BigQuery queries can be surprisingly fast because it’s a columnar store that parallelizes computation across thousands of machines, but you’re still paying for every byte it reads.
Let’s see how a simple query actually runs. Imagine you have a table my_project.my_dataset.my_table with billions of rows and columns like timestamp, user_id, event_type, and payload. You want to count distinct users who performed a specific event on a given day.
SELECT
COUNT(DISTINCT user_id)
FROM
`my_project.my_dataset.my_table`
WHERE
event_type = 'login'
AND DATE(timestamp) = '2023-10-27'
BigQuery doesn’t scan the entire table. It first identifies which columns are needed: user_id, event_type, and timestamp. It then reads only those columns from disk. For the WHERE clause, it filters rows where event_type is 'login' and the date part of timestamp matches '2023-10-27'. Finally, it computes the COUNT(DISTINCT user_id) on the filtered subset. This is already a massive optimization over row-based systems.
But how do we make it even faster?
1. Select Only Necessary Columns
This is the golden rule. BigQuery charges based on data scanned. Even if you only COUNT(*), if you don’t specify columns, it might still scan more than it needs.
Diagnosis: Run bq query --nouse_legacy_sql --format=prettyjson 'SELECT COUNT(*) FROM \my_project.my_dataset.my_table`'and check the "Bytes processed" in the job details. Then runbq query --nouse_legacy_sql --format=prettyjson 'SELECT COUNT(user_id) FROM `my_project.my_dataset.my_table`'` and compare.
Fix: Always use SELECT col1, col2, ... instead of SELECT *. For the example above, we’re already selecting specific columns, which is good.
Why it works: Reduces the amount of data BigQuery must read from storage.
2. Filter Early and Often
The WHERE clause is your best friend. Apply filters as early as possible in the query.
Diagnosis: Examine the query plan for stages that process large amounts of data before filtering.
Fix: Ensure your WHERE clauses are applied to the most granular data possible. For example, if you have a date partition, filter on that partition column directly. Instead of WHERE DATE(timestamp) = '2023-10-27', if timestamp is stored in a date-partitioned column named event_date, use WHERE event_date = '2023-10-27'.
Why it works: Reduces the number of rows that subsequent operations (like joins or aggregations) need to process.
3. Leverage Partitioning and Clustering
BigQuery’s partitioning and clustering are crucial for performance. Partitioning divides a table into segments based on a date/timestamp column or an integer range. Clustering sorts data within partitions based on specified columns.
Diagnosis: Check your table schema in the BigQuery UI. Look for PARTITION BY and CLUSTER BY clauses.
Fix: If your table isn’t partitioned, add partitioning, especially on date/timestamp columns used in WHERE clauses. If it is partitioned, ensure your queries filter on the partitioning column. If you frequently filter or group by certain columns (e.g., user_id, event_type), cluster by them.
Example Partitioning: CREATE TABLE my_project.my_dataset.my_table ( ... ) PARTITION BY DATE(timestamp);
Example Clustering: CREATE TABLE my_project.my_dataset.my_table ( ... ) CLUSTER BY user_id, event_type;
Why it works: Partitioning allows BigQuery to skip entire partitions that don’t match the filter. Clustering co-locates similar data, making scans and aggregations on clustered columns much faster.
4. Optimize JOIN Operations
Large joins can be expensive. BigQuery optimizes JOINs by broadcasting smaller tables to all workers processing the larger table.
Diagnosis: Look for stages in the query plan where data is shuffled between workers, often indicated by high shuffle read/write bytes.
Fix: Ensure the "smaller" table in a JOIN is actually smaller. If possible, filter the larger table before the join. Try to join on columns that are well-suited for distribution (e.g., low cardinality, or clustered). If one table is significantly smaller than the other, BigQuery will likely broadcast it. You can hint this with SELECT ... FROM large_table JOIN (SELECT ... FROM small_table) ON ... but BigQuery’s optimizer is usually smart enough.
Why it works: Efficient broadcasting minimizes data movement across the network and reduces the computational burden on individual workers.
5. Use APPROX_COUNT_DISTINCT When Precision Isn’t Critical
Calculating exact distinct counts over massive datasets can be computationally intensive.
Diagnosis: Compare the execution time and cost of COUNT(DISTINCT col) versus APPROX_COUNT_DISTINCT(col).
Fix: Replace COUNT(DISTINCT user_id) with APPROX_COUNT_DISTINCT(user_id) if a small margin of error is acceptable.
Why it works: Uses a probabilistic algorithm (HyperLogLog++) that requires significantly less memory and processing power than exact distinct counting.
6. Avoid ORDER BY on Large Datasets
ORDER BY requires BigQuery to sort the entire result set, which can be very costly, especially if the result set is large.
Diagnosis: Observe the "Shuffle output" stage in the query plan. High bytes processed here often indicates an ORDER BY on a large intermediate result.
Fix: Only use ORDER BY when absolutely necessary, and apply it to the smallest possible dataset. If you need the top N rows, use LIMIT with ORDER BY. SELECT ... FROM ... ORDER BY some_col DESC LIMIT 100.
Why it works: LIMIT combined with ORDER BY allows BigQuery to stop processing once it has found the top N results, rather than sorting everything.
7. Use ARRAY_AGG for Denormalization
If you find yourself joining small tables repeatedly, consider denormalizing your data.
Diagnosis: Observe frequent joins to lookup tables or dimension tables in your query plan.
Fix: Use ARRAY_AGG to create nested and repeated fields within your main table. This avoids the need for joins at query time. For example, instead of joining a users table to get user details, you might pre-process and store an array of user details within each event record.
Why it works: Reduces the number of tables to scan and joins to perform, effectively "flattening" the query.
8. Materialize Intermediate Results (CTEs/Views)
For complex queries with repeated subqueries or common intermediate steps, materialize these results.
Diagnosis: Identify repeated subqueries or common data transformations in your SQL.
Fix: Use Common Table Expressions (CTEs) or create materialized views. CTEs (WITH ... AS (...)) can help organize queries and sometimes allow BigQuery to optimize better. Materialized views are pre-computed results of a query that BigQuery can automatically use.
Example CTE:
WITH
filtered_events AS (
SELECT user_id, timestamp
FROM `my_project.my_dataset.my_table`
WHERE event_type = 'login' AND DATE(timestamp) = '2023-10-27'
)
SELECT COUNT(DISTINCT user_id) FROM filtered_events;
Why it works: CTEs can improve readability and sometimes guide the optimizer. Materialized views pre-compute and store results, making subsequent queries that use them much faster as they read from the pre-computed data.
The next common issue you’ll encounter is "Query exceeded the maximum allowed execution time" for very large tables.