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Amazon DynamoDB vs InfluxDB: What are the differences?
Key Differences between Amazon DynamoDB and InfluxDB
Data Model and Query Language: The most significant difference between Amazon DynamoDB and InfluxDB lies in their data model and query languages. DynamoDB is a NoSQL database that uses a key-value store approach, allowing efficient retrieval of data based on a primary key. In contrast, InfluxDB is a time-series database specifically designed for handling timestamped data, making it more suitable for storing and querying time-series data efficiently.
Scalability and Performance: Both DynamoDB and InfluxDB offer horizontal scalability, but DynamoDB has the advantage of auto-scaling, where it automatically adjusts capacity based on usage patterns. InfluxDB also supports horizontal scalability but requires manual configuration of clusters. In terms of performance, DynamoDB is optimized for low-latency read and write operations, while InfluxDB excels in handling high influx rates and aggregating data efficiently.
Indexing and Querying: DynamoDB primarily utilizes primary key-based querying for retrieving data, with support for secondary indexes. In contrast, InfluxDB employs a unique inverted index structure known as the time series index, enabling efficient queries on time ranges and fields. This indexing approach in InfluxDB allows complex time-series analytics, including aggregations and downsampling.
Data Retention and Eviction Policies: In DynamoDB, data retention is managed by specifying the time-to-live (TTL) attribute on individual items, allowing automatic deletion of expired data. InfluxDB, being a time-series database, provides native support for data retention policies based on time intervals. This allows automatic removal of older data based on predefined time durations or maximum data sizes.
Data Writes and Consistency: DynamoDB ensures strong consistency for read and write operations by default, resulting in higher latency. InfluxDB, on the other hand, provides eventual consistency, prioritizing high throughput and lower latency for write operations. This makes InfluxDB a more suitable choice for scenarios where real-time data ingestion and fast writes are crucial, such as IoT applications.
Community and Ecosystem: Amazon DynamoDB is a fully managed cloud service provided by Amazon Web Services (AWS), offering excellent integration with other AWS services, extensive documentation, and a large user community. InfluxDB, as an open-source database, has an active community and a growing ecosystem. It also provides integration with various data visualization and monitoring tools like Grafana and Telegraf.
In summary, the key differences between Amazon DynamoDB and InfluxDB can be attributed to their data models, query languages, scalability, indexing approaches, data retention policies, and consistency models. Ultimately, the choice depends on specific requirements, with DynamoDB being suitable for general-purpose NoSQL use cases and InfluxDB tailored for time-series data analysis and storage.
I have a lot of data that's currently sitting in a MariaDB database, a lot of tables that weigh 200gb with indexes. Most of the large tables have a date column which is always filtered, but there are usually 4-6 additional columns that are filtered and used for statistics. I'm trying to figure out the best tool for storing and analyzing large amounts of data. Preferably self-hosted or a cheap solution. The current problem I'm running into is speed. Even with pretty good indexes, if I'm trying to load a large dataset, it's pretty slow.
Druid Could be an amazing solution for your use case, My understanding, and the assumption is you are looking to export your data from MariaDB for Analytical workload. It can be used for time series database as well as a data warehouse and can be scaled horizontally once your data increases. It's pretty easy to set up on any environment (Cloud, Kubernetes, or Self-hosted nix system). Some important features which make it a perfect solution for your use case. 1. It can do streaming ingestion (Kafka, Kinesis) as well as batch ingestion (Files from Local & Cloud Storage or Databases like MySQL, Postgres). In your case MariaDB (which has the same drivers to MySQL) 2. Columnar Database, So you can query just the fields which are required, and that runs your query faster automatically. 3. Druid intelligently partitions data based on time and time-based queries are significantly faster than traditional databases. 4. Scale up or down by just adding or removing servers, and Druid automatically rebalances. Fault-tolerant architecture routes around server failures 5. Gives ana amazing centralized UI to manage data sources, query, tasks.
We are building a social media app, where users will post images, like their post, and make friends based on their interest. We are currently using Cloud Firestore and Firebase Realtime Database. We are looking for another database like Amazon DynamoDB; how much this decision can be efficient in terms of pricing and overhead?
Hi, Akash,
I wouldn't make this decision without lots more information. Cloud Firestore has a much richer metamodel (document-oriented) than Dynamo (key-value), and Dynamo seems to be particularly restrictive. That is why it is so fast. There are many needs in most applications to get lightning access to the members of a set, one set at a time. Dynamo DB is a great choice. But, social media applications generally need to be able to make long traverses across a graph. While you can make almost any metamodel act like another one, with your own custom layers on top of it, or just by writing a lot more code, it's a long way around to do that with simple key-value sets. It's hard enough to traverse across networks of collections in a document-oriented database. So, if you are moving, I think a graph-oriented database like Amazon Neptune, or, if you might want built-in reasoning, Allegro or Ontotext, would take the least programming, which is where the most cost and bugs can be avoided. Also, managed systems are also less costly in terms of people's time and system errors. It's easier to measure the costs of managed systems, so they are often seen as more costly.
We are building an IOT service with heavy write throughput and fewer reads (we need downsampling records). We prefer to have good reliability when comes to data and prefer to have data retention based on policies.
So, we are looking for what is the best underlying DB for ingesting a lot of data and do queries easily
We had a similar challenge. We started with DynamoDB, Timescale, and even InfluxDB and Mongo - to eventually settle with PostgreSQL. Assuming the inbound data pipeline in queued (for example, Kinesis/Kafka -> S3 -> and some Lambda functions), PostgreSQL gave us a We had a similar challenge. We started with DynamoDB, Timescale and even InfluxDB and Mongo - to eventually settle with PostgreSQL. Assuming the inbound data pipeline in queued (for example, Kinesis/Kafka -> S3 -> and some Lambda functions), PostgreSQL gave us better performance by far.
Druid is amazing for this use case and is a cloud-native solution that can be deployed on any cloud infrastructure or on Kubernetes. - Easy to scale horizontally - Column Oriented Database - SQL to query data - Streaming and Batch Ingestion - Native search indexes It has feature to work as TimeSeriesDB, Datawarehouse, and has Time-optimized partitioning.
if you want to find a serverless solution with capability of a lot of storage and SQL kind of capability then google bigquery is the best solution for that.
I chose TimescaleDB because to be the backend system of our production monitoring system. We needed to be able to keep track of multiple high cardinality dimensions.
The drawbacks of this decision are our monitoring system is a bit more ad hoc than it used to (New Relic Insights)
We are combining this with Grafana for display and Telegraf for data collection
Pros of Amazon DynamoDB
- Predictable performance and cost62
- Scalable56
- Native JSON Support35
- AWS Free Tier21
- Fast7
- No sql3
- To store data3
- Serverless2
- No Stored procedures is GOOD2
- ORM with DynamoDBMapper1
- Elastic Scalability using on-demand mode1
- Elastic Scalability using autoscaling1
- DynamoDB Stream1
Pros of InfluxDB
- Time-series data analysis58
- Easy setup, no dependencies30
- Fast, scalable & open source24
- Open source21
- Real-time analytics20
- Continuous Query support6
- Easy Query Language5
- HTTP API4
- Out-of-the-box, automatic Retention Policy4
- Offers Enterprise version1
- Free Open Source version1
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Cons of Amazon DynamoDB
- Only sequential access for paginate data4
- Scaling1
- Document Limit Size1
Cons of InfluxDB
- Instability4
- Proprietary query language1
- HA or Clustering is only in paid version1