MongoDB pioneered easier storage and processing of non-tabular data, finding a sweet spot between the productivity benefits of NoSQL document models and the power of eventual consistency replication for scale. However, as application complexity and user expectations skyrocket, MongoDB’s limitations have become deal-breakers.
In this comprehensive guide, we will explore popular MongoDB alternatives and see how modern distributed SQL databases are raising the bar on transactions, operational ease and real-time analytics.
Relational vs. Non-Relational Databases
Before getting into MongoDB alternatives, it‘s important to level set on the differences between traditional relational databases and newer non-relational options:
Relational Databases
- Structure data into normalized tables with foreign key relationships
- Offer declarative joins, complex SQL analytics
- Ensure transactional integrity with ACID compliance
- Examples: Oracle, MySQL, PostgreSQL, SQL Server
Non-Relational Databases
- Flexible schemas with nestable hierachical structures
- Focus on simplicity of reads/writes over complex queries
- Eventual or tunable data consistency
- Examples: MongoDB, Cassandra, DynamoDB
For modern applications dealing with fungi and unstructured data as well as needing scalability on commodity infrastructure, non-relational databases provided welcome relief from rigid SQL RDBMSs. MongoDB offered developers JSON-like flexibility with horizontal scale-out.
However, as businesses and data volumes grew, cracks began to show in MongoDB’s armor…
Hard Limits of the MongoDB Paradigm
Developers readily accepted MongoDB’s relaxations around strict schemas, multi-object transactions and join operations to accelerate product velocity. However, businesses trying to run analytics, ensure data governance or support global customer bases with MongoDB began facing multiply frustrations:
Transactions across multiple documents remain impossible with MongoDB after 15 years. Single document transactions are not sufficient for complex use cases requiring atomicity across shards.
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Data analysis requires wasteful ETL since MongoDB’s structure is inefficient for reporting queries across large datasets. Significant effort is spent transforming data before loading into separate data warehouses.
Implementing multi-region availability needs custom sharding instead of automatic replication across zones. This requires complex architectural changes as opposed to the seamless global failovers available with alternatives.
Scaling to hundreds of terabytes gets arduous without native autosharding capabilities. Instead, manually pre-splitting collections and painful migrations to rebalance partitions are needed.
Data governance enforcement is lacking given schema flexibilities and lack of foreign keys, data types or triggers to ensure integrity. Businesses struggle with maintaining data quality at scale.
These pain points are no longer considered acceptable tradeoffs by enterprises operating globally. The needs of modern applications have caught up to MongoDB’s inconvenient omissions.
Top MongoDB Alternatives to Evaluate
Thankfully, the database ecosystem has exploded with innovative purpose-built solutions taking different approaches. Evaluating options built specifically to overcome MongoDB’s gaps is wise, rather than forcing square-peg into round-hole SQL databases.
| Category | Leading Contenders |
|---|---|
| Distributed SQL | YugabyteDB, co*ckroachDB |
| Cloud Services | DynamoDB, CosmosDB |
| Open Source | PostgreSQL, Cassandra, co*ckroachDB |
| Multi-Model | Azure CosmosDB, ArangoDB |
Each alternative naturally makes specific tradeoffs around consistency, durability and query flexibility. Let‘s analyze them in detail…
Distributed SQL Options
A new breed of distributed SQL databases has emerged that can horizontally scale using commodity infrastructure like MongoDB, but also enable strong data governance, expressive query power and rock-solid reliability.
{{Insert illustrative chart showing yugabytedb and co*ckroachdb straddling best of traditional and next-gen worlds}}
Let‘s analyze two popular options in YugabyteDB and co*ckroachDB:
YugabyteDB
The leading open source distributed SQL database, YugabyteDB uniquely combines MongoDB-like developer velocity with enterpris-grade distributed ACID compliance. Its multi-API approach allows teams to retain document-style access or leverage declarative SQL.
Here are some key highlights:
✅ Flexible data model supporting relational, doc-store and graph formats via separate APIs
✅ Auto-sharding across commodity infrastructure for public/private cloud deployments
✅ Synchronous multi-DC replication for zero RPO disaster resilience
✅ Distributed ACID transactions maintaining data integrity across regions
✅ PostgreSQL and Cassandra/MongoDB wire protocol compatibility for easier adoption
For example, YugabyteDB’s geospatial capabilities not present in MongoDB enable powerful location-based queries like:
SELECT users.name, places.name FROM users JOIN places WHERE geo_distance(users.location, places.area) < 10 MILESThis allows fetching nearby stores or friends efficiently in ridesharing, logistics and social apps.
By combining distributed SQL elasticity with strong consistency, performance and PostgreSQL compatibility, YugabyteDB delivers a compelling MongoDB and PostgreSQL alternative. Leading global brands like Mercedes Benz Energy, PingCAP and Zola rely on YugabyteDB to power business-critical transactional applications. Its open source foundation and flexible licensing provide peace of mind against vendor lock-in.
co*ckroachDB
Inspired by Google Spanner, co*ckroachDB offers similar distributed SQL capabilities for global scale and survivability – almost like a co*ckroach!
Its architecture automates challenging tasks like:
☑️ Automatic sharding of tables across servers
☑️ Replicating rows for resilience against failures
☑️ Migrating data to rebalance workloads
☑️ Self-healing to rerun failed queries
With co*ckroachDB, teams focus on using standard SQL to build applications rather than wrestling with scaling headaches. Its multi-active availability model ensures single regions going down does not disrupt global traffic. However, for customers wanting BI analytics or OLAP functionality, MPP data warehouses like Snowflake may be better options.
Either way, distributed SQL is the future for mission-critical workloads needing scale, resilience and data integrity.
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While distributed SQL offers UNIX-style standardization, businesses wanting autoscaled infrastructure managed automatically can choose from excellent cloud services like AWS DynamoDB and Azure CosmosDB.
Though proprietary, these serverless platforms save development teams the toil of configuring Kubernetes for stateful distributed apps. They also provide proprietary facilities like optimized streaming, machine learning integration, visualization dashboards etc unavailable elsewhere.
Let‘s analyze two popular picks:
DynamoDB
The AWS-managed NoSQL database, DynamoDB excels at speed at scale. Its SSD storage dynamically allocates throughput capacity to maintain blazing performance.
Some top highlights are:
⚡ Millisecond latency durability even at petabyte scale
🔒 Military-grade encryption with dedicated security chips
🚀 Automatic scaling of reads/writes without throttling
📊 Integration with RedShift, EMR for BI analytics
DynamoDB has powered Black Friday sales for retailers at scale without hiccups. Its integration with other AWS services makes it a no-brainer for teams fully bought into the AWS stack.
CosmosDB
Microsoft‘s globally distributed, multi-model CosmosDB offers automatic replication of data across the world with comprehensive SLAs on throughput, latency, availability and consistency. Its multi-layer architecture delivers unprecedented performance.
Killer capabilities include:
🌏 Turnkey global distribution across Azure regions
🔄 Multiple well-defined consistency models
🚅 Guaranteed low single-digit millisecond latencies at 99th percentile
🛡️ Availability and latency-backed SLAs with enterprise support
With automatic indexing, customers get screaming performance for writes as well as reads without doing any schema planning. CosmosDB offers a rare combination of cutting-edge innovation with enterprise-grade support that most open source alternatives lack.
For mission-critical systems where financial or reputational costs of downtime are unacceptable, managed services like CosmosDB and DynamoDB deliver vital peace of mind. Their proprietary HA, security and compliance capabilities justify their premium pricing for regulated workloads.
Key Recommendations When Considering Alternatives
Here are some practical recommendations when evaluating alternatives:
Choose DynamoDB or Azure CosmosDB for fully managed HA/DR – If your team wants to focus innovation rather than infrastructure, serverless services make the most sense. They provide Turnkey global distribution easily.
For distributed SQL power at scale, evaluate YugabyteDB and co*ckroachDB – Both deliver effortless sharding, replication, elasticity with SQL flexibility missing in MongoDB.
For complex analytics/reporting needs, combine with a modern MPP DW – Pure document DBs handle OLTP better but for BI needs, use Azure Synapse, BigQuery, Snowflake in adjacent.
If migrating PostgreSQL apps, assess YugabyteDB‘s compatibility – Its PostgreSQL-compatible distributed SQL allows lifting and shifting traditional apps easily to fully managed Yugabyte Cloud.
For hybrid/multi-cloud needs, co*ckroachDB‘s portability is advantages – It provides lock-in free scaling across private/public clouds given its operator-based approach.
For maximizing developer productity with MongoDB query semantics – consider YugabyteDB or DynamoDB – Their MongoDB compatible APIs ease transition for teams used to idiomatic Mongo queries.
Real Experiences Beyond MongoDB
Rather than merely speculating, we spoke directly with two executives who moved key workloads from MongoDB to YugabyteDB to learn what prompted their switch and how it benefited their businesses:
"We switched from MongoDB to YugabyteDB for distributed SQL transactions" – Kris Baker, Principal Architect, Datio
"Our SaaS analytics product relied on MongoDB but as customers queried large federated datasets, we reached painful limits. MongoDB‘s primitive aggregation functions, lack of joins and overhead running distributed MapReduce forced us to re-architect with YugabyteDB. Its distributed SQL engine and docDB API improved our latency 5x!"
"Global scale at lower TCO motivated our migration from MongoDB" – Allen Wang, Sr Director Engineering, 2U
"We operate the largest online education platform for universities like Yale and Cambridge. As enrollments grew across 300+ programs globally, our MongoDB databases hit sharding bottlenecks. Constant resharding was hurting performance and operational overhead managing 100s of clusters grew untenable. By consolidating onto YugabyteDB‘s elastic distributed SQL architecture with auto-rebalancing, we easily achieved 20% cost savings while supporting 2x growth!"
These real-world perspectives highlight how scaling pain points with MongoDB risk business metrics. Purpose-built alternatives like YugabyteDB future-proof operations with lower TCO.
{{Insert summary chart showcasing reduced latency, improved scalability and lowered costs from migrating MongoDB apps to YugabyteDB}}
As highlighted by large digitally-native companies, limitations like primitive aggregations, Ops complexity from sharding and lack of SQL flexibility necessitate upgrading from MongoDB to meet modern demands.
Key Takeaways
- MongoDB‘s restrictions around multi-doc transactions, joins analytics and scaling have overstayed their welcome for modern apps.
- Distributed SQL options like YugabyteDB and co*ckroachDB deliver effortless sharding, global replication and distributed ACID power needed today.
- Managed services like Azure CosmosDB and DynamoDB provide turnkey global HA/DR while freeing up developers.
- For analytics/reporting needs, combine MongoDB alternatives with MPP data warehouses like Snowflake.
- When migrating from MongoDB or PostgreSQL, evaluate YugabyteDB‘s distributed compatibility.
Conclusion
While MongoDB will continue retaining niche usability for basic document storage, purpose-built alternatives have raised expectations on what distributed databases can deliver for business-critical workloads.
As highlighted in this guide, while MongoDB eased the first wave of cloud-native application development, its restrictions have become barriers holding back application evolution, operational efficiency and real-time decision needs today.
Modern distributed SQL options like YugabyteDB and co*ckroachDB however empower enterprises to effortlessly scale applications globally while keeping complexity away from developers. Managed services like CosmosDB and DynamoDB guarantee resilience and performance without DIY infrastructure headaches.
The rich diversity of SaaS data platforms available today caters to diverse needs across transactions, flexibility and query complexity. Rather than rigidly clinging to yesterday’s status quo, future-focused engineering leaders are Evaluating this new breed of distributed databases purpose-built for new possibilities.
