CHALLENGES
MongoDB Atlas App Services vs. Couchbase Mobile
Top mobile development challenges with MongoDB Atlas App Services and how Couchbase Mobile solves them.
CHALLENGE #1
Flexible deployment options
MongoDB Realm does not allow data storage and sync for the data center of your choice.
CHALLENGE #2
Vector search at the edge
MongoDB Realm does not support vector search on-device.
CHALLENGE #3
Comprehensive platform support
MongoDB Realm offers limited support for non-mobile embedded device platforms.
CHALLENGE #4
Robust, secure, resilient data sync
MongoDB Realm’s mobile sync offering is limited and restrictive.
FEATURES
Key features of Couchbase Mobile vs. MongoDB Atlas App Services and Realm
Unlike MongoDB Atlas App Services and Realm, Couchbase Mobile offers hosted or self-managed deployments, SQL support, peer-to-peer sync, embedded device support, and customizable conflict resolution.
- What’s included
- Offline support
- Platform support
- Flexible topologies
- Peer-to-peer sync
- Delta sync
- Sync conflict resolution
- SQL++
- On-premises/self-managed deployment
- Vector search on-device
- Couchbase
- Mobile, IoT, and embedded device support
- Comprehensive, customizable
- MongoDB Atlas App Services/Realm
- Mobile platforms only
- Basic, no customization
CUSTOMERS
What customers are saying
-
“We need to have a robust supply chain as well as a good frontline sales application to be able to manage the orders and deliver products.”
Madhav Mekala, Director of Mobile App Development, PepsiCo30K users -
“With Capella, millions of players are able to have a consistent experience without any disruptions. These capabilities are critical for us.”
Lars Schmeller, Team Lead, Lotum800 million downloads worldwide10+ million monthly active users -
“The peer-to-peer synchronization in Couchbase Mobile enables patient data to be shared across the clinic seamlessly in real time.”
Lori Most, CEO, BackpackEMR20% reduction in charting costs25% reduction in manual processes
Code snippet
Couchbase Mobile supports SQL, which is more intuitive than MongoDB’s query language
SELECT SUM(value * volume) AS val, symbol
FROM db.stocks
WHERE symbol IN ( "AAPL", "GOOG" ) AND value > 0
GROUP BY symbol
ORDER BY val DESC, symbol ASC
db.stocks.aggregate([
{ "$match": {
"$and": [
{"symbol": {
"$in": [
"AAPL",
"GOOG"]}},
{ "value": {
"$gt": 0 }}]}},
{ "$group": {
"_id": {
"symbol": "$symbol" },
"sum(value * volume)": {
"$sum": {
"$multiply": [
"$value",
"$volume"]}}}},
{ "$project": {
"_id": 0,
"sum(value * volume)": "$sum(value * volume)",
"symbol": "$_id.symbol"}}
{ "$sort": {
"sum(value * volume)": -1,
"symbol": 1 }}]})