Fine Tuning

This chapter is dedicated to advanced settings and features provided by DEKER™.


In addition to the URI parameter Client accepts several options, that you may want or need to tune. All of them shall be explicitly passed as keyword parameters, none of them is positional.


DEKER™ creates its own ThreadPoolExecutor instance for working with VArray. By default, this parameter is None. You may want to use your own ThreadPoolExecutor (or some custom executor, based on ThreadPoolExecutor) instance. In this case DEKER™ will use the passed one:

from deker import Client

client = Client(uri, executor=<your_executor_instance>)


No executor is initialized and used if you work with a Collection of Array. The executor, passed by you, will be ignored.


When Client is closed your executor will not be shut down, you shall do it manually.


This is a parameter for the native DEKER™ executor mentioned above.

By default, it is None and in this case DEKER™ uses the maximum number of threads from the formula, provided by Python 3.9 documentation : cpu_count() + 4.

You may increase or reduce it, if you need:

from deker import Client

client = Client(uri, workers=8)


DEKER™ uses its own file locking mechanisms for different operations, one of which is for writing. With write_lock_timeout you can modify an amount of seconds during which a parallel writing process waits for the release of the locked file:

from deker import Client

client = Client(uri, write_lock_timeout=120)

The default is 60 seconds. The units are immutable and only int is accepted.


While the parallel writing process waits for the lock release, it sleeps for some time and then checks the state of the lock. You can adjust its sleeping time in seconds:

from deker import Client

client = Client(uri, write_lock_check_interval=5)

The default is 1 second. The units are immutable and only int is accepted.


All the DEKER™ objects (including private ones) have their own loggers. They are bound by the common logging level, which defaults to "ERROR". If you need, you may change it at Client init:

from deker import Client

client = Client(uri, loglevel="INFO")

If you need to change it on the fly, you may use the following function:

from deker.log import set_logging_level

set_logging_level("INFO")  # now DEKER™ logs starting from "INFO" level


This parameter is used for the early run time break in case of potential memory overflow.

DEKER™ operates big amounts of data, and you may be unaware that your machine will probably run out of memory. For example, NumPy shall raise _ArrayMemoryError if you do something like this:

>>> import numpy as np

>>> np.random.random((100000, 100000))
# numpy.core._exceptions._ArrayMemoryError: Unable to allocate 74.5 GiB
# for an array with shape (100000, 100000) and data type float64

As DEKER™ is lazy, you shall be warned about such problems beforehand. For that purpose, DEKER™ checks the memory limits when it is creating:

  • Collection

  • Subset or VSubset

  • xarray.DataArray from a Subset or a VSubset

By default DEKER™ is limited to your total virtual memory size (i.e. total amount of RAM plus swap size). For example, you have 16 GB of RAM and 2 GB of swap. Thus, DEKER™ is limited to 18 GB of memory by default. But usually a machine is already using some parts of these memory for other processes. So your current available free memory is always lower than the total one.

DEKER™ compares its limits with your current available free memory (RAM + swap) and chooses the minimal one of them. Than it compares the result with the requested shape size. In case your request requires too much memory or you are trying to create a Collection with a schema, which may cause a memory overflow in future, DekerMemoryError will be immediately raised.

You can lower the default value by passing a certain number of bytes or by passing a human readable representation of kilobytes, megabytes, gigabytes ot terabytes, for example: "1024K", "512M", "8G", "1T":

from deker import Client

client = Client(uri, memory_limit="4G")  # 4 gigabytes
client = Client(uri, memory_limit=4096)  # 4096 bytes

Only integers are acceptable for both of bytes and human representation. Capitalization of units suffix is ignored: "1024k", "512m", "8g", "1t" will work.


You definitely may want to use it in Docker.

If you set a memory limit to your container, you’d better limit DEKER™ to the same value. Otherwise your container may be killed because of memory overflow.


Currently deker has 3 places, where memory check, described in memory_limit:

  • On collection creation via client.create_collection()

  • On getting subset e.g array[:]

  • On reading array as XArray e.g array[:].read_xarray()

While the last two prevent memory overflow and are required, sometimes you may need to be able to skip the first one

You can do so by providing skip_collection_create_memory_check=True as argument to the Client constructor

HDF5 Options


If you are new to HDF5, please, refer to the HDF5 official documentation

Very briefly, HDF5 is a data model, library, and file format for storing and managing data. It supports an unlimited variety of data types, and is designed for flexible and efficient I/O and for high volume and complex data. This format offers a big number of special tuning options. We will talk about chunks and data compression.

DEKER™ deker-local-adapters plugin has its default implementation of working with this format. It depends on two packages: h5py_ and hdf5plugin_ which provide a Python interface for HDF5 binaries and a pack of compression filters.

DEKER™ applies chunks and compression options to all of the files within one collection. As long as you do not interact directly with the files and low-level interfaces, DEKER™ provides special types for these options usage. Your settings are stored in the collection metadata. When you invoke a Collection, they are recovered and ready to be applied to your data. But they have to make a trip from the collection metadata to the final data, that’s why we need HDF5Options and HDF5CompressionOpts objects.


Chunks and compression options are applied to your dataset within HDF5 file when the data is inserted or updated. When reading, HDF5 file already knows how to manage its chunked and/or compressed contents properly.

First of all, let’s prepare a collection schema once again:

from datetime import datetime, timedelta

from deker import (

dimensions = [
        scale=Scale(start_value=90.0, step=-1.0, name="lat")
        scale=Scale(start_value=-180.0, step=1.0, name="lon")
        labels=["temperature", "humidity", "pressure", "wind_speed"]

attributes = [
    AttributeSchema(name="dt", dtype=datetime, primary=True),
    AttributeSchema(name="tm", dtype=int, primary=False),

array_schema = ArraySchema(
    dtype=float,  # will be converted and saved as numpy.float64
    # fill_value is not passed - will be numpy.nan


Correct data chunking may increase your performance. It makes your data split in smaller equal pieces. When you read data from a chunk, HDF5-file opens and caches it. The next reading of the same pattern will be much faster as it will be captured not from the storage, but from the cache.

A HDF5-file may have no chunks options or be chunked either manually or automatically.


Study HDF5 chunking manual to understand chunks better.

DEKER™ allows you to use all the 3 options.

Chunks options are set to None by default.

from deker import Client

with Client("file:///tmp/deker") as client:
   client.create_collection("weather", array_schema)

When you create an Array, its file is one big chunk.

If you set chunks to True, HDF5-file will automatically determine a chunk size with its own algorithm, basing on the shape of your Array:

from deker import Client, HDF5Options

with Client("file:///tmp/deker") as client:

You will never know the final chunk size, but be sure that your data is chunked now.

If you need to adjust it, you may set it manually. It shall be a tuple of integers. The size of the tuple shall be equal to your Array shape. Its values shall divide your dimensions without remainders:

from deker import Client, HDF5Options

chunks = (1, 181, 36, 4)

# schema shape is (24, 181, 360, 4)
# (24, 181, 360, 4) / (1, 181, 36, 4) = (24.0, 1.0, 10.0, 1.0) - no remainders

with Client("file:///tmp/deker") as client:

Here we chunked our data into pieces, each of which will contain 1 hour, 181 y points (because 181 is a natural number and is divisible only by itself or 1), 36 x points and the full scope of weather layers. If you need to read some data, which is kept in one or several chunks, the file will not affect other chunks, but it will open and cache the correspondent ones.


The best way to decide on chunk size is your the most frequently used reading pattern.


To prevent a lack of the disc space for your data, you can compress it with different filters, supported by HDF5 and provided by h5py and hdf5plugin packages.

There are several default filters, set in h5py and a pack of the most popular filters, brought by hdf5plugin.

Default filters:

  • GZip

  • Lzf

  • SZip

Custom filters, brought by hdf5plugin:

  • Bitshuffle

  • Blosc

  • BZip2

  • FciDecomp

  • LZ4

  • SZ

  • SZ3

  • Zfp

  • Zstd


The data is compressed chunk by chunk. If you use compression without indicating a chunk size, it will be automatically set to True and calculated by the inner HDF5 algorythm.

The default filters shall be used as follows:

from deker import Client, HDF5Options, HDF5CompressionOpts

with Client("file:///tmp/deker") as client:
   compression=HDF5CompressionOpts(compression="gzip", compression_opts=9),
   options = HDF5Options(compression_opts=compression)

The custom filters shall be instantiated and passed to HDF5CompressionOpts as a mapping:

with Client("file:///tmp/deker") as client:
   options = HDF5Options(chunks=(1, 181, 36, 4), compression_opts=compression)


Dive into compression options at h5py filter pipeline, hdf5plugin docs and HDF5 compression manual.