(sql_agent) ➜ sql_agent git:(master) ✗ llm -m gemini-2.0-flash-thinking-exp-1219 'I have a parquet file that looks like this (output of executing duckdb over it), what would be the best way to change the parquet file to make it as small as possible ✗ duckdb -c "select * from web_analytics.parquet limit 10"
┌────────────┬──────────────────────┬──────────┬─────────┬────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ timestamp │ session_id │ action │ version │ payload │
│ uint32 │ varchar │ varchar │ varchar │ varchar │
├────────────┼──────────────────────┼──────────┼─────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ 1735686000 │ ef234be7-4427-49cb… │ page_hit │ 1 │ {"user-agent":"Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; Googlebot/2.1; +http://www.google.com/bot.html) Chrome/131.0.6778.139 Safari/537.36","locale":"en-US","location":"… │
│ 1735686000 │ ef234be7-4427-49cb… │ page_hit │ 1 │ {"user-agent":"Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; Googlebot/2.1; +http://www.google.com/bot.html) Chrome/131.0.6778.139 Safari/537.36","locale":"en-US","location":"… │
│ 1732785995 │ ef234be7-4427-49cb… │ page_hit │ 1 │ {"user-agent":"Mozilla/5.0 (Linux; Android 6.0.1; Nexus 5X Build/MMB29P) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/131.0.6778.69 Mobile Safari/537.36 (compatible; Googlebot/2.1; +http://ww… │
│ 1735689600 │ ef234be7-4427-49cb… │ page_hit │ 1 │ {"user-agent":"Mozilla/5.0 (Linux; Android 6.0.1; Nexus 5X Build/MMB29P) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/131.0.6778.139 Mobile Safari/537.36 (compatible; Googlebot/2.1; +http://w… │
│ 1735689600 │ ef234be7-4427-49cb… │ page_hit │ 1 │ {"user-agent":"Mozilla/5.0 (Linux; Android 6.0.1; Nexus 5X Build/MMB29P) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/131.0.6778.139 Mobile Safari/537.36 (compatible; Googlebot/2.1; +http://w… │
│ 1735706599 │ c6d1942e-84f7-46de… │ page_hit │ 1 │ {"user-agent":"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/133.0.0.0 Safari/537.36","locale":"en-US","referrer":"","pathname":"/","href":"https://javisantana.… │
│ 1735711200 │ ef234be7-4427-49cb… │ page_hit │ 1 │ {"user-agent":"Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; Googlebot/2.1; +http://www.google.com/bot.html) Chrome/131.0.6778.139 Safari/537.36","locale":"en-US","location":"… │
│ 1735711200 │ ef234be7-4427-49cb… │ page_hit │ 1 │ {"user-agent":"Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; Googlebot/2.1; +http://www.google.com/bot.html) Chrome/131.0.6778.139 Safari/537.36","locale":"en-US","location":"… │
│ 1735712831 │ b0ea112f-5452-4c79… │ page_hit │ 1 │ {"user-agent":"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/133.0.0.0 Safari/537.36","locale":"en-US","location":"US","referrer":"","pathname":"/","h… │
│ 1735714800 │ ef234be7-4427-49cb… │ page_hit │ 1 │ {"user-agent":"Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; Googlebot/2.1; +http://www.google.com/bot.html) Chrome/131.0.6778.139 Safari/537.36","locale":"en-US","location":"… │
├────────────┴──────────────────────┴──────────┴─────────┴────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ 10 rows 5 columns │
└─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
'
You're looking to reduce the size of your Parquet file, which is a common and excellent goal! Let's break down the best ways to achieve that, focusing on your specific data structure and using tools you likely have available (like DuckDB).
Here's a step-by-step guide with explanations and code examples:
**1. Analyze Your Data and Identify Optimization Opportunities**
* **Payload Column is Key:** Looking at your sample data, the `payload` column is by far the largest and most complex. It's a JSON string. JSON is inherently verbose. This is the **primary target** for size reduction.
* **String Columns:** `session_id`, `action`, and `version` are also strings. While `action` and `version` might be categorical, `session_id` (UUIDs) and strings in `payload` can be large.
* **Timestamp:** `timestamp` is already a `uint32`, which is efficient.
**2. Strategies for Size Reduction (Prioritized)**
Here's a list of strategies, ordered roughly from most impactful to least, for your scenario:
* **A. Parse the `payload` JSON and Store as Structured Columns:** **This is the most effective strategy.** Instead of storing the JSON as a single string column, parse it and create individual columns for each field within the JSON.
* **Why it works:**
* **Columnar Compression Efficiency:** Parquet's columnar nature shines when data within a column is similar. If you have a column specifically for `user-agent`, Parquet can compress similar user-agent strings much more effectively than compressing them embedded within JSON strings.
* **Data Type Optimization:** You can use more efficient data types for individual fields. For example, if `locale` is always "en-US", "es-ES", etc., you could use a `dictionary` encoding or even an `enum` if the set of locales is very limited.
* **Reduced String Overhead:** JSON has structural overhead (braces, quotes, commas). Parsing it eliminates this overhead in the stored data.
* **How to do it with DuckDB:**
```sql
-- Create a new parquet file with parsed payload
COPY (
SELECT
timestamp,
session_id,
action,
version,
payload->>'user-agent' AS user_agent, -- Extract user-agent as a column
payload->>'locale' AS locale, -- Extract locale as a column
payload->>'location' AS location, -- Extract location as a column
payload->>'referrer' AS referrer, -- ... and so on for other fields you need
payload->>'pathname' AS pathname,
payload->>'href' AS href
-- Add other fields from your JSON payload here
FROM 'web_analytics.parquet'
) TO 'web_analytics_parsed.parquet' (FORMAT 'parquet');
-- Verify the new file and check its size
SELECT * FROM 'web_analytics_parsed.parquet' LIMIT 10;
-- Compare file sizes (use OS commands like `ls -lh`)
-- ls -lh web_analytics.parquet web_analytics_parsed.parquet
```
* **Considerations:**
* **Schema Evolution:** If the structure of your `payload` JSON changes frequently, parsing it might require schema updates in your Parquet file.
* **Querying Flexibility:** While parsing improves storage, if you frequently need to query the *entire* JSON blob as is, parsing might make those specific queries slightly less convenient (though still possible). However, for most analytical queries, querying individual fields is usually more common.
* **B. Choose the Right Compression Algorithm:** Parquet supports various compression algorithms.
* **Common Options (in order of generally increasing compression ratio, but also increasing CPU cost for compression/decompression):**
* `SNAPPY`: Fast, good balance between speed and compression. Often the default.
* `GZIP`: Better compression than SNAPPY, but slower.
* `ZSTD`: Excellent compression, often better than GZIP, and can be faster than GZIP at similar compression levels. Highly recommended for Parquet.
* `BROTLI`: Very high compression, but can be slower.
* **How to specify compression in DuckDB:**
```sql
-- Using ZSTD compression (highly recommended)
COPY 'web_analytics.parquet' TO 'web_analytics_zstd.parquet' (FORMAT 'parquet', COMPRESSION 'zstd');
-- Using GZIP compression
COPY 'web_analytics.parquet' TO 'web_analytics_gzip.parquet' (FORMAT 'parquet', COMPRESSION 'gzip');
-- (SNAPPY is often the default, so you might not need to specify)
COPY 'web_analytics.parquet' TO 'web_analytics_snappy.parquet' (FORMAT 'parquet', COMPRESSION 'snappy');
-- Compare file sizes
-- ls -lh web_analytics*.parquet
```
* **Recommendation:** **Start with `ZSTD`**. It often provides the best balance of compression and performance for Parquet. Experiment with `BROTLI` if you need the absolute smallest file size and are less concerned about compression/decompression speed.
* **C. Use Dictionary Encoding for Categorical Columns (`action`, `version`):** Parquet automatically applies dictionary encoding for string columns with low cardinality (few unique values). `action` and `version` seem like good candidates.
* **How it works:** Dictionary encoding creates a dictionary (mapping) of unique values in the column. Instead of storing the full string repeatedly, it stores an integer index pointing to the dictionary entry. This is very efficient for repeated strings.
* **DuckDB usually handles this automatically.** You generally don't need to explicitly configure dictionary encoding in DuckDB when writing Parquet. However, if you were using a lower-level Parquet library, you might have more explicit control.
* **Verify with Parquet Tools (optional):** You can use tools like `parquet-tools` (if you have them installed) to inspect the encoding used in your Parquet file.
* **D. Optimize Data Types (Minor, but good practice):**
* **`timestamp` (uint32):** Already good. `uint32` is sufficient for timestamps up to approximately 2106. If you need a wider range, consider `uint64` or `int64` (signed).
* **`version`:** If `version` is always "1" or a small set of categorical values, you could consider:
* **Integer if numeric versions:** If versions are numbers, store as `INT8`, `INT16`, etc., if appropriate.
* **Categorical String (Dictionary Encoding):** As discussed above, Parquet will likely dictionary-encode string versions.
* **Boolean if binary versions:** If it's just "version 1" vs. "other versions", a boolean column might be even more compact.
* **E. Consider Row Group Size (Advanced, Less Impactful for Small Files):** Parquet files are divided into row groups. Adjusting the row group size can sometimes slightly affect compression.
* **Larger row groups:** Can sometimes improve compression ratios, but might slightly increase memory usage during query processing.
* **Smaller row groups:** Can improve query performance for certain workloads (especially point lookups), but might slightly reduce compression.
* **DuckDB defaults are usually reasonable.** You generally don't need to tweak row group size unless you have very specific performance requirements.
**3. Iterative Approach and Testing**
The best approach is to be iterative:
1. **Start with parsing the `payload` (Strategy A):** This is likely to give you the biggest win.
2. **Then, experiment with different compression algorithms (Strategy B):** Try `ZSTD` first.
3. **Verify dictionary encoding (Strategy C):** Likely already happening, but good to be aware of.
4. **Review data types (Strategy D):** Ensure they are appropriate.
5. **(Optional) Experiment with row group size (Strategy E):** If the above steps are not enough, or for very large datasets, you can explore this.
**After each change, *measure the file size*!** Use OS commands like `ls -lh` or file explorer properties to see the reduction. This is the only way to know for sure which optimizations are most effective for your specific data.
**Example Workflow:**
1. **Parse `payload` and save as `web_analytics_parsed.parquet`**. Check size.
2. **Compress `web_analytics_parsed.parquet` with ZSTD to `web_analytics_parsed_zstd.parquet`**. Check size.
3. **Compare the sizes of `web_analytics.parquet`, `web_analytics_parsed.parquet`, and `web_analytics_parsed_zstd.parquet`.**
By following these steps, especially parsing the `payload` JSON and using `ZSTD` compression, you should be able to significantly reduce the size of your Parquet file.
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