Hi @Rocky, Please have a look into the configuration and give feedback is this good for my >50 GB workload

Hi! I'd be happy to take a look at your configuration. While 50 GB is a relatively small workload for StarRocks (which can handle petabytes), getting the configuration right from the start ensures better performance and stability. Could you please share the details of your setup? Specifically: 1. Node Count: How many FE (Frontend) and BE (Backend) nodes are you planning to use? 2. Hardware Specs: CPU (cores), RAM, and Disk type (SSD/HDD) for each node. 3. Table Types: Are you using the Primary Key model, Duplicate Key, etc.? 4. Query Profile: Is this for high-concurrency short queries or complex analytical joins? ────────── General Feedback for a > 50 GB Workload Based on StarRocks' standard recommendations, here is what a "good" baseline looks like: * High Availability (Production): * 3 FE nodes (Followers) to ensure no single point of failure. * 3 BE nodes to maintain the default 3-replica data safety. * Minimum Hardware (Per Node): * FE: 8 vCPU cores and 16 GB RAM. (Metadata for 50 GB is very small, so 16 GB is plenty). * BE: 16 vCPU cores and 64 GB RAM is the standard recommendation, though for 50 GB you could likely start with 8 cores and 16-32 GB RAM if the query volume is low. * CPU: Ensure your CPUs support the AVX2 instruction set for vectorized execution. * Storage: * SSD is strongly recommended, especially if you use the Primary Key table engine or require low-latency queries. * Capacity: 50 GB of raw data will likely compress to ~10-15 GB. With 3 replicas, you'll need about 30-45 GB of usable disk space across your BEs. * Networking: 10 Gigabit Ethernet is recommended for stable data transmission between nodes. Once you provide your specific specs, I can give you more tailored advice! References • deployment/plan_cluster.md • faq/Others.md • deployment/deployment_prerequisites.md • introduction/Architecture.md

Hi @Rocky, Please review my configuration my data will be below 50 gb at the starting, and i wanted to know is this resources are suitable or over and the method of deployment ?

Hi there! I'm Rocky. I've reviewed your proposed configuration for a starting dataset of 50 GB. Overall, your plan is very well-structured, especially for a "Safest Configuration" focusing on High Availability (HA). For a dataset of < 50 GB, here is the breakdown of whether these resources are suitable or overkill: 1. Resource Suitability * FE (m6i.large - 2 vCPU, 8 GB): * Suitability: This is a good "starter" production spec. While official documentation often recommends 8 vCPU / 16 GB for large clusters, for < 50 GB of data, your metadata will be very small. 8 GB RAM is the minimum safe amount for FE. * Verdict: Suitable. * BE (m6i.xlarge - 4 vCPU, 16 GB): * Suitability: StarRocks is a vectorized engine that thrives on CPU and Memory. 16 GB is the minimum recommended for a BE. For 50 GB of data, this will work well for low-to-moderate query complexity. If you plan to run massive joins or have hundreds of concurrent users, you might eventually need to scale to

m6i.2xlarge

(32 GB RAM). * Verdict: Suitable for starting, but keep an eye on memory usage if queries get complex. * Node Count (3 FE, 3 BE): * Suitability: For 50 GB, 1 BE could technically hold all the data. However, 3 BEs are required for High Availability and to support your "Replication = 3" strategy. Similarly, 3 FEs are required for Leader election/quorum. * Verdict: Overkill for capacity, but PERFECT for safety/HA. 2. Table Design Review * Partitioning (Monthly): At 50 GB/year, you have ~4 GB per month. This is a very manageable partition size. * Bucketing (8 Buckets): With 4 GB per partition and 8 buckets, each bucket (tablet) will be roughly 500 MB. * StarRocks Best Practice: Aim for compressed bucket sizes between 100 MB and 1 GB. Your choice of 8 buckets puts you exactly in the "sweet spot" for performance and metadata management. * Mapping Table (1 Bucket): Correct. Small dimension/mapping tables should use 1 bucket to keep metadata low. 3. Storage * Disk (500 GB gp3 per BE): _ 50 GB raw data _ 3 replicas = 150 GB total. * Distributed across 3 BEs = 50 GB used per BE. * Verdict: Slightly overkill. You could safely start with 200 GB gp3 disks and scale them up later (gp3 allows live volume expansion). However, 500 GB gives you plenty of headroom for growth and background compaction. 4. Method of Deployment Since you are on AWS, you have two primary options: 1. StarRocks Kubernetes Operator (Recommended): If you use Amazon EKS, the Operator is the easiest way to manage lifecycle, scaling, and upgrades. It handles the deployment of FE/BE and ensures they stay healthy. 2. Manual EC2 Deployment: If you prefer standard VMs, you can deploy manually. * Tip: Use a tool like Ansible or simple scripts. * Tip: Ensure all nodes are in the same Placement Group (Cluster) for the lowest possible network latency. Summary Recommendations: * Keep the 3-node setup: It's the only way to guarantee the "Fault Tolerance" you listed. * Instance Type: Your choices are a great balance between cost and the "safe" minimums for StarRocks. * Deployment: Use Amazon EKS with the StarRocks Operator if you are comfortable with Kubernetes; otherwise, use EC2 with an Auto Scaling Group (fixed at 3) to ensure nodes are replaced if they fail. Your backup strategy (S3 snapshots) is the industry standard for StarRocks and is highly recommended. You are in great shape to start! References • best_practices/bucketing.md • best_practices/partitioning.md • knowledge/usage/best-practice.md • table_design/data_distribution/Data_distribution.md

Hi @Rocky, What about AWS quick start deployment ? is this good way to deploy prod by this method ?

Hello! I'm Rocky. The AWS Quick Start (which typically uses CloudFormation templates) is a great way to get a StarRocks cluster up and running quickly for testing, development, or a Proof of Concept (PoC). However, for a production environment, there are several factors to consider when deciding if it's the right fit for you. Is it good for Production? While you can use the Quick Start for production, most users prefer other methods for long-term production management due to the following reasons: 1. Lifecycle Management: Quick Start templates are excellent for initial "day 1" deployment, but "day 2" operations—like rolling upgrades, scaling individual components, and complex monitoring integration—can be more manual compared to other methods. 2. Customization: Production environments often require specific VPC configurations, security group hardening, and integration with existing IAM roles that might exceed the parameters provided in a standard Quick Start template. Recommended Production Deployment Methods on AWS: If you are planning a production rollout, I recommend looking at these alternatives: 1. StarRocks Operator on Amazon EKS: This is highly recommended if you are already using Kubernetes. The StarRocks Operator manages the lifecycle of the cluster, including automated upgrades, scaling, and self-healing. It’s generally considered the best way to run StarRocks in a modern cloud-native production environment. 2. StarRocks Cloud (Managed Service): For the easiest production experience, StarRocks has a fully managed version (offered by CelerData) available on the AWS Marketplace. This handles all the infrastructure management, tuning, and scaling for you. 3. Manual Deployment on EC2 (via Terraform/Ansible): If you need absolute control over the OS and hardware tuning and don't use Kubernetes, many production users use Terraform to provision EC2 instances and Ansible to deploy StarRocks. This allows for fine-grained tuning of disk I/O and network settings. Summary: * Use AWS Quick Start if: You want to test StarRocks features quickly or need a baseline template to build your own automation. * Use StarRocks Operator (EKS) if: You want the best balance of control and automated management for production. * Use Managed Service if: You want to focus on data and queries rather than managing the database infrastructure. Would you like more details on the EKS deployment or specific hardware recommendations for AWS?