MySQL vs MongoDB
Choose MySQL when records are relational and money, stock or permissions must stay consistent across tables through InnoDB transactions. Choose MongoDB when documents differ in shape and write volume outgrows one server — its WiredTiger engine shards writes horizontally. Neither is faster in general; the data shape decides.
For developers and store owners in India choosing between a relational and a document database.
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- Read time
- 4 min
- Sections
- 7
- Updated
- 14 Nov 2026
- Written by
- WebJi® team
Last editorial update: 15 November 2026.
MySQL or MongoDB: which one should you choose?
Choose MySQL when records are relational and money, stock or permissions must stay consistent across tables; choose MongoDB when documents differ in shape and write volume outgrows a single server. Neither engine is faster in general — each is faster at the workload it was designed for, so the shape of your data makes the decision, not popularity.
- Take MySQL if you run billing, inventory, bookings or reporting that depends on joins and foreign keys.
- Take MongoDB if you store product catalogues with uneven attributes, event streams or content assembled per request.
- Either works for a small app: both engines are free to download and both run on a single server.
How do the two data models differ?
MySQL stores rows inside tables fixed by a schema you define before inserting anything; MongoDB stores BSON documents in collections where every document can carry its own fields. In MySQL a column that does not fit the table does not exist until you run ALTER TABLE. In MongoDB one product document can hold storage and camera_mp while its neighbour holds size and fabric.
That difference drives everything else. Relational tables normalise a fact into one place and join it back at query time; documents denormalise, keeping together what a page reads together. A catalogue page that would need three joined tables in SQL can be one fetched document in MongoDB, while a monthly tax report that is one SQL statement over clean tables is a multi-stage aggregation in the document world.
Do MySQL and MongoDB both support ACID transactions?
Yes — MySQL has provided full ACID through its default InnoDB engine for decades, and MongoDB has supported multi-document ACID transactions since version 4.0 on replica sets and 4.2 on sharded clusters. So the old advice that MongoDB cannot keep a checkout atomic is out of date.
Two practical details remain. First, MongoDB's transactions depend on WiredTiger, the default storage engine, which already gives every single-document write document-level concurrency control and atomicity — a well-embedded schema often needs no multi-document transaction at all. Second, MongoDB's own manual notes that distributed transactions cost more than plain writes, so teams design collections to avoid them. InnoDB, by contrast, wraps every ordinary statement in a transaction by default with row-level locking; you never opt in.
Transaction and storage-engine facts checked against the official MongoDB manual and MySQL documentation on 15 November 2026.
Where MySQL wins: joins, constraints and reporting
MySQL wins wherever one fact must stay true in several tables at once — a checkout that decrements stock, writes an order and records a payment belongs in one transaction with a foreign key stopping orphan rows. Constraints live in the database, not only in application code, so a buggy import cannot silently break the ledger.
It also wins on reporting. A finance query that joins orders, refunds and tax lines across four tables is one SQL statement; in MongoDB the same report is a pipeline with $lookup stages. If your team already writes SQL — and in India most college curricula and legacy ERP integrations do — that is a real saving every month. WooCommerce, Magento, WordPress and the payment-gateway plugins around them are all written against MySQL, so a store on WordPress hosting has already made this choice. For a plain explanation of what lives inside those tables, see how the WordPress database is organised.
Where MongoDB wins: uneven documents and write volume
MongoDB wins when two records in the same collection need different fields, or when sustained inserts outgrow what one machine can absorb. The catalogue case needs no sparse columns and no attribute-join tables; the write case is handled by a sharded cluster that spreads inserts across servers, while MySQL scales writes mainly by moving to a bigger box.
Event logs, IoT readings, chat history and activity feeds fit that pattern; a payments ledger usually does not, because it wants the constraint discipline of relational tables. The MEAN and MERN stacks standardised on MongoDB, so JavaScript teams get one document model from browser to database — the MongoDB hosting guide covers sizing a server for it, and the MEAN stack hosting walkthrough shows the full stack on one VPS.
MySQL vs MongoDB: specifications compared
The table below lines up the attributes that actually change a buying decision: model, joins, transactions, scaling, replication and licence. Where the vendors describe themselves differently, each row follows that vendor's own documentation.
| Attribute | MySQL | MongoDB |
|---|---|---|
| Data model | Tables and rows; fixed schema defined up front | Collections of BSON documents; flexible per document |
| Query language | SQL | MongoDB Query API (find, aggregation pipeline) |
| Joins | Native JOIN across indexed tables | $lookup stage inside an aggregation |
| Transactions | Full ACID via InnoDB, on by default | Single-document atomic always; multi-document ACID since 4.0 (replica set) and 4.2 (sharded) |
| Default storage engine | InnoDB, row-level locking | WiredTiger, document-level concurrency |
| Scaling writes | Vertical; read replicas for read scale | Horizontal through sharded clusters |
| High availability | Source-replica replication | Replica sets with automatic failover |
| Licence (community editions) | GPLv2 | SSPL |
Compiled from the official MySQL and MongoDB documentation; MongoDB pages checked 15 November 2026.
What does each database cost to host in India?
Both engines are free to download, so the real cost is the server: MySQL is included in WebJi® shared plans from ₹299 a month, while MongoDB needs a cloud server or VPS where you control the installation. Shared hosting does not let you run your own database daemon, so MongoDB starts one tier up.
| Option | Price | What you get |
|---|---|---|
| MySQL on Linux shared hosting (Launch) | ₹299 per month | 5 MySQL databases of 1 GB each, 1 domain, free SSL |
| Either engine on managed cloud (2 vCPU) | ₹5,900 per month | Managed cloud server you install and tune the database on |
| Either engine on managed cloud (4 / 8 vCPU) | ₹7,900 / ₹11,900 per month | Larger managed cloud tiers for bigger working sets |
| MongoDB Atlas free cluster | Free, 512 MB | A MongoDB, Inc. cloud tier for learning, not production |
WebJi® prices read from WebJi® billing on 15 November 2026; checkout confirms current charges.
For MySQL-backed sites, MySQL database hosting explains plan limits and phpMyAdmin access; for MongoDB, start with cloud hosting or a VPS and size RAM so the working set stays in memory. WebJi® rents and supports the server — it is not Oracle or MongoDB, Inc.
How hard is it to switch from one to the other?
Switching is a re-model and a rewrite, not a dump and restore: every table must be rethought as a collection and every query rewritten against the new API. Budget weeks for a live store, not hours.
- List every query the application runs and mark the ones joining more than two tables; they decide whether the move is sensible at all.
- Model new collections around how each page reads data, not around how the old tables were normalised.
- Run both databases in parallel and dual-write for a week, comparing row counts and totals nightly.
- Cut reads over behind a feature flag, keep writes on both, and watch error rates for 48 hours.
- Retire the old writes only after one full billing cycle matches to the rupee.
If the goal is only a faster or larger MySQL server, migrating the server — not the engine — is the cheaper path; repairing and optimising MySQL tables often removes the pressure that made a switch look attractive.
Frequently asked questions
Is MongoDB faster than MySQL?
Not in general. MongoDB inserts and updates large volumes of documents faster, while MySQL returns joined, indexed result sets faster. Measure your own query mix before believing any benchmark that declares a winner.
Can MySQL store JSON documents?
Yes. MySQL has a native JSON column type with functions to extract and index nested values. The table still needs a defined schema around it, so this suits occasional JSON fields rather than documents whose shape changes per record.
Which database does WordPress require?
MySQL (or its fork MariaDB). WordPress core, WooCommerce and nearly every plugin assume it, so a WordPress site has effectively already chosen MySQL. There is no supported MongoDB driver for WordPress.
Can I run MongoDB on shared hosting?
No. Shared hosting gives you MySQL databases the host manages, but not the root access needed to install and configure a database daemon. MongoDB belongs on a cloud server or VPS, where you choose the version and shard key.
Are both databases free for commercial use?
Both community editions download free of charge, but under different licences: MySQL Community is GPLv2 and MongoDB Community is SSPL. Running either behind your own website is normal; if you resell or bundle database access, have legal read both licences first.
Last editorial update: 15 November 2026. Prices follow the dated sources on this page; checkout confirms current charges.
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