Working with JSON and VARIANT in Snowflake: Parsing, Querying, and Performance Tuning

JSON is everywhere: API responses, event logs, clickstream data, and, it’s persisted in your data in Snowflake. But while its flexible nature makes it perfect for capturing real-world data, it can quickly become complex to manage and query. Thankfully, Snowflake has several built-in JSON functions that enable fast, efficient parsing and querying. 

Key Takeaways

• Embrace VARIANT for Effortless JSON Handling: Snowflake’s VARIANT data type enables schema-on-read flexibility, allowing you to ingest raw JSON via PARSE_JSON() without a rigid upfront schema, making it perfect for dynamic or evolving data sources like API logs.
• Prioritize Query Design for Optimal Performance: Performance hinges on targeted queries—avoid SELECT * on VARIANT columns to enable pruning, cluster tables on frequent filters (e.g., timestamps), and leverage search optimization for faster point lookups, potentially reducing costs by minimizing scanned data.
• Use FLATTEN Judiciously for Array Processing: Minimize LATERAL FLATTEN on large arrays to prevent exponential row growth and high compute usage; optimize by filtering early, using REDUCE for simple aggregations, specifying PATH for targeted extraction, and materializing results into reusable tables.
• Hybrid Approach for Flexibility and Efficiency: Store JSON as VARIANT during ingestion for adaptability, then extract key fields into structured columns or dynamic tables for analytics—queries on extracted data can be 40-45% faster than on VARIANT alone.
• Incorporate Error Handling and Monitoring: Opt for TRY_PARSE_JSON to gracefully manage invalid inputs, differentiate JSON null from SQL NULL to avoid logical errors, and regularly review query profiles for metrics like bytes scanned to fine-tune efficiency.

What Is JSON in Snowflake?

JSON (JavaScript Object Notation) is one of the most commonly used data formats in the world. Its flexibility allows engineers to use it in representing all kinds of data, including hierarchical, nested, and semi-structured data. However, that same flexibility can complicate traditional SQL workflows, especially those that expect fixed schemas. 

Snowflake solves this problem by using the VARIANT data type: a special column format designed for schema-on-read storage. Instead of forcing you to define a rigid schema upfront, Snowflake stores JSON, Avro, Parquet, or XML data in a self-describing structure that you can then query like you would regular relational data. This enables you to join, filter, and aggregate fields within your JSON without having to transform it first.

Some of the most useful JSON-related functions in Snowflake include: 

For example, if you execute the following query:

SELECT PARSE_JSON('{"user": "alice", "events": [1, 2, 3]}') AS json_data;

Snowflake will return a VARIANT object (not a plain string). This, then, will allow you to access nested attributes and combine semi-structured data with standard SQL analytics. 

How the VARIANT Type Works

The VARIANT data type is built to store heterogeneous, semi-structured data in a compressed binary format. Unlike fixed-schema tables, VARIANT allows each row to store a different data structure. This makes it perfect for unpredictable or evolving data sources (e.g., API logs, telemetry feeds). 

Size & NULL semantics (worth knowing up front): A single VARIANT value can be up to 128 MB of uncompressed data (the practical limit can be smaller due to internal overhead). Also, Snowflake distinguishes a missing value (SQL NULL) from a JSON null stored inside VARIANT. For example, PARSE_JSON(NULL) returns SQL NULL, while PARSE_JSON(‘null’) returns a VARIANT value containing JSON null. When navigating paths, a missing key typically evaluates to SQL NULL; an explicit JSON null can be detected with IS_NULL_VALUE(…).

Among VARIANT’s more powerful features is schema-on-read. Rather than enforcing a particular schema when loading data, Snowflake can automatically infer a given piece of data’s structure at the time of the query.

This flexibility enables you to query JSON objects directly using dot or bracket notation: 

SELECT json_data:user, json_data:events[0]
FROM my_table;

Snowflake automatically optimizes storage for frequently accessed paths, often allowing for effective pruning. However, for the best performance on large datasets, explicit extraction or clustering on key columns is recommended.

Parsing JSON in Snowflake Using PARSE_JSON()

When working with semi-structured data in Snowflake, use the PARSE_JSON() function to convert a JSON-formatted string into a VARIANT value, like so: 

PARSE_JSON( <expr> [ , '<parameter>' ] )

Here, <expr> is a string expression (e.g., VARCHAR) containing valid JSON. The optional second argument, ‘<parameter>’, controls how Snowflake handles duplicate keys in JSON objects:

• ‘s’ = strict (default): duplicate keys raise an error
• ‘d’ = allow duplicates: Snowflake keeps the last value for the duplicate key

Tip: PARSE_JSON supports input expressions up to 64 MB compressed, so if you’re parsing very large payload strings, validate sizes early (or parse during ingestion instead of repeatedly in SELECTs).

Some of the more common use cases of the PARSE_JSON() function include: 

• Ingesting raw JSON when loading logs or API data
• Validating JSON inputs, ensuring that strings conform to valid JSON formatting before querying
• Converting existing columns, such as VARCHAR fields, into queryable VARIANT columns

Alternatively, you could use the TRY_PARSE_JSON() function. It behaves much the same way as PARSE_JSON(), but will safely return NULL when the input is invalid. This can be especially helpful when working with data ingestion pipelines that often have improperly formatted records. 

Note: It’s usually a good idea to avoid parsing JSON repeatedly within the same query, as PARSE_JSON() is a resource-intensive function. Instead, parse once during ingestion, store the result as VARIANT, then query that VARIANT directly. This will help to optimize both speed and cost. 

Querying and Extracting JSON Fields

Once you’ve parsed your data as a VARIANT, Snowflake enables you to use JSON path notation to query and extract JSON fields from VARIANT columns. You just need to navigate nested objects and arrays using the colon (:) and bracket ([]) operators.

Here’s an example: 

SELECT  
  data:user:id AS user_id,
  data:events[0].type AS first_event
FROM json_table;

In this case, data:user:id accesses the id key nested under the user, while data:events[0].type retrieves the type of the first element in the events array. 

Remember: JSON is case-sensitive. data:User is not the same as data:user. Ensure your queries match the casing in your source data exactly.

Once you’ve retrieved the JSON value, you can cast it into a native SQL data type using a double-colon (::). For example: 

SELECT data:user:age::INT FROM json_table;

This command converts the JSON field “age” into an integer, which can then be used in aggregations or joins.

Snowflake also offers several helper functions for exploring and manipulating JSON:

Best Practices for JSON Performance & Cost Optimization

Optimizing JSON performance and cost in Snowflake requires thoughtful data design, precise querying, and efficient compute usage. Here are key best practices to follow:

• Parse on Ingestion. Users should use PARSE_JSON in their COPY INTO or INSERT statement so the data lands in the table as VARIANT, rather than storing it as VARCHAR and parsing it every time they run a SELECT query.
• Avoid SELECT * on VARIANT columns. Always target specific JSON paths in queries. Snowflake’s optimizer can prune unnecessary subpaths and micro-partitions when access paths are predictable. This drastically reduces both scan time and cost.
• Use FLATTEN sparingly. Flattening large arrays expands rows exponentially, consuming more compute. If possible, pre-aggregate or stage the data before flattening. For very large arrays, consider filtering or flattening incrementally in smaller steps.
• Cluster large JSON tables. Use clustering keys on common filters (e.g., timestamp, user_id) to improve pruning and limit the number of micro-partitions scanned. This helps Snowflake skip irrelevant data blocks during queries, reducing warehouse consumption.
• Cache and reuse parsed data. If you repeatedly extract the same JSON fields, materialize them in a transient table or a dynamic table with a defined refresh schedule. This avoids recomputation and speeds up downstream queries.
• Continuously monitor usage. Inspect “bytes scanned” and “partitions scanned” metrics in Query Profile for JSON-heavy queries. Large scan volumes or high pruning inefficiency usually indicate overly broad queries or suboptimal clustering.

Common Errors & Troubleshooting

Common issues when working with JSON in Snowflake typically involve malformed input, missing keys, data type mismatches, or inefficient queries — all of which can lead to parsing errors or performance slowdowns.

When to Use VARIANT vs Structured Tables

Generally speaking, it’s best to use VARIANT when you need flexibility and are ingesting raw or evolving data. But when you have predictable, performance-sensitive workloads, structured tables are a better option. 

But since many teams combine both (e.g., using VARIANT for ingestion and structured tables for optimized analytics), it’s worth comparing the two to see where their strengths and weaknesses lie.  

Final Thoughts on JSON in Snowflake

Snowflake’s native JSON capabilities make it easy to ingest, explore, and query semi-structured data. Indeed, by combining schema-on-read flexibility with SQL accessibility, Snowflake bridges the gap between structured and unstructured data processing.

However, overuse of SELECT *, frequent flattening, and unclustered large VARIANT tables can all inflate your compute costs. That’s why it’s important to combine JSON tools with cost optimization capabilities, like the kind that Keebo offers. 

With Keebo, you can set up warehouses to automatically upsize when resource-heavy JSON queries come in, then revert to a smaller size once the queries are done. Plus, Keebo’s query routing feature can take JSON-related queries and route them to warehouses with plenty of provisioned resources (so you don’t have to pay extra).

To see what all of this looks like in action, schedule a live Keebo demo today.