# Hanzo Datastore — a column store for analytical SQL > High-performance columnar datastore with SIMD vectorization. Purpose-built for real-time analytics, metrics, logs, and telemetry. Hanzo Datastore # A column store for analytical SQL Datastore keeps each column of a table in its own file, compressed on its own terms, so a query naming four columns out of a hundred reads four. Aggregations run over compressed blocks with vector instructions rather than a row at a time. It is built for the queries that scan a great deal and return very little — dashboards, event analytics, metrics, logs and traces. It is an analytics database and only that, not the place your application's rows live. [Try Hanzo](https://hanzo.ai/login)[Documentation](https://docs.hanzo.ai) eventsexample SELECT count() FROM events WHERE event = 'signup' ts user_id event country device plan revenue duration_ms referrer path session version Reads 1 of 12 columnsPress a column to add it to the query. Paste and ship Paste this into any agent. It reads the skill manifest and calls Datastore from there. Agent promptapi /v1/provisioning/datastoreCopy ``` Read https://hanzo.ai/skill.md and use Hanzo Datastore in my project. Start with: hanzo provisioning datastore create ``` ## What it does Scan a great many rows. Return a small answer. Do it while the rows are still arriving. ### Columns, not rows A table is stored one column at a time, so a query reads only the columns it names. Each column gets a codec suited to what it holds — deltas for a rising timestamp, dictionaries for a repeated label, general compression over the result. ### Shard it and it keeps up Tables are partitioned and spread across nodes; add nodes and both the storage and the scan spread with them. Parts merge in the background, so ingest never stops for a compaction window. ### Replicas that agree Replicated tables coordinate through a Raft quorum running inside the server itself — there is no separate coordination service to stand up beside the database. A replica that falls behind catches up part by part. ### Local disks, or object storage Run a shared-nothing cluster on local NVMe, or put the table's data on object storage and scale query nodes on their own. Old partitions expire on a TTL you write into the schema. ### Events, metrics and logs are one shape Append-heavy, partitioned by time, queried by range — the same engine serves all three. Materialized views keep rollups current as rows land, so the summary exists before anyone asks for it. ### SQL, on several protocols Query over HTTP on 8123, on the native protocol on 9000, or through the MySQL and PostgreSQL wire ports — which is how BI tools and drivers that know nothing about it still connect. A CSV, a Parquet file or a JSON stream loads without a conversion step first. ## Where the speed comes from Nothing exotic. Read less, decompress less, and touch memory in the order the CPU wants it. ### Read only what the query names A row store reads the whole row to answer a question about two of its fields. A column store reads the two. Fewer bytes off the disk, fewer bytes to decompress, and more of the working set fits in memory. ### One instruction, many values Values from a single column sit next to each other in memory in the same representation, so a sum or a filter runs across a block of them with vector instructions instead of once per row. That layout is also what keeps the CPU cache full rather than chasing pointers. ### Sorted, so a range is contiguous Rows are written in the order of the table's sorting key and grouped into parts by partition, so a query bounded by time reads a run of adjacent blocks instead of seeking around the disk. Marks inside each part let the scan jump straight to the block that could hold the answer. ### Compression chosen per column A rising timestamp compresses as deltas. A repeated label compresses as a dictionary. A general-purpose compressor runs over whatever is left. Less data on disk is also less data on the wire once the query is spread across nodes. ## What people point it at Different industries, the same shape of problem: a great many rows in, a small answer out, asked again a second later. Web and app analytics E-commerce and finance Time series Advertising networks and RTB Information security Business intelligence Telecommunications Monitoring and telemetry Online games Internet of Things (IoT) Observability User behavior analytics Open source royalty ### 20% to the author of the code that runs Verify that you own a repository, and every org that deploys a project built from it pays you 20% of its metered spend, accrued each period. [How authors are paid](https://hanzo.ai/oss)[Our repositories](https://hanzo.ai/open-source) ## Get Datastore Document datastore License: Apache-2.0[hanzoai/datastore](https://github.com/hanzoai/datastore) [Deploy to Cloud](https://platform.hanzo.ai/deploy)[Self-host](https://docs.hanzo.ai/docs/projects/hanzoai/datastore)