arcticdb

library6.18.1pypypi✓ verified 85d ago

ArcticDB is a high-performance, serverless DataFrame database engine designed for the Python Data Science ecosystem. It enables storing, retrieving, and processing Pandas DataFrames at scale, backed by commodity object storage like S3-compatible services, Azure Blob Storage, and local LMDB. It provides a Python API backed by a fast C++ data-processing and compression engine, supporting flexible schemas and bitemporal versioning. The library is actively maintained with frequent patch and minor releases.

database data aws azure serialization

pip install arcticdb

github ↗package ↗homepage ↗

INSTALL

IMPORT

SIG · ARCTICDB

arcticdb

databasepythonv6.18.1

Install

9.5s avg

Import

1398ms

Disk

241MB

Pass rate

5/ 10

Env Coverage5 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v6.18.1 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.920 runs

build_error

glibc

py 3.10–3.920 runs

installs and imports cleanly · install 9.5s · import 1.398s · 232MB

241MB installed

● package 241MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

Arctic

✓ from arcticdb import Arctic

adb

✓ import arcticdb as adb

This quickstart demonstrates how to initialize ArcticDB with local LMDB storage, create a library, write a Pandas DataFrame as a 'symbol', and then read it back. The example uses a temporary directory for the LMDB database.

import arcticdb as adb import pandas as pd import numpy as np import os # Use a temporary LMDB directory for local storage uri = f"lmdb://{os.environ.get('ARCTICDB_PATH', '/tmp/arcticdb_quickstart')}" ac = adb.Arctic(uri) # Create a library library_name = 'my_test_library' if not ac.library_exists(library_name): ac.create_library(library_name) lib = ac.get_library(library_name) # Create a sample DataFrame df = pd.DataFrame(np.random.randint(0, 100, size=(10, 3)), columns=list('ABC')) df.index = pd.date_range('2023-01-01', periods=10, freq='D') # Write the DataFrame symbol_name = 'my_test_symbol' lib.write(symbol_name, df) print(f"DataFrame written to {library_name}/{symbol_name}") # Read the DataFrame back read_df = lib.read(symbol_name).data print("Read DataFrame head:") print(read_df.head()) # Clean up (optional for temporary storage) # ac.delete_library(library_name)

Debug

Known issues

gotchaWhen using `update` or `append` operations, ArcticDB enforces a static schema by default. If you intend to modify the schema (add/remove/change column types), you must create the library with `dynamic_schema=True`.

fix

Create the library using `ac.create_library(library_name, library_options={'dynamic_schema': True})` if schema evolution is required.

affects: All versions

gotchaFor LMDB storage, ensure only one Arctic instance is open per LMDB database within a single Python process. LMDB also does not support remote filesystems.

fix

Limit to a single `Arctic('lmdb://...')` instance per process for a given path. Use object storage (S3, Azure) for distributed access.

affects: All versions

gotchaProduction use of ArcticDB (including business or commercial environments) or its use as a Database Service requires a paid license from ArcticDB Limited.

fix

Contact info@arcticdb.io for licensing details for production deployments.

affects: All versions

gotchaWhen connecting to Azure Blob Storage, if the `CA_cert_path` is incorrect or the certificate cannot be found, Azure exceptions may lack meaningful error codes, making debugging difficult.

fix

Verify the `CA_cert_path` is correct and the certificate exists. For debugging, set the environment variable `export AZURE_LOG_LEVEL=1` to enable SDK debug logging.

affects: All versions supporting Azure

deprecatedThe `QueryBuilder` API is officially deprecated in favor of the more intuitive and recommended `LazyDataFrame` API for complex querying, filtering, group-bys, and aggregations.

fix

Migrate code using `QueryBuilder` to use the `LazyDataFrame` API for improved readability and maintainability.

affects: All versions

Errors

Common errors & fixes

Normalization Error 2000: Attempting to update or append an existing type with an incompatible object type

You are trying to update or append data to an existing symbol, but the new data (NumPy array or Pandas DataFrame) has a different type than the original data stored in the symbol.

fix

Read the latest version of the symbol and ensure your update/append operation uses data of the corresponding type. If you need to change the schema, the library must be created with `dynamic_schema=True`.

LMDB error code -30792

The LMDB map size limit has been reached, meaning your local database file is full and cannot store more data.

fix

Increase the map size when initializing `Arctic` for LMDB by passing a larger `map_size` in the URI or options. Example: `adb.Arctic('lmdb:///path/to/db?map_size=100GB')`.

Normalization Error 2003: A write of an incompatible index type has been attempted.

ArcticDB only supports specific Pandas index types for its on-disk index (e.g., `DatetimeIndex` for time-series, or a `RowCount` integer index). You attempted to write data with an unsupported index type.

fix

Ensure your Pandas DataFrame uses a supported index type, such as `DatetimeIndex` for time-series data. Non-DatetimeIndex will be converted to a `RowCount` integer index.

ArcticException: A missing key has been requested. [...] The symbol being worked on does not exist.

You are trying to read, update, or append to a symbol that does not exist in the specified library.

fix

Verify the symbol name is correct. Use `library.list_symbols()` to check existing symbols. Ensure the symbol has been written at least once before attempting read/modify operations.

Upgrade

Version history

6.18.1latest on PyPI · released Jun 8, 2026

Audit

Dependencies

pandasrequiredCore functionality revolves around storing and retrieving Pandas DataFrames.

numpyrequiredFrequently used with Pandas DataFrames for data generation and manipulation.

Agent activity

69 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/man-group/arcticdb ↗

packagehttps://pypi.org/project/arcticdb/ ↗

homepagehttps://arcticdb.io ↗