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ClickHouse engineering resources

  • How to scale vector search in Postgres (pgvector) for RAG and AI agents: memory limits, filtering, and when to go hybrid

    Scale vector search in Postgres with pgvector: avoid HNSW RAM limits, fix filtering recall, and know when to go hybrid. Read now.

    Last updated: Jun 18, 2026

  • How to query a REST API in Python

    Read a JSON API response into a DataFrame with chDB. Use the pandas API you already know to filter and aggregate the response, running on ClickHouse's engine with no server to start.

    Last updated: Jun 15, 2026

  • How to convert Parquet to ORC

    Convert a Parquet file to ORC with one clickhouse-local command. The schema is read from the Parquet footer and the types carry into ORC, with no server and no upload.

    Last updated: Jun 6, 2026

  • How to query a REST API with SQL

    Run SQL straight against a live JSON or CSV HTTP endpoint with clickhouse local — fetch, filter, aggregate and join the response in one statement, with no download step.

    Last updated: Jun 15, 2026

  • Parse url-encoded form data with SQL

    Turn an application/x-www-form-urlencoded body like a=1&b=hello into a queryable row with clickhouse local, with no parsing code and no import step.

    Last updated: Jun 15, 2026

  • What is Protobuf?

    Protobuf (Protocol Buffers) is a schema-first, compact binary serialization format. This page explains its structure, the .proto schema requirement, and proves it by writing and reading a real Protobuf file.

    Last updated: Jun 15, 2026

  • Run SQL across multiple CSV or Parquet files

    Query a whole directory of CSV or Parquet files as one table with clickhouse-local. A glob path reads them all, aggregates run across every file, and a virtual column tells you which file each row came from.

    Last updated: Jun 15, 2026

  • Read Avro from a schema registry with clickhouse-local

    Read a plain .avro file with one command, and use the AvroConfluent format plus format_avro_schema_registry_url to read Confluent-framed Avro messages from a Kafka Schema Registry.

    Last updated: Jun 15, 2026

  • How to query a mysqldump file without importing

    Run SQL against the tables inside a mysqldump .sql file directly with clickhouse-local. No MySQL server, no restore step — the INSERT statements are read as a table.

    Last updated: Jun 15, 2026

  • How to convert Avro to JSON

    Convert an Avro file to JSON with one command using clickhouse-local. The schema is read from the Avro file itself, types are carried into JSON, and there is no upload and no server.

    Last updated: Jun 15, 2026

  • How to read a RowBinary file

    Read a ClickHouse RowBinary file from your terminal with one command, using clickhouse local. RowBinaryWithNamesAndTypes is self-describing; plain RowBinary needs an explicit structure.

    Last updated: Jun 15, 2026

  • How to read a ClickHouse Native format file

    Open and query a ClickHouse Native (.native) file from your terminal with one command, using clickhouse local. The file carries its own schema, so reads need no types and lose no precision.

    Last updated: Jun 15, 2026

  • How to convert TSV to JSON

    Convert a TSV file to JSON from your terminal with clickhouse-local — the header becomes the keys and types are inferred, with no upload and no server.

    Last updated: Jun 15, 2026

  • How to convert NDJSON to CSV

    Convert a line-delimited NDJSON file to CSV from your terminal with clickhouse-local — schema auto-inferred, nested objects flattened, no server and no upload.

    Last updated: Jun 15, 2026

  • How to convert JSONL to JSON

    Turn a line-delimited JSONL file into a single JSON array from your terminal with clickhouse-local. Schema auto-inferred, types preserved, no server and no upload.

    Last updated: Jun 15, 2026

  • How to convert JSON to TSV

    Convert a JSON file to a tab-separated file from your terminal with clickhouse-local — schema auto-inferred, types preserved, nested objects flattened, no upload and no server.

    Last updated: Jun 15, 2026

  • How to convert JSON to JSONL

    Turn a top-level JSON array into JSONL (one object per line) with one clickhouse-local command — no upload, schema auto-inferred, and nested fields preserved.

    Last updated: Jun 15, 2026

  • How to convert CSV to NDJSON

    Convert a CSV file to NDJSON (newline-delimited JSON) in one command with clickhouse-local — schema auto-detected, types carried into the JSON, no upload and no server.

    Last updated: Jun 15, 2026

  • How to convert CSV to Avro

    Convert a CSV file to Avro with one clickhouse-local command. The Avro schema is derived from the column types inferred from the CSV, with no server and no upload.

    Last updated: Jun 15, 2026

  • How to convert Avro to CSV

    Convert an Avro file to CSV with one clickhouse-local command. No upload, no server, and the Avro schema is read straight from the file so types carry over.

    Last updated: Jun 15, 2026

  • What is an Avro file?

    An Avro file is a row-oriented, binary data format that embeds its own JSON schema in the file header, built for schema evolution. This page explains its structure and proves it by reading the embedded schema with clickhouse local.

    Last updated: Jun 15, 2026

  • How to read an Avro file in Python

    Read an Avro file into a DataFrame with chDB and work with it using the pandas API you already know. The embedded schema is read for you, with no struct declarations and no reader loop.

    Last updated: Jun 15, 2026

  • How to read an Avro file

    Read and query an Avro file from your terminal with one command, using clickhouse local. The schema is embedded in the file, so there is no structure to declare and no import step.

    Last updated: Jun 15, 2026

  • How to convert Parquet to Avro

    Convert a Parquet file to Avro from your terminal with clickhouse-local — the schema is read from the Parquet footer and carried into Avro, with no server and no upload.

    Last updated: Jun 15, 2026

  • How to convert ORC to JSON

    Convert an ORC file to JSON from your terminal with one clickhouse-local command — the schema is read from the ORC footer, nested columns become nested JSON, with no upload and no server.

    Last updated: Jun 15, 2026

  • How to convert JSONL to CSV

    Convert a JSONL (NDJSON) file to CSV from your terminal with clickhouse-local — schema auto-inferred, nested objects and arrays flattened into columns, no upload and no server.

    Last updated: Jun 15, 2026

  • How to convert CSV to JSONL

    Convert a CSV file to JSONL (one JSON object per line) in a single command with clickhouse-local — the schema is auto-inferred and the column types are carried into the JSON, with no upload and no server.

    Last updated: Jun 15, 2026

  • How to convert CSV to Arrow

    Convert a CSV file to the Arrow IPC format in one command with clickhouse-local — the schema is inferred from the CSV and the column types are embedded in the Arrow file, with no server and no upload.

    Last updated: Jun 15, 2026

  • How to convert Avro to Parquet

    Convert an Avro file to Parquet with one clickhouse-local command. The schema embedded in the Avro file is read automatically and carried into Parquet, with no server and no upload.

    Last updated: Jun 15, 2026

  • How to convert Arrow to CSV

    Convert an Arrow (Arrow IPC / Feather) file to CSV with one clickhouse-local command. The schema is read from the file, types carry over, and there's no upload and no server.

    Last updated: Jun 15, 2026

  • What is MessagePack?

    MessagePack is a compact binary serialization format that encodes the same data model as JSON in far fewer bytes. This page explains how it is laid out and proves it by reading a real .msgpack file.

    Last updated: Jun 15, 2026

  • What is an .npy file?

    An .npy file is NumPy's simple binary format for storing a single numeric array: a small text header describing dtype and shape, followed by the raw bytes. This page explains its structure and proves it by reading one with clickhouse local.

    Last updated: Jun 15, 2026

  • How to read an NPY file in Python

    Read a NumPy .npy array into a DataFrame with chDB and work with it using the pandas API you already know, without loading the whole array into memory.

    Last updated: Jun 15, 2026

  • How to read a .npy (NumPy) file with SQL

    Query a NumPy .npy array file with SQL from your terminal using clickhouse local. The dtype is read from the file, with no Python and no import step.

    Last updated: Jun 15, 2026

  • How to read a MessagePack file in Python

    Read a MessagePack (.msgpack) file into a DataFrame with chDB. MsgPack carries no schema, so you pass the column list once and then use the pandas API you already know, running on ClickHouse's engine.

    Last updated: Jun 15, 2026

  • How to read a MessagePack file

    Read a MessagePack (.msgpack) file with SQL from your terminal using clickhouse local. MsgPack carries no schema, so you pass the column structure explicitly in one command.

    Last updated: Jun 15, 2026

  • How to convert NPY to Parquet

    Convert a NumPy .npy file to Parquet from your terminal with clickhouse-local — the array dtype is read straight from the .npy header and carried into typed Parquet, with no upload and no Python.

    Last updated: Jun 15, 2026

  • How to convert NPY to CSV

    Convert a NumPy .npy array to CSV from your terminal with clickhouse-local — the dtype is read from the .npy header, with no upload and no Python.

    Last updated: Jun 15, 2026

  • How to convert MessagePack to JSON

    Convert a MessagePack (.msgpack) file to JSON from your terminal with clickhouse-local — one command, no server and no upload, with the column types carried through into the output.

    Last updated: Jun 15, 2026

  • How to convert MessagePack to CSV

    Convert a MessagePack (.msgpack) file to CSV from your terminal with clickhouse-local — one command, no server, no upload, and types carried straight into the CSV.

    Last updated: Jun 15, 2026

  • What is a BSON file?

    A BSON file is MongoDB's binary, typed, length-prefixed encoding of JSON documents. This page explains its structure and proves it by reading a real .bson file with clickhouse local.

    Last updated: Jun 15, 2026

  • How to read a Feather file in Python (faster than pandas)

    Read a Feather file into a DataFrame with chDB, a drop-in replacement for pandas. Change one import line and your existing pandas code runs on ClickHouse's engine, so it stays fast as files grow.

    Last updated: Jun 15, 2026

  • How to read a Feather file from the command line

    Read a .feather file and query it with SQL from your terminal using clickhouse local. Feather is the Arrow IPC format, so you read it with FORMAT Arrow. No server, no schema declaration, no import step.

    Last updated: Jun 15, 2026

  • How to read a BSON file in Python

    Read a .bson export (mongoexport / mongodump) into a DataFrame with chDB. Work with it using the pandas API you already know, running on ClickHouse's engine, with no MongoDB server and no document-by-document decode loop.

    Last updated: Jun 15, 2026

  • How to read an Arrow file in Python (faster than pandas)

    Read an Arrow IPC file into a DataFrame with chDB, a drop-in replacement for pandas. Change one import line and your existing pandas code runs on ClickHouse's engine, so it stays fast as files grow.

    Last updated: Jun 15, 2026

  • How to query a BSON file

    Query a BSON file (MongoDB's binary JSON, the format mongoexport produces) directly from your terminal with clickhouse local. The schema, including nested sub-documents, is auto-detected. No server, no import step.

    Last updated: Jun 15, 2026

  • How to convert Parquet to Arrow

    Convert a Parquet file to the Arrow IPC (Feather) format in one command with clickhouse-local — columnar in, columnar out, types preserved, no upload and no server.

    Last updated: Jun 15, 2026

  • How to convert BSON to JSON

    Convert a BSON file to JSON from your terminal with clickhouse-local — the document schema is auto-inferred and nested fields are preserved, with no upload and no server.

    Last updated: Jun 15, 2026

  • Convert BSON to CSV

    Convert a BSON file to CSV from your terminal with clickhouse-local — one command, no upload and no server, with nested sub-documents flattened into real columns.

    Last updated: Jun 15, 2026

  • How to convert Arrow to Parquet

    Convert an Arrow IPC file to Parquet with one clickhouse-local command — types carry across exactly, nothing is uploaded, and files larger than RAM stream straight through.

    Last updated: Jun 15, 2026

  • How to read an ORC file in Python (faster than pandas)

    Read an ORC file into a DataFrame with chDB, a drop-in replacement for pandas. Change one import line and your existing pandas code runs on ClickHouse's engine, so it stays fast as files grow.

    Last updated: Jun 15, 2026

  • How to read an ORC file from the command line

    Open and query an ORC file from your terminal with one command using clickhouse-local. The schema is read from the file's footer, so there's no server and no CREATE TABLE.

    Last updated: Jun 6, 2026

  • How to parse a log file with regex in SQL

    Turn unstructured log lines into typed columns and query them with SQL, using a regex and clickhouse local. One pattern maps the line layout, then ordinary SQL answers the questions.

    Last updated: Jun 6, 2026

  • ORC file format

    ORC (Optimized Row Columnar) is an open, columnar, compressed storage format for tabular data. This page explains its stripes, indexes and footer, and proves the structure by reading a real ORC file.

    Last updated: Jun 6, 2026

  • How to convert ORC to Parquet

    Convert an ORC file to Parquet with a single command using clickhouse-local — the schema is read from the ORC footer and carried into Parquet, with no server and no upload.

    Last updated: Jun 6, 2026

  • How to convert ORC to CSV

    Convert an ORC file to CSV with one clickhouse-local command — the schema is read from the ORC footer, no server and no upload, and it streams files larger than RAM.

    Last updated: Jun 6, 2026

  • How to convert JSON to CSV

    Convert JSON to CSV from your terminal with clickhouse-local — one command, schema auto-inferred, with the nested fields flattened into flat CSV columns.

    Last updated: Jun 6, 2026

  • How to convert CSV to ORC

    Convert a CSV file to columnar ORC with one clickhouse-local command — no upload and no server, the schema is inferred from the CSV and the types are carried into the ORC file.

    Last updated: Jun 8, 2026

  • How to convert CSV to JSON

    Convert a CSV file to JSON from your terminal with clickhouse-local — one object per line or a single JSON array, schema auto-inferred and types preserved, with no upload and no server.

    Last updated: Jun 8, 2026

  • What is a Parquet file?

    A Parquet file is an open, column-oriented, compressed storage format for tabular data, built for analytics. This page explains its internal structure and proves it by reading a real file's metadata.

    Last updated: Jun 8, 2026

  • What is NDJSON / JSON Lines?

    NDJSON (newline-delimited JSON), also called JSON Lines, stores one JSON object per line. This page explains the format, how it differs from a single JSON array, and proves it by reading a real file with clickhouse local.

    Last updated: Jun 8, 2026

  • What is clickhouse-local? Run SQL on any file or data source, no server

    clickhouse-local is a small, fast, standalone build of ClickHouse that runs full ClickHouse SQL over local and remote files (and external databases) from a single binary, with no server to install or start.

    Last updated: Jun 8, 2026

  • What is chDB? In-process ClickHouse for Python

    chDB is an in-process SQL OLAP engine for Python: an embedded build of ClickHouse you pip install and call directly, running full ClickHouse SQL over files, DataFrames, and remote sources with no server to start.

    Last updated: Jun 8, 2026

  • What is a TSV file?

    A TSV file is a plain-text table whose columns are separated by tab characters, optionally with a header row. This page explains the format, how it differs from CSV, and proves it by reading a real TSV with clickhouse local.

    Last updated: Jun 8, 2026

  • How to run SQL on a JSONL file

    Query a .jsonl file (one JSON object per line) with SQL straight from your terminal using clickhouse local. Keys become columns, types are inferred, no server and no import step.

    Last updated: Jun 8, 2026

  • How to query a JSON file with SQL

    Query a JSON file with SQL directly from your terminal using clickhouse local. Reads line-delimited JSON or a top-level array, infers nested objects and arrays, with no server and no import step.

    Last updated: Jun 8, 2026

  • How to run SQL on a CSV file

    Query a CSV file with SQL directly from your terminal using clickhouse-local — header and column types are auto-detected, with no database and no import step.

    Last updated: Jun 8, 2026

  • How to read a TSV file in Python (faster than pandas)

    Read a tab-separated file into a DataFrame with chDB, a drop-in replacement for pandas. Change one import line and your existing pandas code runs on ClickHouse's engine, so it stays fast as files grow.

    Last updated: Jun 8, 2026

  • How to read a semicolon-separated file

    Query a semicolon-delimited file (European-style CSV) with SQL from your terminal using clickhouse local — set the field delimiter to ";", with no database and no import step.

    Last updated: Jun 8, 2026

  • How to read a pipe-delimited file

    Query a pipe-delimited (|) file with SQL from your terminal using clickhouse local. Set the field delimiter to a pipe, let the header and types be detected, and run.

    Last updated: Jun 8, 2026

  • How to read a Parquet file in Python (faster than pandas)

    Read a Parquet file into a DataFrame with chDB, a drop-in replacement for pandas. Change one import line and your existing pandas code runs on ClickHouse's engine, so it stays fast as files grow.

    Last updated: Jun 8, 2026

  • How to read an NDJSON file in Python (faster than pandas)

    Read an NDJSON file into a DataFrame with chDB, a drop-in replacement for pandas. Change one import line and your existing pandas code runs on ClickHouse's engine, so it stays fast as files grow.

    Last updated: Jun 8, 2026

  • How to read a JSONL file in Python (faster than pandas)

    Read a JSONL file into a DataFrame with chDB, a drop-in replacement for pandas. Change one import line and your existing pandas code runs on ClickHouse's engine, so it stays fast as files grow.

    Last updated: Jun 8, 2026

  • How to read a JSON file in Python (faster than pandas)

    Read an NDJSON or JSON file into a DataFrame with chDB, a drop-in replacement for pandas. Change one import line and your existing pandas code runs on ClickHouse's engine, so it stays fast as files grow.

    Last updated: Jun 8, 2026

  • How to read a file with a custom delimiter

    Query a file with any field or row separator using clickhouse local and the CustomSeparated format. Set the delimiter, query with SQL, no import step and no preprocessing.

    Last updated: Jun 8, 2026

  • How to read a CSV file in Python (faster than pandas)

    Read a CSV file into a DataFrame with chDB, a drop-in replacement for pandas. Change one import line and your existing pandas code runs on ClickHouse's engine, so it stays fast as files grow.

    Last updated: Jun 8, 2026

  • How to query nested JSON with SQL

    Query deeply nested JSON with SQL from your terminal using clickhouse local — dot access into objects, ARRAY JOIN to explode arrays, and the JSON type for irregular keys, with no server and no import step.

    Last updated: Jun 8, 2026

  • How to query an NDJSON file with SQL

    Run SQL on an NDJSON file from your terminal with one command, using clickhouse local. The schema is inferred from the data, with no server and no import step.

    Last updated: Jun 8, 2026

  • How to query a JSON Lines file

    Query a JSON Lines (JSONL) file with SQL straight from your terminal using clickhouse local. One object per line, schema inferred, no server and no import step.

    Last updated: Jun 8, 2026

  • How to query a compressed file (gzip, zstd) from the command line

    Query a gzipped CSV or a zstd-compressed Parquet file directly with clickhouse local. The codec is detected from the file, decompressed on the fly, with no unzip step and no flag.

    Last updated: Jun 8, 2026

  • How to read a TSV file

    Read and query a tab-separated (TSV) file with SQL from your terminal using clickhouse local. The header and column types are auto-detected, with no database and no import step.

    Last updated: Jun 8, 2026

  • How to query a Parquet file from the command line

    Open and query a Parquet file from your terminal with one command, using clickhouse local. No server, no schema declaration, no import step.

    Last updated: Jun 8, 2026

  • How to flatten nested JSON in Python

    Flatten JSON with nested objects and arrays-of-objects into a tidy pandas DataFrame using chDB. Change one import line and the pandas code you already write keeps working on large files.

    Last updated: Jun 8, 2026

  • How to convert TSV to Parquet

    Convert a tab-separated file to Parquet from your terminal with clickhouse-local — schema inferred from the TSV, types carried into Parquet, zstd compression by default. No upload, no server.

    Last updated: Jun 8, 2026

  • How to convert TSV to CSV

    Convert a tab-separated file to comma-separated from your terminal with clickhouse-local — one command, header preserved, types inferred, no upload and no row limit.

    Last updated: Jun 8, 2026

  • How to convert Parquet to TSV

    Convert a Parquet file to tab-separated values with one clickhouse-local command. Types are read from the Parquet footer, nothing is uploaded, and files larger than RAM stream through.

    Last updated: Jun 8, 2026

  • How to convert Parquet to NDJSON

    Convert a Parquet file to NDJSON from your terminal with clickhouse-local — one command, no server and no upload, with the Parquet schema read straight from the file footer.

    Last updated: Jun 8, 2026

  • How to convert Parquet to JSONL

    Convert a Parquet file to newline-delimited JSON (JSONL) with one clickhouse-local command — schema read from the Parquet footer, types carried into JSON, nested columns preserved. No server, no upload.

    Last updated: Jun 8, 2026

  • How to convert Parquet to JSON

    Convert a Parquet file to JSON from your terminal with clickhouse-local — the schema is read from the Parquet footer, and typed and nested columns carry straight into JSON. No server, no upload.

    Last updated: Jun 8, 2026

  • How to convert Parquet to CSV

    Convert a Parquet file to CSV from your terminal with clickhouse-local — one command, schema read from the file, types written as text, no upload and no server.

    Last updated: Jun 8, 2026

  • How to convert NDJSON to Parquet

    Convert an NDJSON (or JSONL) file to Parquet with one clickhouse-local command — the schema is auto-inferred and nested objects and arrays are carried straight into Parquet, with no upload and no server.

    Last updated: Jun 8, 2026

  • How to convert JSONL to Parquet

    Convert a JSONL file to Parquet with one command using clickhouse-local — column types are inferred automatically and nested objects become real Parquet structs, with no server and no upload.

    Last updated: Jun 8, 2026

  • How to convert JSON to Parquet

    Convert a JSON or NDJSON file to Parquet with one clickhouse-local command — schema is inferred automatically and types are carried across, including nested objects, with no server and no upload.

    Last updated: Jun 8, 2026

  • How to convert CSV to TSV

    Convert a CSV file to tab-separated values in one command with clickhouse-local. The header is kept, column types are carried over, and nothing is uploaded.

    Last updated: Jun 8, 2026

  • How to convert CSV to Parquet

    Convert a CSV file to Parquet with one command using clickhouse-local — types are inferred from the CSV, you choose the compression codec, and there is no upload and no server.

    Last updated: Jun 8, 2026

  • How to analyze log files with SQL

    Query raw nginx, Apache, or application logs in place with clickhouse-local. No server, no pipeline, no import step: one binary, SQL, and the file on disk.

    Last updated: Jun 8, 2026

  • OPTIMIZE TABLE ... FINAL in ClickHouse: when to use it, when to avoid it, and how merges work

    Stop running OPTIMIZE TABLE FINAL in ClickHouse. Learn how parts and background merges work, avoid part explosions, and fix duplicates safely. Read now.

    Last updated: Jun 15, 2026

  • The 5 best AWS RDS alternatives in 2026 (pricing, performance, and migration)

    Compare the top AWS RDS alternatives for 2026: Postgres managed by ClickHouse, Aurora, Crunchy, Neon and AlloyDB.

    Last updated: Jun 10, 2026

  • 6 Amazon Aurora alternatives for high-volume ingestion (2026 guide)

    Explore the best AWS Aurora alternatives for high-volume ingestion in 2026. Compare cost, throughput, and HA tradeoffs, choose the right stack now.

    Last updated: Jun 9, 2026

  • How to choose a database for real-time analytics in 2026

    In 2026, the gap between "Data Warehouse" and "Real-Time Serving" has become the primary bottleneck for data teams. For the last decade, we've operated on a split model: transactional databases (OLTP) handled application state, and data warehouses (DWH) handled nightly reporting.

    Last updated: Mar 25, 2026

  • Unifying OLTP and OLAP: HTAP databases, zero-ETL, and best-of-breed architectures

    A complete guide to unifying transactional and analytical workloads. Covers HTAP databases, zero-ETL integration, Postgres CDC, columnar databases, and how composable architectures beat converged systems.

    Last updated: Mar 29, 2026

  • What is OLAP?

    OLAP is a category of database systems and workloads built for analytical queries across millions to billions of rows. This is what OLAP is, how MOLAP, ROLAP, and HOLAP differ, and why OLAP databases in 2026 run on columnar engines rather than pre-built cubes.

    Last updated: May 27, 2026

  • Real-time analytics platforms: a practical comparison for 2026

    A practical 2026 comparison of real-time analytics platforms across ingestion, query latency, freshness, and concurrency — managed and open source.

    Last updated: Apr 17, 2026

  • Columnar storage formats: Parquet, ORC, and Arrow explained

    How Apache Parquet, ORC, and Arrow lay out columnar data, and why they coexist instead of converging on one format.

    Last updated: May 8, 2026

  • Row-oriented vs column-oriented databases: a head-to-head comparison

    Row stores win point lookups and per-row updates; column stores win wide aggregations and compress 5–10× tighter. A measured comparison with a decision rule.

    Last updated: May 8, 2026

  • What is vectorized query execution?

    Vectorized query execution processes batches of values per operator call instead of one row at a time. It is the dominant execution model for analytical databases.

    Last updated: May 15, 2026

  • How columnar storage works

    A mechanism-level look at how columnar storage works on disk (per-column blocks, zone maps, and predicate pushdown), with ClickHouse MergeTree and Apache Parquet internals side by side.

    Last updated: Jun 1, 2026

  • Why columnar databases are fast

    Columnar databases are 10-1000× faster than row stores on analytical queries because two principles compound: efficient execution (storage layout, vectorisation) and smart pruning (data skipping, late materialisation).

    Last updated: May 8, 2026

  • Best columnar databases in 2026

    Nine columnar databases in 2026, ranked using measured benchmark latency and compression rather than marketing copy.

    Last updated: May 8, 2026

  • When should you use a columnar database?

    Use a columnar database when queries scan many rows but few columns and the workload is aggregation-heavy. Skip it for point lookups, per-row updates, and 10K+ TPS write workloads.

    Last updated: May 8, 2026

  • How to build real-time customer-facing analytics on Postgres (without slowing down OLTP)

    Build real-time customer-facing analytics on Postgres without slowing OLTP. Learn the 2-stage path to sub-100ms dashboards with CDC + OLAP.

    Last updated: May 11, 2026

  • How to architect multi-tenant SaaS on Postgres

    Architect multi-tenant SaaS on Postgres with secure isolation, scaling patterns, and compliance-ready design. Get the blueprint and ship safely today.

    Last updated: May 25, 2026

  • What is an OLAP cube?

    An OLAP cube is a multidimensional data structure that pre-aggregates measures across hierarchical dimensions. Cubes were the standard analytics implementation from the 1990s through the early 2010s; columnar OLAP engines run the same workloads without the pre-aggregation step.

    Last updated: May 27, 2026

  • What are OLAP operations?

    OLAP operations are the canonical analytical actions performed on multidimensional data: drill-down, roll-up, slice, dice, and pivot. Each maps to a specific SQL pattern.

    Last updated: May 27, 2026

  • What is an OLAP database?

    OLAP databases are optimised for analytical queries over millions to billions of rows. Here is how they work, the five categories on the market in 2026, and how to choose between them.

    Last updated: May 27, 2026

  • Is Postgres an OLAP database?

    Postgres is an OLTP database, not OLAP. Its row-oriented storage and B-tree indexes are built for point lookups and small writes, not the wide aggregations OLAP workloads run. Here is how each Postgres-for-analytics extension actually performs, and when CDC to a columnar engine is the right call.

    Last updated: May 27, 2026

  • The fastest OLAP databases in 2026 (ranked by ClickBench)

    The fastest OLAP engines deliver sub-second median query latency on a 100 GB analytical dataset. Here are the OLAP databases ranked by their results on ClickBench, the public benchmark for analytical query latency.

    Last updated: May 27, 2026

  • OLTP vs OLAP

    OLTP handles transactional reads and writes in milliseconds; OLAP scans billions of rows for analytics in sub-second time. Architectures, decision rules, and where the line blurs.

    Last updated: May 27, 2026

  • What is a columnar database?

    A columnar database stores each column on disk separately to cut I/O and compress aggressively. Here is what that means in practice and when to use one.

    Last updated: May 25, 2026

  • Database compression: encodings, codecs and ratios

    Learn how databases compress data, the trade-offs between row and column storage, and why ClickHouse delivers industry-leading compression ratios.

    Last updated: May 6, 2026

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