> For the complete documentation index, see [llms.txt](https://help.tellius.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.tellius.com/6.0/getting-started/admin-guide/what-is-ice.md). # What is ICE? Discover how the ICE In-Memory Compute Engine in Tellius powers fast, low-latency analytics with vectorized caching for search, visualizations, and Insights. The ICE (In-Memory Compute Engine) powers interactive analytics in Tellius. It keeps frequently used data hot in a vectorized columnar format so queries, search, and visualizations run fast. **Purpose:** Low-latency, in-memory analytics.\ **Storage type:** Hot cache (memory/disk), not permanent storage.\ **Data flow:** Source → ICE (optimize/compress/partition) → Cache → Interactive compute → (optional) Publish to FQE. ### What it does * Loads data in parallel from databases, cloud stores, and files. * Stores and compresses data in a vectorized columnar format with fast in-memory compression for low-latency access. * Shards and partitions data to maximize parallelism and data locality across the cluster. * Caches data in memory, on local disk, or in a hybrid mode for predictable performance. * Serves interactive workloads such as search, visualizations, transformations (ETL), Insights, and AutoML. * Publishes denormalized Business Views to the query engine (FQE) for sub-second queries. ### How caching works * **Memory cache:** Keeps optimized, compressed columns in RAM for the fastest response. * **Disk cache:** Stores the same optimized format on local disks to extend capacity with strong performance. * **Hybrid:** Uses a configurable portion of RAM and spills the rest to disk for balanced speed and scale. * **Eviction policy:** Least-recently used datasets are evicted automatically to make space for new workloads. * **Copy to System (optional):** Keeps a Parquet copy in distributed storage to accelerate cache rebuilds without re-hitting source systems. ### When to use ICE * Interactive exploration with dashboards and search. * Fast iteration on transformations and feature engineering. * Running Insights and AutoML on hot data. * Preparing denormalized Business Views for FQE. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://help.tellius.com/6.0/getting-started/admin-guide/what-is-ice.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.