How can you design data models for high concurrency and low latency?

When dealing with high concurrency and needing low latency in your data models, it's crucial to balance various factors. First, understand what data and queries are most commonly used to target your optimizations better. Consider normalizing data to avoid duplications and reduce contention, but also think about when denormalization might speed up read-heavy operations. Your schema and indexes should be designed to reduce bottlenecks and improve speed. Employ caching to store frequently accessed data, cutting down on query time. Also, look into sharding your data across multiple servers to enhance scalability and performance.

Understanding the data sources, data types, volume of the data that is being inserted/modified/deleted, how fast data should be loaded, query pattern(s) by the end users, business requirement(s) changes, existing functional constraints and data store(s) are key to design data models for high concurrency and low latency. The above criteria helped me in designing data frameworks and pipelines for a Hadoop big data project. I picked tools addressing specific use cases within the Hadoop ecosystem and based on the low latency requirements. Another project which I worked on using the Oracle database required faster access of seldom changing aggregated data. I created materialized views which gets executed once a day to address this problem.

Data is interestingly fluid and even the most static data is hardly static. When thinking about your data, it is important to understand where it is being sourced from or being generated from as the reliability of your stack is as good as the reliability of the producer of the data. Understand whether the producer will meet the needs or if you need to mitigate it. The other aspect that is critical is understanding the authentication model for your data upfront. Depending on the type of data the authentication may need to be applied at a per user level, per data element level or combination of both. This has an impact on how you may make queries fast if your data session is not persistent as designed.

Understanding the data and recognizing its value is the most crucial aspect of the ingestion process. At Bosch, especially when working with automotive and manufacturing data, idealy we focus around crafting a secure pipeline for the aggregation, ingestion, and storage of data originating from numerous sensors. Ensuring the data's integrity and velocity. My approach consistently centers on understanding the 5Vs (Velocity, Volume, Value, Variety, and Veracity) to make informed choices regarding the optimal file formats, suitable frameworks, data store and the most appropriate programming languages.

In my experience, understanding data and queries relies on collaborative sessions with key stakeholders: ???????? ??????????????????/????????????????????. Who oversee the model and pipelines. ???????????????? ????????????????????????. They're essential in identifying critical use cases, response expectations, and data retention needs. ???????? ????????????????. Experts in optimizing data organization for efficient analysis.

I worked on a POC where I had to select a data store in the Cloudera Hadoop distributed file system to store and retrieve time series data. There are different data stores like Hive, HBase, Druid, Kudu ..etc. to choose from the ecosystem. The users should be able to run queries with aggregations against billion rows and retrieve data with sub-second latency. After ingesting data into different data stores and benchmarking the query response times, I proceeded with Druid for this use case. Druid is a column-oriented, open-source, distributed data store. It is designed to quickly ingest massive quantities of time series data, and provide low-latency queries on top of the data.

Data stores always have tradeoffs. Understanding CAP theorem thoroughly will give you the foundational knowledge when assessing which data store to use. You might need strong consistency over anything else, think financial transactions, in which case use a traditional relational data base like Postgres. You might need every request to return a quick response but it doesn't have to be consistent. Think follower counts on social media. If you see I have 9833 or 9817 followers doesn't matter that much. The ballpark is good enough. Pick something like Cassandra for these use cases. The last one is consistent and scalable systems. In these cases you something like MongoDB

Normalization vs. Denormalization: Choosing between a normalized or denormalized data model depends on the use case. Normalized models reduce redundancy but can increase the complexity of queries, while denormalized models can improve read performance but may lead to data inconsistency. Partitioning and Sharding: Partitioning involves breaking up large tables into smaller, more manageable pieces. Sharding takes this a step further by distributing those partitions across different servers or nodes. This can significantly improve both performance and scalability.

I've run into a few not-so-great choices for data storage that really slowed down our pipelines and even caused them to crash. In that situation, our team had to completely refactore the pipeline design, from the type of database to the frameworks we were using. For data store we switched from an SQL (postgres) to NoSQL(influxdb) for data storage because SQL just couldn't handle the huge amount of data pouring in from the trucks. InfluxDB, on the other hand, is built for handling time-series data like what we get from those trucks. This was just one part of the changes but it was enough to put an end on the bottleneck of the data ingestion and reading

1. Data Type Match: Select a data store optimized for your data types (e.g., relational, NoSQL). 2. Scalability: Ensure the chosen data store can scale horizontally to accommodate concurrency. 3. Latency Consideration: Assess data store performance to meet low-latency requirements. 4. ACID Compliance:Confirm support for ACID properties if needed for data consistency. 5. Data Access Patterns: Align data store choice with anticipated query patterns for optimal performance.

You shouldn't always minimize data duplication. If minimizing data duplication means introducing a high cardinality join, you're in for a world of hurt. You should be thinking of this both in terms of storage and compute resources. When I was at Netflix, we added the microservice app to every network request even though that could be looked up by IP address. The reason we did this is the IP address join was a high cardinality lookup that degraded performance dramatically. This data duplication actually saved our bacon quite a bit.

In one of the projects which I have worked in Oracle, I was tasked with improving the query response time which involved Index organized tables (IOT). IOTs has 2 components, index (can have one or more columns) and value(s). The index and the value are not stored separately. IOTs are mainly used to quickly retrieve value using the index. The users started querying with columns which are not a part of index or some of the columns of the index. This resulted in inefficient queries due to costly table scans. I converted the IOTs into regular tables with indexes where the query pattern changed drastically like the example which I provided above. The changed table with indexes executed faster with lesser cost.

Telematics data from trucks for fleet management have many kinds of sensor with high volume and velocity such as engine temperature, GPS coordinates, speed, and fuel consumption. At Bosch, we consider data organized into measurements. Each measurement represents a type of data, for example. InfluxDB automatically indexes timestamp and tag keys, which are crucial for efficient time-series data retrieval. Tag Indexing and Field Indexing on InfluxDb helps a lot to optimize the schema and it will result in efficient storage, faster queries, and better overall database performance, making it well-suited for high-concurrency and low-latency scenarios.

Two things that have helped me are clustering and partitioning large datasets, to reduce query runtime. Partitioning should be done on the most granular date field and clustering should be performed on high cardinality variables. However, too many clusters might have the opposite effect and increase query runtime. So a variable that is granular enough should be picked for clustering - but not sthing as granular as something, such as, a transaction ID!

One example of the data model for high concurrency and low latency that comes to my mind is IOT sensor data. I worked for one of my AWS cloud customers. Leverage time-series databases like Amazon TimeStream or InfluxDB that are purpose-built for high-ingest sensor data. Their compression and downsampling features streamline storage and querying of timestamped sensor streams. Start by thinking about structuring the schemas around primary query and analysis needs - what insights are most critical to derive from the data? Model to optimize for those use cases. Another point to note is to avoid over-indexing. Focus indexes on frequently filtered attributes and dimensions needed for joins. Excess indices slow writes.

1. Caching Layer: Introduce an in-memory caching layer (e.g., Redis) to store frequently accessed data. 2. Query Result Caching: Cache the results of frequently executed queries to reduce redundant processing. 3. Batch Processing: Group multiple small transactions into larger batches for more efficient processing. 4. Asynchronous Processing: Use asynchronous batch processing to handle data updates without blocking queries. 5. Cache Invalidation: Implement cache invalidation strategies to keep cached data up-to-date.

Caching, like AWS's ElastiCache, swiftly serves frequent queries by storing data temporarily in a fast-access layer, reducing database load. Batching, on the other hand, aggregates multiple queries, minimizing network and database overhead, much like AWS's Data Pipeline that groups and processes data efficiently. For instance, employing Redis with ElastiCache to handle frequent read queries, and utilizing Data Pipeline for batch-processing write queries can significantly enhance system performance, striking a balance between latency and concurrency. This fusion of caching and batching, tailored to specific use-cases, forms the bedrock of a robust, high-performance data architecture.

I always setup tools to monitor the pipelines as like Prometheus and Grafana. So then I can create alerts and automate auto scales to guarantee the pipeline is running smoothly. Also, the creation of dashboards in grafana or new relic helps a lot to visualize and understand your data ingestion.

Creating a robust data model is just the beginning; the real magic happens when you fine-tune and monitor it. Regular monitoring and tuning are essential to keep your data systems operating at peak efficiency. Data models should be agile, adapting to changing data and business needs. By following these steps, you ensure your data engineering endeavors are set up for success. So, remember, it's not just about building it; it's about perfecting it over time. How do you approach monitoring and tuning your data models? Share your thoughts and experiences in the comments. Let's exchange insights and best practices! ???? #DataModeling #DataEngineering #DataOptimization

1. Performance Metrics: Define key performance metrics aligned with model goals. 2. Real-time Monitoring: Continuously monitor model predictions for accuracy and anomalies. 3. Feedback Loops: Integrate feedback mechanisms to retrain and improve the model. 4. Hyperparameter Tuning: Experiment with different hyperparameters to optimize model performance. 5. Scalability: Ensure the model can handle increased data loads and maintain low latency.

When I’m using AWS, I like to set up CloudWatch for monitoring and QuickSight for visualization to maintain a robust data pipeline. CloudWatch alerts help me to identify issues promptly. In case of high load, AWS Auto Scaling is a good choice for managing limited resources. QuickSight dashboards facilitate a better understanding of data ingestion. Especially when it has ML insights, which sometimes can give really interesting ideas and direction in optimizing the data model. As a side note: You can also leverage AWS’s load testing and benchmarking tools to simulate real-world scenarios, helping in fine-tuning schema, indexes, caching, batching, and scaling policies.

One reason I love modern data tools is the ability to create various tests on your models. For example, dbt has a tool which will stop your refresh and alert you if certain tests fail. The most common tests are: 1. Non-null 2. Unique This is especially great for primary key fields. It can prevent duplicates and give you confidence that your models are accurate.

In a SQL environment, consider the throughput and minimize JOINs. Consider using the One Big Table data modeling concepts. Kimball data modeling is nice for reporting but for low latency and high volume data sets it falls a part pretty quickly!

To design data models for high concurrency and low latency: Minimize Data Size: Use efficient data types, like bits for booleans, and compress data for reduced storage and faster transmission. Serialization and Protocols: Choose appropriate formats (e.g., Protocol Buffers) and protocols (e.g., gRPC) for efficient data transfer. For finance, consider FIX for standardised messaging. Consistency Level: Determine the right balance between strong and eventual consistency to meet application requirements without compromising latency. CQRS Pattern: Consider Command Query Responsibility Segregation (CQRS) to separate read and write operations for optimised data handling.

Knowing how to set up the pipeline is crucial. Tools like Kubernetes, load balancers, and brokers are the real heroes here. They make sure our pipeline stays up and running, keeps downtime to a minimum, and minimize data loss. With Kubernetes for example you can setup scaling outs based or data volume grows, providing a seamless and cost-effective way to accommodate increased demand.

Designing data models for high concurrency and low latency is ?? for real-time applications. 1. Partition data for scalability. 2. Optimize indexing. 3. Use in-memory caching. 4. Consider NoSQL databases. 5. Implement load balancing. 6. Leverage asynchronous processing. 7. Monitor & optimize regularly. Achieving low latency requires thoughtful planning, but it's essential for responsive, high-performance applications. #DataModeling #PerformanceOptimization #LinkedInTips

Another piece of advice is to limit duplication of your data whenever possible. Big datasets can get expensive quickly, so be cognizant of your models sizes at every step of the way. Decrease the number of CTEs, temp tables, and nested queries whenever possible. While readability might be better with more frequent, smaller transformations, the load times and storage costs can get exponential.