Convert MySQL to Google BigQuery
A free online converter that translates MySQL queries into Google BigQuery. Paste your SQL, press Translate, and Query Studio rewrites the syntax that differs between the two databases — instantly, with no login and nothing stored.
Not a like-for-like move: MySQL is a transactional row store and BigQuery is a columnar analytical warehouse. The syntax converts, but indexes, primary keys and row-at-a-time updates have no counterpart, and the billing model makes SELECT * a cost decision rather than a style one.
MySQL → Google BigQuery example
Here is a real MySQL query and the Google BigQuery output Query Studio produces:
SELECT u.id, u.name, COUNT(o.id) AS orders
FROM users u
LEFT JOIN orders o ON o.user_id = u.id
WHERE u.created_at > '2024-01-01' AND u.active = 1
GROUP BY u.id, u.name
HAVING COUNT(o.id) > 3
ORDER BY orders DESC
LIMIT 10;SELECT
u.id,
u.name,
COUNT(o.id) AS orders
FROM
users u
LEFT JOIN orders o ON o.user_id = u.id
WHERE
u.created_at > '2024-01-01'
AND u.active = 1
GROUP BY
u.id,
u.name
HAVING
COUNT(o.id) > 3
ORDER BY
orders DESC
LIMIT
10;What changes from MySQL to Google BigQuery
- Identifier quoting is rewritten from backtick `identifiers` to double-quoted "identifiers".
- Null-handling functions such as IFNULL() are mapped to COALESCE().
- The result is re-indented and keyword-cased in Google BigQuery style so it's ready to paste and run.
MySQL to Google BigQuery data type mapping
Query Studio translates queries, not schemas. When you come to move the tables themselves, this is what changes between MySQL and Google BigQuery:
| MySQL | Google BigQuery | Watch out for |
|---|---|---|
| INT / BIGINT | INT64 | BigQuery has one integer type. |
| DOUBLE / FLOAT | FLOAT64 | Direct equivalent. |
| DECIMAL(p,s) | NUMERIC / BIGNUMERIC | Direct equivalent. |
| VARCHAR(n) / TEXT | STRING | No length limits, and no length enforcement. |
| TINYINT(1) | BOOL | Direct equivalent. |
| DATETIME | DATETIME or TIMESTAMP | BigQuery distinguishes them strictly: TIMESTAMP is an absolute instant, DATETIME is a wall-clock reading with no zone. |
| BLOB | BYTES | Direct equivalent. |
| JSON | JSON | Direct equivalent. |
| AUTO_INCREMENT | No equivalent | BigQuery is analytical and has no sequences. Generate keys upstream, or use GENERATE_UUID(). |
MySQL to Google BigQuery: what actually catches people out
Syntax is the part a translator can fix. These are the differences that survive a clean conversion and show up later as wrong results rather than as errors.
BigQuery is analytical, and the cost model changes how you write queries
You are billed by bytes scanned, so SELECT * on a wide table is not merely untidy — it is the difference between a free query and an expensive one. Column pruning and partition filters matter far more than index design, because there are no indexes to design. A query that was fine in MySQL can be genuinely costly here without being slow.
There are no indexes, no primary keys and no foreign keys
BigQuery enforces none of them. Performance comes from partitioning (usually by date) and clustering (by the columns you filter on), which are declared on the table rather than created alongside it. Uniqueness has to be maintained by the pipeline, not the database.
Backticks mean something different here
MySQL uses backticks to quote a column. BigQuery uses them to quote a fully-qualified table path — `project.dataset.table`. The character is the same and the meaning is not, so a mechanical find-and-replace produces valid-looking SQL that references the wrong things.
SELECT `name` FROM `users`;SELECT name FROM `my-project.my_dataset.users`;UPDATE and DELETE exist, but are not for row-at-a-time work
BigQuery supports DML, but it is designed around large batch operations and has quotas on how frequently a table can be modified. Application code that updates single rows on every request needs a different design, not a translated statement.
Standard SQL only
BigQuery's Legacy SQL used different syntax entirely. Everything here targets Standard SQL, which is the default and the only one worth writing new queries in.
What this converter will not do
Query Studio translates SQL syntax. Being honest about the boundary is more useful than claiming there isn’t one — and on MySQL → Google BigQuery specifically, these are the three that matter most:
Performance characteristics
A converted query is correct, not fast. Index strategy, partitioning, distribution keys and statistics differ per engine, and a query that was well-tuned for the source is merely valid on the target. Run the Analyze and Optimize tabs on the output.
Vendor-specific extensions
PostGIS geometry, MySQL spatial functions, SQL Server's FOR XML and hierarchyid, BigQuery's nested/repeated model, Snowflake's time travel. Where there is no equivalent concept, there is no translation — only a redesign.
Stored procedures, functions and triggers
Procedural code — PL/pgSQL, T-SQL procedures, MySQL routines — is a different language in every engine, with different control flow, error handling, variable declaration and transaction semantics. Query Studio translates queries, not programs. These have to be ported by hand.
The full list of what a syntax translator cannot do is on the Query Studio page.
How to convert MySQL to Google BigQuery
- Open the Query Studio editor and choose MySQL as the “From” dialect.
- Choose Google BigQuery as the “To” dialect.
- Paste your MySQL query and press Translate — copy the Google BigQuery result.
Try it with your own query
The editor is preloaded with MySQL → Google BigQuery. You can also explain, format, validate and analyze the result.
Convert MySQL to Google BigQuery now →Working with the data rather than the schema? Open a large CSV, JSON or Parquet file and query it with SQL — no upload, no row limit, and files far past what Excel will open.
MySQL to Google BigQuery FAQ
Is this MySQL to Google BigQuery converter free?
Yes — it's completely free with no account, no sign-up and no usage limits. Your query is processed to return the result and never stored.
Is the MySQL to Google BigQuery conversion accurate?
Query Studio rewrites syntax deterministically using real SQL parsers, so it gives the same result every time — there is no AI involved and no variation between runs. It handles the differences listed above automatically. What it cannot do is anything semantic: stored procedures, triggers, vendor extensions and performance characteristics all need a human. Review complex, vendor-specific queries before running them in production.
What breaks when migrating from MySQL to Google BigQuery?
The differences that most often cause problems on this pair: BigQuery is analytical, and the cost model changes how you write queries; There are no indexes, no primary keys and no foreign keys; Backticks mean something different here; UPDATE and DELETE exist, but are not for row-at-a-time work; Standard SQL only. Each is explained in full above, with before-and-after examples where seeing it is quicker than reading about it.
How do MySQL data types map to Google BigQuery?
The full mapping table is above and covers 9 types. The ones that are not a straight rename: INT / BIGINT → INT64, VARCHAR(n) / TEXT → STRING, DATETIME → DATETIME or TIMESTAMP, AUTO_INCREMENT → No equivalent. Note that Query Studio translates queries rather than schemas — the table is there to tell you what your CREATE TABLE statements need, not to rewrite them for you.
Does this MySQL to Google BigQuery converter use AI?
No. Every result is computed by real SQL parsers and rule engines, which is what makes it free, instant, unlimited and identical on every run. Nothing is sent to a model, so there are no rate limits, no per-request cost to pass on, and no possibility of a confidently wrong answer that looks plausible.