}\) percentile).

Key Results

As shown in the graph, for small datasets (< 1,000 rows), DBI::dbWriteTable() is competitive. However, once you exceed 10,000 rows, the overhead of SQL-based inserts becomes a massive bottleneck. At 1 million rows, while write_df_to_delta() finishes in roughly 11 seconds, the standard DBI approach takes nearly 30 minutes.

Figure 1: Performance comparison between DBI and dfeR across increasing row counts.

Recommendation: Choosing the Right Tool

Methodology

The stress tests were performed on the DfE High Memory Desktop (AVD). We used a synthetic dataset (comprising integers, numerics, characters, factors, logicals, Dates, and UTC timestamps) of up to 1 billion rows (~41 GB). We conducted 5 independent runs for each data volume (rows), \(n\), where \(n \in \{10^2,...,10^9\}\).

A 1-billion-row dataset represents the practical maximum scale for a single R session on AVD. While the AVD hardware is robust, a 10-billion-row dataset would exceed R’s in-memory capacity.

Note: Because these tests were conducted on a shared DfE cluster, execution times include the real-world impact of concurrent user activity and network contention.

The “Safety Net”: Resilience at Scale

When moving bulk data, the biggest risk is the network. Because the internal functions that handle these uploads are hidden from the user, it is important to understand the built-in “safety net” that ensures your data actually arrives:

• Patience for Large Payloads: We have configured a 10-minute (600s) “Transfer Safety” window for each chunk. This ensures that even if a 5GB payload is moving slowly across the network, it isn’t cut short prematurely.

• Automatic “Self-Healing” Retries: If an upload hangs or fails due to a network flicker, the tool triggers an internal recovery loop. It will automatically attempt to re-upload the specific failed chunk up to 5 times, waiting for an increasing amount of time (exponential backoff) between attempts.

Why this matters: In the benchmarks, you may notice variability in execution times. This usually indicates the tool detected a network issue, waited for the safety window, and successfully retried the upload automatically.

Key Results and Observations

The boxplot below captures both the speed and the stability of the transfer across eight orders of magnitude.

• Linear Scaling: write_df_to_delta() demonstrates consistent, predictable scaling even as it approaches the practical memory ceiling.

• Automated Chunk Management: The Databricks REST API has a strict 5 GB limit per file. write_df_to_delta() automatically calculated and managed the 41 GB upload in 9 sequential chunks. This removes the need for analysts to manually slice their data, ensuring each piece stays under the API limit while maintaining maximum throughput.

• Stability & Memory Efficiency: Despite the massive data volume, the R session maintained a stable ~5 GB overhead. By streaming data in chunks, the utility avoids “double-buffering” the data in RAM, which prevents the RStudio crashes that may happen during large-scale exports.

• Reliability Trade-off: Sequential chunking introduces a small amount of network overhead for each “slice.” While this makes the total execution time longer than a single (theoretical) massive upload, it is a necessary trade-off to ensure the transfer is fault-tolerant and bypasses REST API payload limits.

Figure 2: Performance resiliency testing from 100 to 1 billion rows, showing stable execution times.