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<@U0A71G31CDV> whats the diference between a be no...
# questions-and-troubleshooting
e
@Rocky whats the diference between a be node and a cn node in starRocks whats the use case for each ?
r
In StarRocks, the primary difference between a BE (Backend) node and a CN (Compute Node) is whether they store data or just process it. BE (Backend Node) BE nodes are the traditional "workers" in a StarRocks shared-nothing architecture. * Role: Responsible for both data storage and SQL execution. * Storage: They store data tablets on local disks and manage data replicas to ensure high availability. * Scaling: When you add or remove BE nodes, StarRocks must rebalance the data across the cluster to maintain distribution and replication. * Use Case: The standard choice for Shared-Nothing deployments where you want the best performance by keeping compute and storage on the same physical machine (data locality). CN (Compute Node) CN nodes are stateless components introduced to support elastic scaling and cloud-native architectures. * Role: Responsible only for SQL execution. They do not store any persistent data. * Storage: They have no local data storage responsibilities. They fetch data from remote storage (like S3/HDFS in Shared-Data mode) or from BE nodes. * Scaling: Because they are stateless, they can be scaled up or down instantly without any data rebalancing or migration. * Use Case: * Shared-Data Architecture: In "cloud-native" mode, CNs are the primary workers that pull data from centralized storage (S3/OSS). * Data Lake Analytics: Used as a dedicated compute layer to query external tables (like Iceberg, Hudi, or Hive) without needing to ingest the data into StarRocks. * Elastic Scaling: In shared-nothing clusters, you can add CNs as "overflow" compute resources during peak hours to handle heavy query loads without affecting the underlying data distribution. Summary Table Feature | BE (Backend) | CN (Compute Node) Data Storage | Yes (Local disks) | No (Stateless) SQL Execution | Yes | Yes Architecture | Shared-Nothing (Standard) | Shared-Data (Cloud-native) / Elastic Scaling Speed | Slower (requires data rebalance) | Instant (no data movement) Main Benefit | Performance via data locality | Flexibility and elasticity References • deployment/plan_cluster.md • introduction/Architecture.md • quick_start/shared-data.md • deployment/deploy_manually.md
e
@Rocky ok so if I got got a external catalog (nessie) for iceberg tables on s3 , having a fe and be is less better performance wise than having a fe and cn ?
r
For an external Iceberg catalog (using Nessie and S3), the performance for a single query is effectively the same between BE and CN nodes, but CN nodes are the better choice for your overall architecture. Here is a breakdown of why: 1. Execution Performance Both BE (Backend) and CN (Compute Node) use the same vectorized execution engine. When querying Iceberg on S3, neither node has "data locality" because the data is remote. * Result: For the actual processing (CPU/RAM usage), a BE and a CN with the same hardware specs will perform identically. 2. Data Caching (The Performance Booster) To avoid downloading data from S3 for every query, StarRocks uses a Data Cache to store hot data on local NVMe/SSD disks. * BE Nodes: Support Data Cache. * CN Nodes: Support Data Cache. * Note: Since both support caching, once the data is "warmed up" on the local disk, they both achieve performance comparable to local storage. 3. Why CN is "Better" for Your Use Case If your primary workload is querying an external catalog (Iceberg), CN nodes offer several advantages over BE nodes: * Stateless Elasticity: Because your data lives in S3/Nessie, you don't need the storage management features of a BE node. You can spin up 10 CN nodes during a heavy reporting window and shut them down afterward. Doing this with BE nodes would trigger unnecessary "data rebalancing" tasks as the cluster tries to figure out where to store internal tablet replicas. * Resource Isolation: You can have a stable set of BE nodes for internal StarRocks tables and a separate, auto-scaling group of CN nodes dedicated to the Iceberg "Data Lake" queries. This prevents a heavy S3 scan from starving your internal real-time dashboards of resources. * Ease of Management: CN nodes are simpler to deploy in containers (Kubernetes/K8s) because they don't require persistent volumes for data storage (unless you want to persist the local cache across restarts). Summary Recommendation * Use BE nodes if you also plan to ingest data into StarRocks' internal tables (Shared-Nothing tables) for the absolute lowest latency. * Use CN nodes if you are primarily doing "Data Lake Analytics" (querying Iceberg/S3). They give you the same query speed but with much better flexibility and easier scaling. Pro-tip: Regardless of which you choose, ensure you enable the Data Cache on your nodes' local SSDs to avoid the latency and cost of repeated S3 fetches. References • data_source/catalog/iceberg/iceberg_catalog.md • introduction/Architecture.md • data_source/feature-support-data-lake-analytics.md References • introduction/Architecture.md • deployment/plan_cluster.md
e
@Rocky question, if I got hot data (last 7 days) in native starRocks table and cold data(older than 7 days) in s3 iceberg tables -> when I do a query how does the fe know whether to query a be node or a cn node ? also if I want for millions (200 million plus) rows analytics queries in less than a second, or if I want to make queries to fetch data by time and different multiple order by's can querying s3 iceberg achieve good timings ? can querying native starRocks tables achieve good timings ?
r
In this hybrid setup, StarRocks provides high performance by intelligently routing tasks. Here is how it works: 1. How the FE chooses between BE and CN nodes The FE (Frontend) acts as the brain and handles query scheduling based on where the data resides: * For Native Tables (Hot Data): Native tables are stored as "tablets" physically located on the disks of BE nodes. To maintain "data locality" (processing data where it sits to avoid network overhead), the FE will almost always dispatch the scan and compute tasks for these tables to the BE nodes that own those tablets. * For Iceberg Tables (Cold Data): Since Iceberg data lives in S3, any node (BE or CN) can technically process it. * By default: The FE will use the available BE nodes. * With CN nodes: If you add CN nodes and set the session variable 
SET prefer_compute_node = true;
, the FE will prioritize the CN nodes for these external catalog queries. This is the recommended setup because it leaves the BE nodes' CPU/IO free to handle your high-concurrency "hot data" queries. * Mixed Queries (JOINs): If you join your hot native table with cold Iceberg data, the FE creates a distributed execution plan. It will scan the native data on the BEs and the Iceberg data on the CNs, then shuffle the intermediate results over the network to perform the join. 2. Performance: Sub-second queries on 200M+ rows Native StarRocks Tables * Performance: Excellent. Sub-second latency for 200 million rows is StarRocks' "bread and butter." * Why: It uses a highly optimized columnar format, "Short Key" indexes (for fast range scans on time), and data pruning (partition/bucket pruning). With multiple 
ORDER BY
clauses, StarRocks' vectorized execution engine processes blocks of data in parallel, making complex sorting very fast. * Tip: Use the 
Duplicate Key
or 
Primary Key
table model and set your most frequent 
ORDER BY
columns as the first columns in the table definition (the "Sort Key"). S3 Iceberg Tables * Performance: Good (with Caching), Slower (without). * Without Cache: Achieving sub-second latency for a 200M row scan directly from S3 is extremely difficult due to S3's latency and network bandwidth limits. * With Data Cache: If you enable the StarRocks Data Cache (which uses local NVMe/SSD on your CN/BE nodes), performance becomes comparable to native tables. Once the "cold" data is queried once and cached locally, subsequent queries for that time range will be sub-second. * Metadata: StarRocks also caches Iceberg manifest files, which speeds up the "planning" phase of the query significantly. Summary Recommendation Requirement | Native Table (BE) | Iceberg on S3 (CN) Sub-second 200M rows | Yes (Native) | Yes (If Data Cache is enabled) Complex Order By | Fast (via Sort Keys) | Moderate (Vectorized compute) Time Filtering | Excellent (Partitioning) | Good (Partitioning) For your use case: Use BE nodes for the last 7 days of data to ensure the most consistent, sub-second performance. Use CN nodes with Data Cache enabled for the older Iceberg data. This gives you an "Elastic Data Warehouse" where hot data is always fast, and cold data is cost-effective but still high-performance when needed. References • data_source/catalog/iceberg/iceberg_catalog.md • administration/management/Data_cache.md • table_design/table_types/duplicate_key_table.md • sql-reference/System_variable.md References • sql-reference/System_variable.md • table_design/table_design.md
In your hybrid setup (Native Hot Data + Iceberg Cold Data), StarRocks handles node selection and performance through a mix of data locality and execution preference. 1. How the FE chooses between BE and CN nodes The Frontend (FE) uses different logic depending on where the data is stored: * For Native StarRocks Tables (Hot Data): These tables are physically stored in "tablets" on the disks of your BE nodes. The FE knows exactly which BE contains which tablet. To minimize network overhead, the FE always assigns the scan task to the specific BE node where that data lives. * For Iceberg Tables (Cold Data): Since this data is external (on S3), it can technically be processed by either a BE or a CN. * Prioritizing CNs: You can tell the FE to use CNs for these external queries by setting the session variable: 
SET prefer_compute_node = true;
* Separation of Work: This is the ideal architecture. It ensures that heavy S3 scans on cold data don't consume the CPU/IO of your BE nodes, which are busy serving your sub-second hot data queries. * Hybrid Queries (Joins): If you join a native table with an Iceberg table, the FE will generate a distributed plan: the BEs scan the native data, the CNs scan the Iceberg data, and they shuffle the intermediate results to complete the join. 2. Can you achieve sub-second queries on 200M+ rows? Native StarRocks Tables: YES StarRocks is designed specifically for this. For 200 million rows, sub-second latency is standard if the table is designed correctly. * Order By Optimization: To speed up queries with multiple 
ORDER BY
clauses, you should define a Sort Key (the first few columns in your 
CREATE TABLE
statement). StarRocks stores data in this sorted order on disk, making range scans and sorting extremely efficient. * Indexing: It uses columnar storage and zone-map indexes (min/max values per block) to skip irrelevant data instantly. S3 Iceberg Tables: YES (with conditions) Querying S3 directly for 200M rows in under a second is difficult due to the "S3 Tax" (network latency and manifest file overhead). However, you can achieve it by enabling two features: 1. Data Cache: This is critical. By enabling 
block_cache_enable = true
on your CN nodes, StarRocks will store "hot" parts of your Iceberg files on local NVMe/SSD. Once the cache is warmed up, the query speed for Iceberg data becomes nearly identical to native tables. 2. Metadata Cache: StarRocks caches Iceberg manifest files in the FE memory. This removes the overhead of having to talk to the Nessie catalog and S3 just to find out which files to read. Summary Table Capability | Native Tables (BE) | Iceberg on S3 (CN) 200M rows < 1s | Native (Optimized for this) | Yes, but requires Data Cache Scheduling | Fixed to BEs (Data Locality) | Flexible (Prefer CN via config) Sort Performance | Excellent (via Sort Keys) | Good (Vectorized engine) Best For | High-concurrency, fixed-latency | Cost-effective, elastic scaling Recommendation: For your "multiple order by" requirements, put the most frequent sorting columns at the beginning of your Native table's schema. For Iceberg, ensure your CN nodes have fast local SSDs for the Data Cache to bridge the performance gap with S3. References • introduction/Architecture.md • sql-reference/System_variable.md • data_source/data_cache.md • data_source/catalog/iceberg/iceberg_catalog.md References • data_source/catalog/iceberg/iceberg_catalog.md • data_source/data_cache.md
e
@Rocky and how many BE nodes would I need for 200 million rows of data in native starRocks tables, if I wanted sub second query time ?
r
For a dataset of 200 million rows in a native StarRocks table, you can easily achieve sub-second query performance with a small, standard production cluster. The Recommended Configuration To ensure both sub-second performance and high availability (HA), the recommendation is: * 3 BE (Backend) Nodes * Specs per node: 16 Cores (CPU) and 64 GB RAM. Why 3 BE Nodes? 1. High Availability: StarRocks typically stores data in 3 replicas by default. Having at least 3 BE nodes ensures that if one node fails, your data remains available and your queries continue to run without interruption. 2. Parallelism: StarRocks is a Massively Parallel Processing (MPP) engine. When you query 200 million rows, the task is split into fragments across all 3 nodes. Each node only has to process about 66 million rows (and even less if you use partitioning). 3. Processing Power: A general rule of thumb for StarRocks is that one CPU core can process between 10 million and 100 million rows per second, depending on the query complexity. * 200M rows / 48 total cores (3 nodes × 16) = ~4 million rows per core. * Even with complex 
ORDER BY
clauses and aggregations, 48 cores can easily crunch 200 million rows in well under 500ms. How to Guarantee Sub-Second Performance Even with the right number of nodes, your Table Design is what will truly lock in that sub-second speed: * Sort Keys: Since you mentioned "multiple order bys," this is critical. Put the columns you most frequently use in 
ORDER BY
or 
WHERE
clauses at the top of your table definition. StarRocks will store the data physically sorted by these columns, allowing it to skip massive amounts of data. * Partitioning: Partition your data by Time (e.g., 
PARTITION BY dt
). If you query "the last 24 hours," StarRocks will only scan the 24-hour partition and ignore the rest of the 200 million rows entirely. * Bucketing: Use a high-cardinality column (like 
user_id
or 
order_id
) for your 
DISTRIBUTED BY HASH
key. This ensures the 200 million rows are spread evenly across your 3 BE nodes. Summary of Sizing Metric | Requirement Data Volume | 200 Million Rows Minimum BEs | 3 (for HA and Performance) vCPU per BE | 16 Cores RAM per BE | 64 GB Expected Latency | 100ms - 500ms (for typical analytics) If your query volume (QPS) is very high (hundreds of users querying at the exact same time), you might eventually scale to more nodes, but for a standard workload, 3 BEs is the "sweet spot" for 200 million rows. References • deployment/plan_cluster.md • table_design/table_types/duplicate_key_table.md • introduction/Architecture.md • administration/management/Scale_up_down.md References • faq/Deploy_faq.md • deployment/plan_cluster.md • administration/management/BE_configuration.md • faq/Others.md
For a dataset of 200 million rows, StarRocks can easily achieve sub-second query times with a relatively small cluster. Based on official benchmarks and best practices, here is the recommended sizing: Recommended Setup: 3 BE Nodes For a production environment, we recommend 3 BE nodes. * Why 3? This is the minimum requirement for High Availability (HA). If one node fails, your 7 days of "hot data" remains available, and StarRocks continues to serve queries. * Performance Capacity: In StarRocks benchmarks (like SSB or TPC-H), a 3-node cluster with standard hardware often handles 300 million to 600 million rows with sub-500ms response times for complex analytical queries. * Row Processing Speed: StarRocks' vectorized engine typically processes between 10 million and 100 million rows per second, per CPU core. With a 3-node cluster, you have plenty of aggregate CPU power to scan and sort 200M rows in well under a second. Hardware Specs per BE Node To ensure consistent sub-second performance for multiple 
ORDER BY
and complex analytics: * CPU: 16 Cores (vCPU). * RAM: 64 GB. * Disk: SSD or NVMe (Essential for the "Data Cache" for your Iceberg queries and fast local scans). ────────── Critical Design Tips for Your "Sub-Second" Goal Even with enough nodes, your Table Design is what guarantees the performance: 1. The Sort Key (ORDER BY Clause): Since you mentioned fetching data by time with multiple 
ORDER BY
requirements, you must define the 
ORDER BY
columns in your 
CREATE TABLE
statement. * Tip: Put the most frequently filtered column (e.g., 
event_time
) first in the 
ORDER BY
list. This creates a "short-key index" that allows StarRocks to skip massive amounts of data instantly. 2. Bucketing (Distribution): Set your bucket count so that each "tablet" is roughly 100MB to 1GB in size. For 200M rows, this might mean 16 to 32 buckets. This ensures that when you run a query, all 3 BE nodes (and all their CPU cores) work on the data in parallel. 3. Partitioning: Partition your native table by day. When you query "the last 24 hours," StarRocks will only touch the specific partition for that day, ignoring the other 6 days of hot data entirely. Summary You do not need a massive cluster for 200 million rows. 3 BE nodes (16c/64G each) is the "sweet spot"—it gives you the high availability you need for production and more than enough horsepower to hit sub-second latencies on a 200M row dataset. References • deployment/plan_cluster.md • table_design/table_design.md • benchmarking/SSB_Benchmarking.md • administration/management/Data_cache.md
e
@Rocky why not 1 BE and 2 CN ?
r
While you can run a cluster with 1 BE and 2 CNs, it is not recommended for your specific "hot/cold" strategy for two main reasons: High Availability (HA) and Native Table Limitations. 1. The "Single Point of Failure" for Hot Data Native StarRocks tables (the ones you are using for your "last 7 days" hot data) rely on a multi-replica mechanism to ensure they stay online if a server crashes. * Replication: By default, StarRocks wants to store 3 replicas of every piece of data (tablet) across different BE nodes. * The Problem with 1 BE: If you only have 1 BE node, you can only have 1 replica of your hot data. If that single BE node goes down (hardware failure, reboot, etc.), your entire "hot" dataset is offline. * CNs don't help here: CN nodes (Compute Nodes) are stateless. They do not store persistent copies (replicas) of native StarRocks tables. They cannot "pick up the slack" if your only BE node fails. 2. Performance & Data Locality The primary reason you are using native tables for hot data is speed. * BE Advantage: BE nodes scan data from their local disks. This is why they are so fast for sub-second queries. * CN Limitation: If you use CN nodes to query a native table sitting on a remote BE, the data has to travel over the network from the BE to the CN. This adds latency and defeats the purpose of having "hot" local data. * Best Practice: You want the nodes doing the processing to be the same nodes holding the data. With 3 BEs, each node stores a portion of the data and processes it locally. 3. When would you use 1 BE and 2 CNs? This configuration is typically only used for very specific testing scenarios: * External Queries Only: If you were only querying Iceberg/S3 and had almost no native data. * Cost Saving in Non-Prod: If you don't care about the 7 days of hot data disappearing if the BE crashes. Summary Recommendation To meet your requirement of sub-second queries on 200M rows with High Availability: * Use 3 BE nodes: This allows for 3-way replication. If one BE fails, the other two still have the data and can continue serving your "hot" queries without interruption. * Add CNs only for scaling: If your Iceberg queries (cold data) start to slow down the BEs, then add CN nodes to handle the S3 processing. In short: BE nodes are the "Storage + Compute" workers, while CN nodes are "Compute Only" workers. You need at least 3 "Storage" workers (BEs) to keep your hot data safe and fast. References • deployment/plan_cluster.md • introduction/Architecture.md • administration/management/resource_management/Replica.md • faq/Others.md References • deployment/plan_cluster.md • administration/management/resource_management/Replica.md • faq/Others.md • faq/Deploy_faq.md • quick_start/helm.md • faq/operation_maintenance_faq.md
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