## Performance evidence update / 18 September 2026 MODALITY PERFORMANCE / EVIDENCE REVIEWED 18 SEPTEMBER 2026 Analytical work on approximately 100 million rows. Cross-campaign context May–September 2026 references milliseconds · filtered count Lower is better · linear scale from zero 8DB 18 May 2026 · specialized server handler ClickHouse 1 September 2026 published reference DuckDB 41 May 2026 published reference Apache Doris 50 May 2026 published reference StarRocks 59 September 2026 published reference c6a.4xlarge machine class; dates, settings and timing boundaries differ. This selected count is not the full ClickBench suite or evidence of SQL compatibility. ClickHouse is faster in the shown reference. Read the results, comparison and methods → 01 Correct query outputs 02 Scan / aggregation latency 03 Ingest & index readiness Include memory, storage, energy and hardware writes [Read the results, comparison and methods →](/journal/8db-analytics-market-comparison) # Tables & records | 8DB Product reference. Reviewed 18 September 2026. Implementation and evaluation scope are stated below. Explore the models (https://8braid.com/modalities) / 8DB Tables & records Familiar rows. More context within reach. Work with structured records and selected fields alongside documents, relationships and time-based observations. Explore the capabilities See a complete workload Filter · sort · project Model / Tables Conceptual diagram [Diagram: tables representation: conceptual diagram] Rows share a structure. A query selects the fields and records relevant to the question. Capabilities What changes Input to value Alternatives Performance Adoption Related articles Reference Start with the operations your question needs. Typed rows and record identity Filtering, sorting and projections Point and range reads on supported paths Documented product scope, reviewed 17 September 2026. The underlying build and deployment must be qualified for your application. A relational representation is not a claim of full SQL or PostgreSQL compatibility. Check joins, transactions, aggregations, null handling and connectors against the exact supported query surface. Connected native models Keep the next question within reach. The opportunity is to carry row identity into the rest of a mixed-data question. A table can remain a useful projection while the application follows richer context beyond its columns. [Diagram: graphs representation: conceptual diagram] Graphs Follow the relationship to a better answer. Connect · traverse · follow Explore model ↗ (https://8braid.com/modalities/graphs) [Diagram: documents representation: conceptual diagram] Documents Keep the source behind the answer. Structure · search · trace Explore model ↗ (https://8braid.com/modalities/documents) [Diagram: time series representation: conceptual diagram] Time series Keep the meaning in every observation. Observe · window · analyze Explore model ↗ (https://8braid.com/modalities/time-series) These connections show the models involved in the application design. Compare the supported combination and full workflow with a realistic alternative. Input → usable value / interactive design example Build an evidence-backed operations report 0 1 Ingest records → 0 2 Select rows → 0 3 Follow the sources → 0 4 Check observations → 0 5 Build the report → [Diagram: tables representation: conceptual diagram] Step 1 / Tables Ingest operational records and stable identifiers Rows share a structure. A query selects the fields and records relevant to the question. Next: Select rows Conceptual application workflow. Qualify the supported operations, external tools, protection and persistence for the complete path. These diagrams are explanatory, not measured results. Read the complete workflow and comparison scope Ingest operational records and stable identifiers Filter the relevant assets and inspection dates Resolve relationships and source documents Inspect associated readings or spatial context Produce a report whose rows lead back to evidence Measure preparation, indexing, query and result assembly. A fast selected SQL-like operation does not establish faster analytics across every workload. Compare a realistic alternative. DuckDB is an embedded analytics alternative with an extension ecosystem. Compare required expressions, formats and deployment alongside the value of connected native representations. DuckDB extensions (https://duckdb.org/docs/stable/extensions/overview) Compare equivalent outputs and required guarantees, including the simplest single-product alternative where one exists. A specialist may remain the better fit when its operators, ecosystem or operational maturity are decisive. Combining native models becomes valuable when your actual question crosses them. Performance & resources Measure the work that delivers the answer. Result equivalence; supported expressions; scan/filter/sort/aggregation latency; loading and index cost; resource footprint; full report freshness and latency. The published comparison below covers selected historical paths and configurations. Keep those conditions attached to the result; it does not measure the complete design example on this page. 8DB analytics | How we compare, and what else you get (https://8braid.com/journal/8db-analytics-market-comparison) What belongs inside the timer and resource budget? For input-to-value testing, declare the input state and the consumer-ready output first. Include parsing, derivation, storage/protection, index readiness, querying, result assembly and delivery as applicable. Identify amortized setup and any work deferred past the finish line. Report database-stage and full-job results separately. Do not add unrelated medians or p95 values, or multiply component speedups into a claimed workflow result. Energy needs measured joules; reduced handoffs alone do not establish a saving. Choose the path that fits your deployment. Verify the supported API/SDK, data format, persistence and indexing behaviour for the release you plan to use. Match the operation-by-modality protection profile and device qualification to the workload. Post-quantum protection (https://8braid.com/pqc) · Plan a workload evaluation (https://8braid.com/build) Related articles Take the next step into the thinking. Benchmark & methods 8DB analytics | How we compare, and what else you get (https://8braid.com/journal/8db-analytics-market-comparison) Explore the measured path, comparison and conditions. Mechanism & application 8DB | Native Multimodal Data: Connect the Dots Across Graphs, Maps and Time Series (https://8braid.com/journal/modality-is-a-projection) Explore the ideas and implementation context behind this capability. Mechanism & application One Sealed Store for Every Shape of Data, and It Works With the Network Gone (https://8braid.com/journal/one-store-for-many-shapes-disconnected) Explore the ideas and implementation context behind this capability. Reference / Reviewed 17 September 2026 Readable, shareable and traceable. This reference explains 8DB’s public modality description (https://8braid.com/modalities#explore) with an explicit workload and selection criteria. It is a documentary review, not fresh execution of every operator or platform. Public sources: 8DB modality scope (https://8braid.com/modalities) ; DuckDB extensions (https://duckdb.org/docs/stable/extensions/overview) ; the related articles above. Internal source leads and outstanding release checks remain in the editorial record. Download this reference (https://8braid.com/modality-reference/tables.md) Evaluate the question your application needs to answer. Bring the data shapes, input state, expected output and the systems you use today. Define a comparison around that complete job. Discuss your workload (https://8braid.com/build)