ArcticDB_merge

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ArcticDB Merge¶

⚠️ Warning: The merge API is under development and subject to change. Additional features will be introduced in the future. The API is not subject to semver and can change in minor or patch releases. Stay tuned for updates! The API call at the moment is named merge_experimental but it will be changed to merge when the API becomes stable.

Motivation¶

The merge API offers a straightforward way for users to modify or insert specific rows in their data. Data providers often issue corrections for particular entries, and the merge functionality can efficiently apply these updates.

Example¶

Let's start with a quick example before exploring the details.

In this example, we have an ArcticDB library called prices that stores price data, and a symbol named daily_prices for daily prices. At some point, the data provider issued corrections for 2023-01-05, 2023-01-07, and 2023-01-19. We want to apply these corrections, modifying only the affected rows.

Begin by importing the necessary modules.

In [1]:

import pandas as pd
import arcticdb
import numpy as np
from IPython.display import display
from arcticdb.version_store.library import MergeStrategy, MergeAction

import pandas as pd import arcticdb import numpy as np from IPython.display import display from arcticdb.version_store.library import MergeStrategy, MergeAction

Next, create an ArcticDB instance and a library.

In [2]:

ac = arcticdb.Arctic("lmdb://merge_example")
lib = ac.get_library("prices", create_if_missing=True)

ac = arcticdb.Arctic("lmdb://merge_example") lib = ac.get_library("prices", create_if_missing=True)

Create an example prices DataFrame and store it in ArcticDB.

In [3]:

# Create example data
daily_prices = pd.DataFrame(
    data={
        "High": [102.5, 103.6, 101.7, 103.2, 104.8, 106.0, 108.9, 107.6, 109.3, 108.7, 
                 111.1, 113.5, 113.2, 115.0, 112.3, 115.9, 117.3, 118.6, 119.8, 119.1],
        "Low": [100.5, 101.8, 99.7, 100.9, 101.7, 103.4, 106.2, 104.8, 107.5, 106.3, 
                109.1, 110.8, 110.2, 111.7, 109.6, 112.7, 114.0, 115.4, 116.5, 116.2],
        "Volume": [1200, 1500, 900, 1400, 1600, 1900, 2500, 2300, 1800, 2100, 
                   2000, 2700, 2600, 3000, 2200, 3100, 2800, 3200, 3500, 3400],
    },
    index=pd.date_range(start="2023-01-01", periods=20, freq="D")
)
daily_prices

# Create example data daily_prices = pd.DataFrame( data={ "High": [102.5, 103.6, 101.7, 103.2, 104.8, 106.0, 108.9, 107.6, 109.3, 108.7, 111.1, 113.5, 113.2, 115.0, 112.3, 115.9, 117.3, 118.6, 119.8, 119.1], "Low": [100.5, 101.8, 99.7, 100.9, 101.7, 103.4, 106.2, 104.8, 107.5, 106.3, 109.1, 110.8, 110.2, 111.7, 109.6, 112.7, 114.0, 115.4, 116.5, 116.2], "Volume": [1200, 1500, 900, 1400, 1600, 1900, 2500, 2300, 1800, 2100, 2000, 2700, 2600, 3000, 2200, 3100, 2800, 3200, 3500, 3400], }, index=pd.date_range(start="2023-01-01", periods=20, freq="D") ) daily_prices

Out[3]:

	High	Low	Volume
2023-01-01	102.5	100.5	1200
2023-01-02	103.6	101.8	1500
2023-01-03	101.7	99.7	900
2023-01-04	103.2	100.9	1400
2023-01-05	104.8	101.7	1600
2023-01-06	106.0	103.4	1900
2023-01-07	108.9	106.2	2500
2023-01-08	107.6	104.8	2300
2023-01-09	109.3	107.5	1800
2023-01-10	108.7	106.3	2100
2023-01-11	111.1	109.1	2000
2023-01-12	113.5	110.8	2700
2023-01-13	113.2	110.2	2600
2023-01-14	115.0	111.7	3000
2023-01-15	112.3	109.6	2200
2023-01-16	115.9	112.7	3100
2023-01-17	117.3	114.0	2800
2023-01-18	118.6	115.4	3200
2023-01-19	119.8	116.5	3500
2023-01-20	119.1	116.2	3400

In [4]:

lib.write("daily_prices", daily_prices)

lib.write("daily_prices", daily_prices)

Out[4]:

VersionedItem(symbol='daily_prices', library='prices', data=n/a, version=2, metadata=None, host='LMDB(path=/home/vasil/Documents/source/ArcticDB/docs/mkdocs/docs/notebooks/merge_example)', timestamp=1771501596070866899)

Create the correction DataFrame.

In [5]:

daily_prices_correction = pd.DataFrame(
    data={
        "High":  [3000.0, 4000.0, 5000.0],
        "Low":   [1000.0, 2000.0, 3000.0],
        "Volume":[10000, 20000, 30000]
    },
    index=pd.DatetimeIndex([pd.Timestamp("2023-01-05"), pd.Timestamp("2023-01-07"), pd.Timestamp("2023-01-19")])
)
daily_prices_correction

daily_prices_correction = pd.DataFrame( data={ "High": [3000.0, 4000.0, 5000.0], "Low": [1000.0, 2000.0, 3000.0], "Volume":[10000, 20000, 30000] }, index=pd.DatetimeIndex([pd.Timestamp("2023-01-05"), pd.Timestamp("2023-01-07"), pd.Timestamp("2023-01-19")]) ) daily_prices_correction

Out[5]:

	High	Low	Volume
2023-01-05	3000.0	1000.0	10000
2023-01-07	4000.0	2000.0	20000
2023-01-19	5000.0	3000.0	30000

Perform the merge update using the correction data. Ensure that both daily_prices_correction and the stored data are sorted. By default, two rows in DataFrames with a datetime index are considered matching if their indexes are equal.

In [6]:

lib.merge_experimental("daily_prices", daily_prices_correction, strategy=MergeStrategy(matched="update", not_matched_by_target="do_nothing"))
print("Merged result")
display(lib.read("daily_prices").data)
print("Diff between merged and original data")
daily_prices.compare(lib.read("daily_prices").data, keep_equal=True)

lib.merge_experimental("daily_prices", daily_prices_correction, strategy=MergeStrategy(matched="update", not_matched_by_target="do_nothing")) print("Merged result") display(lib.read("daily_prices").data) print("Diff between merged and original data") daily_prices.compare(lib.read("daily_prices").data, keep_equal=True)

Merged result

	High	Low	Volume
2023-01-01	102.5	100.5	1200
2023-01-02	103.6	101.8	1500
2023-01-03	101.7	99.7	900
2023-01-04	103.2	100.9	1400
2023-01-05	3000.0	1000.0	10000
2023-01-06	106.0	103.4	1900
2023-01-07	4000.0	2000.0	20000
2023-01-08	107.6	104.8	2300
2023-01-09	109.3	107.5	1800
2023-01-10	108.7	106.3	2100
2023-01-11	111.1	109.1	2000
2023-01-12	113.5	110.8	2700
2023-01-13	113.2	110.2	2600
2023-01-14	115.0	111.7	3000
2023-01-15	112.3	109.6	2200
2023-01-16	115.9	112.7	3100
2023-01-17	117.3	114.0	2800
2023-01-18	118.6	115.4	3200
2023-01-19	5000.0	3000.0	30000
2023-01-20	119.1	116.2	3400

Diff between merged and original data

Out[6]:

	High		Low		Volume
	self	other	self	other	self	other
2023-01-05	104.8	3000.0	101.7	1000.0	1600	10000
2023-01-07	108.9	4000.0	106.2	2000.0	2500	20000
2023-01-19	119.8	5000.0	116.5	3000.0	3500	30000

Merge vs. Update¶

ArcticDB also provides an update method, which requires sorted input. The key difference is that update overwrites all data between the start and end of the input, potentially removing any rows that fall within those bounds and are not in the input.

In [7]:

lib.write("prices_update_example", daily_prices)
lib.update("prices_update_example", daily_prices_correction)
lib.read("prices_update_example").data

lib.write("prices_update_example", daily_prices) lib.update("prices_update_example", daily_prices_correction) lib.read("prices_update_example").data

Out[7]:

	High	Low	Volume
2023-01-01	102.5	100.5	1200
2023-01-02	103.6	101.8	1500
2023-01-03	101.7	99.7	900
2023-01-04	103.2	100.9	1400
2023-01-05	3000.0	1000.0	10000
2023-01-07	4000.0	2000.0	20000
2023-01-19	5000.0	3000.0	30000
2023-01-20	119.1	116.2	3400

In the example above, the input to update included only rows for 2023-01-05, 2023-01-07, and 2023-01-19. As a result, any data between 2023-01-05 and 2023-01-19 that was not present in the update input is omitted from the final result.

Merge: Semantics and Behavior¶

In this context, the data stored in ArcticDB is referred to as the target, while the input to merge is called the source.

Merge works by performing a join between target and source on a subset of columns, and updating target based on the strategy parameter. If target is a time series, the index will always be included among the join columns. This is done to ensure that ordered date-time indexes stay ordered after performing a merge. Otherwise the following would be possible

lib.write("sym", pd.DataFrame({"a": [1, 2, 3]}, index=pd.DatetimeIndex([pd.Timestamp(1), pd.Timestamp(2), pd.Timestamp(3)])))
lib.merge_experimental("sym", pd.DataFrame({"a": [2]}, index=pd.DatetimeIndex([pd.Timestamp(10)])), on=["a"])
print(lib.read("sym").data)
                               a
1970-01-01 00:00:00.000000001  1
1970-01-01 00:00:00.000000010  2
1970-01-01 00:00:00.000000003  3

Strategies¶

The strategy is a named tuple that defines how target will be modified. Its members can be either case-insensitive strings (update, insert, or do_nothing) or members of the MergeAction enum.

• matched specifies what to do when a row in source matches a row in target. Acceptable values are update and do_nothing.
• not_matched_by_target specifies what to do when a row in source does not match any row in target. Acceptable values are insert and do_nothing.

Acceptable combinations¶

• matched="update", not_matched_by_target="do_nothing"
• matched="update", not_matched_by_target="insert"
• matched="do_nothing", not_matched_by_target="insert"

Unacceptable combinations¶

• matched="do_nothing", not_matched_by_target="do_nothing" - nothing is going to happen
• matched="do_nothing"|"insert"|"update", not_matched_by_target="update" - cannot update a row that is not existing in the target
• matched="insert", not_matched_by_target="do_nothing"|"insert"|"update" - while technically possible it doesn't make sense to insert duplicates

Examples¶

• MergeStrategy(matched="update", not_matched_by_target="do_nothing") updates only the rows in target that match the selected columns. Rows that do not match remain unchanged, and new rows are not inserted. If a row in source matches multiple rows in target, all matching rows in target will be updated.

In [8]:

data_with_duplicates = pd.DataFrame(
    {"Bid": [100, 101, 102], "Ask": [101.1, 101, 102.5]},
    index=pd.DatetimeIndex([pd.Timestamp("2025-01-01 08:00:00"), pd.Timestamp("2025-01-01 08:00:00"), pd.Timestamp("2025-01-01 09:00:00")])
)
lib.write("merge_update_with_duplicates", data_with_duplicates)
print("Original data")
display(lib.read("merge_update_with_duplicates").data)
lib.merge_experimental(
    "merge_update_with_duplicates",
    pd.DataFrame(
        {"Bid": [105, 102], "Ask": [105.3, 102]},
        index=pd.DatetimeIndex([pd.Timestamp("2025-01-01 08:00:00"), pd.Timestamp("2025-02-01 08:00:00")])
    ),
    MergeStrategy(matched="update", not_matched_by_target="do_nothing")
)
print("Data after merge")
display(lib.read("merge_update_with_duplicates").data)

data_with_duplicates = pd.DataFrame( {"Bid": [100, 101, 102], "Ask": [101.1, 101, 102.5]}, index=pd.DatetimeIndex([pd.Timestamp("2025-01-01 08:00:00"), pd.Timestamp("2025-01-01 08:00:00"), pd.Timestamp("2025-01-01 09:00:00")]) ) lib.write("merge_update_with_duplicates", data_with_duplicates) print("Original data") display(lib.read("merge_update_with_duplicates").data) lib.merge_experimental( "merge_update_with_duplicates", pd.DataFrame( {"Bid": [105, 102], "Ask": [105.3, 102]}, index=pd.DatetimeIndex([pd.Timestamp("2025-01-01 08:00:00"), pd.Timestamp("2025-02-01 08:00:00")]) ), MergeStrategy(matched="update", not_matched_by_target="do_nothing") ) print("Data after merge") display(lib.read("merge_update_with_duplicates").data)

Original data

	Bid	Ask
2025-01-01 08:00:00	100	101.1
2025-01-01 08:00:00	101	101.0
2025-01-01 09:00:00	102	102.5

Data after merge

	Bid	Ask
2025-01-01 08:00:00	105	105.3
2025-01-01 08:00:00	105	105.3
2025-01-01 09:00:00	102	102.5

In the example above, there is a row in source with the index value 2025-01-01 08:00:00 that matches two rows in target. Both matching rows in target are updated. A row in source that does not match any row in target is not inserted. Rows in target that do not match any row in source remain unchanged.

The elements of MergeStrategy can also be values of the MergeAction enum.

In [9]:

data_with_duplicates = pd.DataFrame(
    {"Bid": [100, 101, 102], "Ask": [101.1, 101, 102.5]},
    index=pd.DatetimeIndex([pd.Timestamp("2025-01-01 08:00:00"), pd.Timestamp("2025-01-01 08:00:00"), pd.Timestamp("2025-01-01 09:00:00")])
)
lib.write("merge_update_with_duplicates", data_with_duplicates)
print("Original data")
display(lib.read("merge_update_with_duplicates").data)
lib.merge_experimental(
    "merge_update_with_duplicates",
    pd.DataFrame(
        {"Bid": [105, 102], "Ask": [105.3, 102]},
        index=pd.DatetimeIndex([pd.Timestamp("2025-01-01 08:00:00"), pd.Timestamp("2025-02-01 08:00:00")])
    ),
    MergeStrategy(MergeAction.UPDATE, not_matched_by_target=MergeAction.DO_NOTHING)
)
print("Data after merge")
display(lib.read("merge_update_with_duplicates").data)

data_with_duplicates = pd.DataFrame( {"Bid": [100, 101, 102], "Ask": [101.1, 101, 102.5]}, index=pd.DatetimeIndex([pd.Timestamp("2025-01-01 08:00:00"), pd.Timestamp("2025-01-01 08:00:00"), pd.Timestamp("2025-01-01 09:00:00")]) ) lib.write("merge_update_with_duplicates", data_with_duplicates) print("Original data") display(lib.read("merge_update_with_duplicates").data) lib.merge_experimental( "merge_update_with_duplicates", pd.DataFrame( {"Bid": [105, 102], "Ask": [105.3, 102]}, index=pd.DatetimeIndex([pd.Timestamp("2025-01-01 08:00:00"), pd.Timestamp("2025-02-01 08:00:00")]) ), MergeStrategy(MergeAction.UPDATE, not_matched_by_target=MergeAction.DO_NOTHING) ) print("Data after merge") display(lib.read("merge_update_with_duplicates").data)

Original data

	Bid	Ask
2025-01-01 08:00:00	100	101.1
2025-01-01 08:00:00	101	101.0
2025-01-01 09:00:00	102	102.5

Data after merge

	Bid	Ask
2025-01-01 08:00:00	105	105.3
2025-01-01 08:00:00	105	105.3
2025-01-01 09:00:00	102	102.5

• MergeStrategy(matched="do_nothing", not_matched_by_target="insert") inserts rows from source that do not match the selected columns.
• MergeStrategy(matched="update", not_matched_by_target="insert") updates target by modifying matching rows and inserting non-matching rows from source.

When the strategy involves updating on match, a row in target must not be matched by more than one row in source, as this would create ambiguity about which values to use.

The on Parameter¶

The on parameter allows specifying additional columns to use for matching. When on is provided, a row in source matches a row in target only when both the index and all columns listed in on have equal values. The parameter can contain any column that appear both in source and target, there aren't any restrictions on the number, dtype or order of the columns.

Important: For date-time indexed data, the index is always included in matching and cannot be excluded.

Example: Matching on Additional Columns¶

Consider a scenario where we have intraday price data with multiple exchanges. Corrections arrive for specific exchange/timestamp combinations. Without on, all rows sharing the same timestamp would match. With on=["Exchange"], only the row with the correct timestamp and exchange will be updated.

In [10]:

# Target: price data from multiple exchanges at the same timestamps
multi_exchange_data = pd.DataFrame(
    {
        "Exchange": ["NYSE", "LSE", "NYSE", "LSE"],
        "Price": [100.0, 200.0, 110.0, 210.0],
    },
    index=pd.DatetimeIndex([
        pd.Timestamp("2025-01-01 09:00"),
        pd.Timestamp("2025-01-01 09:00"),
        pd.Timestamp("2025-01-01 10:00"),
        pd.Timestamp("2025-01-01 10:00"),
    ])
)
lib.write("multi_exchange", multi_exchange_data)
print("Original data")
display(lib.read("multi_exchange").data)

# Source: correction only for NYSE at 09:00
correction = pd.DataFrame(
    {
        "Exchange": ["NYSE"],
        "Price": [999.0],
    },
    index=pd.DatetimeIndex([pd.Timestamp("2025-01-01 09:00")])
)
print("Correction data")
display(correction)

# Merge using on=["Exchange"] so only the NYSE row at 09:00 is updated
lib.merge_experimental(
    "multi_exchange",
    correction,
    strategy=MergeStrategy(matched="update", not_matched_by_target="do_nothing"),
    on=["Exchange"]
)
print("Data after merge with on=['Exchange']")
display(lib.read("multi_exchange").data)

# Target: price data from multiple exchanges at the same timestamps multi_exchange_data = pd.DataFrame( { "Exchange": ["NYSE", "LSE", "NYSE", "LSE"], "Price": [100.0, 200.0, 110.0, 210.0], }, index=pd.DatetimeIndex([ pd.Timestamp("2025-01-01 09:00"), pd.Timestamp("2025-01-01 09:00"), pd.Timestamp("2025-01-01 10:00"), pd.Timestamp("2025-01-01 10:00"), ]) ) lib.write("multi_exchange", multi_exchange_data) print("Original data") display(lib.read("multi_exchange").data) # Source: correction only for NYSE at 09:00 correction = pd.DataFrame( { "Exchange": ["NYSE"], "Price": [999.0], }, index=pd.DatetimeIndex([pd.Timestamp("2025-01-01 09:00")]) ) print("Correction data") display(correction) # Merge using on=["Exchange"] so only the NYSE row at 09:00 is updated lib.merge_experimental( "multi_exchange", correction, strategy=MergeStrategy(matched="update", not_matched_by_target="do_nothing"), on=["Exchange"] ) print("Data after merge with on=['Exchange']") display(lib.read("multi_exchange").data)

Original data

	Exchange	Price
2025-01-01 09:00:00	NYSE	100.0
2025-01-01 09:00:00	LSE	200.0
2025-01-01 10:00:00	NYSE	110.0
2025-01-01 10:00:00	LSE	210.0

Correction data

	Exchange	Price
2025-01-01 09:00:00	NYSE	999.0

Data after merge with on=['Exchange']

	Exchange	Price
2025-01-01 09:00:00	NYSE	999.0
2025-01-01 09:00:00	LSE	200.0
2025-01-01 10:00:00	NYSE	110.0
2025-01-01 10:00:00	LSE	210.0

Only the NYSE row at 2025-01-01 09:00 was updated. The LSE row at the same timestamp and all rows at 10:00 remain unchanged.

Without on=["Exchange"], both rows at 09:00 (NYSE and LSE) would have been updated, because both match on the index alone.

Equality Semantics for NaN and None in on Columns¶

• Float columns: NaN is considered equal to NaN. This differs from the IEEE 754 standard where NaN != NaN.
• String columns: None and NaN are indistinguishable. All of the following are treated as matches: NaN == NaN, NaN == None, None == NaN, None == None.

Example: NaN Matching in Float Columns¶

In [11]:

# Target: data with NaN in a float column
float_nan_data = pd.DataFrame(
    {
        "Sector": [np.nan, 1.0, np.nan, 2.0],
        "Price": [100.0, 200.0, 300.0, 400.0],
    },
    index=pd.DatetimeIndex([
        pd.Timestamp("2025-01-01"),
        pd.Timestamp("2025-01-01"),
        pd.Timestamp("2025-01-02"),
        pd.Timestamp("2025-01-02"),
    ])
)
lib.write("float_nan_example", float_nan_data)
print("Original data")
display(lib.read("float_nan_example").data)

# Source: correction for the NaN sector row at 2025-01-01
correction_nan = pd.DataFrame(
    {
        "Sector": [np.nan],
        "Price": [999.0],
    },
    index=pd.DatetimeIndex([pd.Timestamp("2025-01-01")])
)
print("Correction data (NaN in Sector)")
display(correction_nan)

# NaN == NaN in float columns, so the NaN row at 2025-01-01 will match
lib.merge_experimental(
    "float_nan_example",
    correction_nan,
    strategy=MergeStrategy(matched="update", not_matched_by_target="do_nothing"),
    on=["Sector"]
)
print("Data after merge - NaN matched NaN in the float Sector column")
display(lib.read("float_nan_example").data)

# Target: data with NaN in a float column float_nan_data = pd.DataFrame( { "Sector": [np.nan, 1.0, np.nan, 2.0], "Price": [100.0, 200.0, 300.0, 400.0], }, index=pd.DatetimeIndex([ pd.Timestamp("2025-01-01"), pd.Timestamp("2025-01-01"), pd.Timestamp("2025-01-02"), pd.Timestamp("2025-01-02"), ]) ) lib.write("float_nan_example", float_nan_data) print("Original data") display(lib.read("float_nan_example").data) # Source: correction for the NaN sector row at 2025-01-01 correction_nan = pd.DataFrame( { "Sector": [np.nan], "Price": [999.0], }, index=pd.DatetimeIndex([pd.Timestamp("2025-01-01")]) ) print("Correction data (NaN in Sector)") display(correction_nan) # NaN == NaN in float columns, so the NaN row at 2025-01-01 will match lib.merge_experimental( "float_nan_example", correction_nan, strategy=MergeStrategy(matched="update", not_matched_by_target="do_nothing"), on=["Sector"] ) print("Data after merge - NaN matched NaN in the float Sector column") display(lib.read("float_nan_example").data)

Original data

	Sector	Price
2025-01-01	NaN	100.0
2025-01-01	1.0	200.0
2025-01-02	NaN	300.0
2025-01-02	2.0	400.0

Correction data (NaN in Sector)

	Sector	Price
2025-01-01	NaN	999.0

Data after merge - NaN matched NaN in the float Sector column

	Sector	Price
2025-01-01	NaN	999.0
2025-01-01	1.0	200.0
2025-01-02	NaN	300.0
2025-01-02	2.0	400.0

The NaN value in the Sector column matched between source and target at 2025-01-01, so only that row was updated. The row with Sector=1.0 at the same timestamp was not affected.

Example: None and NaN Are Indistinguishable in String Columns¶

In string columns, None and NaN are treated as the same value. A source row with None in a string on column will match a target row with NaN in the same column (and vice versa).

In [12]:

# Target: data with NaN in a string column
string_none_data = pd.DataFrame(
    {
        "Exchange": [np.nan, "LSE", None, "LSE"],
        "Price": [100.0, 200.0, 300.0, 400.0],
    },
    index=pd.DatetimeIndex([
        pd.Timestamp("2025-01-01"),
        pd.Timestamp("2025-01-01"),
        pd.Timestamp("2025-01-02"),
        pd.Timestamp("2025-01-02"),
    ])
)
lib.write("string_none_example", string_none_data)
print("Original data (NaN at 2025-01-01, None at 2025-01-02 in Exchange)")
display(lib.read("string_none_example").data)

# Source: correction with None in the string column - will match both NaN and None in target
correction_none = pd.DataFrame(
    {
        "Exchange": [None],
        "Price": [999.0],
    },
    index=pd.DatetimeIndex([pd.Timestamp("2025-01-01")])
)
print("Correction data (None in Exchange)")
display(correction_none)

# None in source matches NaN in target for string columns
lib.merge_experimental(
    "string_none_example",
    correction_none,
    strategy=MergeStrategy(matched="update", not_matched_by_target="do_nothing"),
    on=["Exchange"]
)
print("Data after merge - None in source matched NaN in target's string column")
display(lib.read("string_none_example").data)

# Target: data with NaN in a string column string_none_data = pd.DataFrame( { "Exchange": [np.nan, "LSE", None, "LSE"], "Price": [100.0, 200.0, 300.0, 400.0], }, index=pd.DatetimeIndex([ pd.Timestamp("2025-01-01"), pd.Timestamp("2025-01-01"), pd.Timestamp("2025-01-02"), pd.Timestamp("2025-01-02"), ]) ) lib.write("string_none_example", string_none_data) print("Original data (NaN at 2025-01-01, None at 2025-01-02 in Exchange)") display(lib.read("string_none_example").data) # Source: correction with None in the string column - will match both NaN and None in target correction_none = pd.DataFrame( { "Exchange": [None], "Price": [999.0], }, index=pd.DatetimeIndex([pd.Timestamp("2025-01-01")]) ) print("Correction data (None in Exchange)") display(correction_none) # None in source matches NaN in target for string columns lib.merge_experimental( "string_none_example", correction_none, strategy=MergeStrategy(matched="update", not_matched_by_target="do_nothing"), on=["Exchange"] ) print("Data after merge - None in source matched NaN in target's string column") display(lib.read("string_none_example").data)

Original data (NaN at 2025-01-01, None at 2025-01-02 in Exchange)

	Exchange	Price
2025-01-01	NaN	100.0
2025-01-01	LSE	200.0
2025-01-02	None	300.0
2025-01-02	LSE	400.0

Correction data (None in Exchange)

	Exchange	Price
2025-01-01	None	999.0

Data after merge - None in source matched NaN in target's string column

20260219 13:46:36.494286 1173784 I arcticdb | Column Exchange does not have non null elements.

	Exchange	Price
2025-01-01	None	999.0
2025-01-01	LSE	200.0
2025-01-02	None	300.0
2025-01-02	LSE	400.0

The target had NaN in the Exchange column at 2025-01-01 and None at 2025-01-02. The source contained None in Exchange at 2025-01-01. Because None and NaN are indistinguishable in string columns, the source row matched the target row at 2025-01-01 (where the target had NaN). The row at 2025-01-02 was not affected because the index did not match.

Limitations¶

Current implementation has a few limitations

1. Both target and source must have a sorted pd.DatetimeIndex.
2. The library must use a static schema, and both source and target must share the same schema.
3. The only supported strategy is MergeStrategy(matched="update", not_matched_by_target="do_nothing").

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