Hydrolix uses an ANSI-compliant SQL interface. This interface uses the syntax and some of the SQL engine of Clickhouse. All standard features, including how the interface API works for querying data, are supported.

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Because Hydrolix query infrastructure is decoupled from storage and collection, you can quickly scale query pools up or down. Small query pools give you consistent low-cost queries, while large pools give you consistent low-latency queries.

You can create separate query pools for different groups of users. For example, you might configure separate sandboxes for administrator, interactive analyst, and monitoring queries.

Pool groups support independent scaling, so the capacity for each pool can adjust automatically to satisfy demand. You can even scale an entire pool to zero when demand is negligible–for example, over the weekend when staff do not need to access data. When demand returns, you can scale the pool back up within minutes.

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Hydrolix improves query efficiency compared to other cloud data platforms through its unique architecture. Our decoupled and stateless design separates ingest and query resources from storage, allowing us to focus on efficiently handling high-cardinality and high-dimensionality data.

Here’s how our architecture achieves query efficiency:

• Scalable Query Pools: Hydrolix enables you to scale query resources independently, ensuring consistently low-latency queries as your data workload grows.
• Partition Metadata: We utilize partition metadata to speed up time-based queries, which is particularly beneficial for time-series data analysis.
• Full Column Indexing: Hydrolix leverages full-column indexing, which optimizes query performance by swiftly locating the necessary data.
• Predicate Pushdown: Our platform efficiently filters datasets using predicate pushdown, further enhancing query efficiency.

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