Syncari Python SDK
Syncari Python Synapse Development Kit or synapse-sdk
Helpers
syncari.helpers provides utilities for the common edge cases a synapse hits when
mapping an external system onto Syncari records: epoch-millisecond dates, fabricated
modified dates, flat-vs-nested payloads, sending every field, and CSV data.
All helpers are pure functions importable from syncari.helpers:
from syncari.helpers import (
to_epoch_millis, from_epoch_millis, now_epoch_millis,
seconds_to_millis, millis_to_seconds, fabricate_last_modified,
flatten, unflatten, get_in,
all_field_names, with_all_fields, build_record, build_record_from_connection,
csv_to_records, records_to_csv,
)
Dates and epoch millis
Syncari stores Record.lastModified and Record.createdAt as epoch milliseconds (UTC).
These helpers convert to and from that representation.
to_epoch_millis('2021-01-01T00:00:00Z') # 1609459200000
to_epoch_millis(datetime(2021, 1, 1)) # naive datetime is treated as UTC
from_epoch_millis(1609459200000) # datetime(2021, 1, 1, tzinfo=utc)
now_epoch_millis() # current time as epoch millis
seconds_to_millis(1609459200) # 1609459200000
to_epoch_millis accepts a datetime, a date, or an ISO-8601 string (a trailing Z is
accepted). It rejects integers on purpose, so that seconds are never silently mistaken for
millis; use seconds_to_millis for epoch seconds.
For systems that do not expose a real modified date, fabricate one. Only fields that are
None are filled, unless force=True:
fabricate_last_modified(records) # set lastModified to now where missing
fabricate_last_modified(records, when=1609459200000) # use a specific stamp
fabricate_last_modified(record, set_created=True) # also fill createdAt
Accepts a single Record or a list and returns the same shape.
Flatten and unflatten
For APIs that reject flat schemas (or, conversely, need a flat payload), repackage the
dictionary. Keys are joined with a separator (. by default).
flatten({'a': {'b': {'c': 1}}}) # {'a.b.c': 1}
unflatten({'a.b.c': 1}) # {'a': {'b': {'c': 1}}}
flatten(nested, sep='/') # custom separator
unflatten raises ValueError when a key would be both a leaf and a branch
(for example {'a': 1, 'a.b': 2}).
Limitations: only nested dicts are descended into (no list indices), and round-tripping is not safe when keys themselves contain the separator. Integer keys become strings.
To pluck a single nested value into a flat field (rather than flattening the whole dict),
use get_in. It returns default when any step along the path is missing, so it never
raises on absent keys:
row['value_amount'] = get_in(row, 'value.amount') # 100 -> flat key
row['value_currency'] = get_in(row, 'value.currency', default='USD')
owner_id = get_in(row, ['owner_id', 'id']) # list path also accepted
This fits connectors that return nested reference objects (e.g. {'owner_id': {'id': 42}})
where you want one inner value, not the whole subtree flattened.
Sending all fields
Build a record that carries every field, lifting id / watermark / created values out of the
raw dict by field name. The full values dict is preserved, so unmapped fields still travel
downstream.
record = build_record(
raw,
id_field='Id',
watermark_field='LastModifiedDate', # converted to epoch millis
created_field='CreatedDate',
name='contact', # Record.name = the entity/object name
)
# Use the field names already configured on the connection:
record = build_record_from_connection(raw, connection)
Record.name is normally the entity/object name ('contact', 'deal'), so pass it via
name. If a per-record label lives in the data instead, pass name_field='Name'; when both
are given, the name_field value (when present) overrides name.
build_record accepts watermark / created values as a datetime, date, ISO string, an int,
or an all-digit string (for example a CSV-sourced '1609459200000'); numeric values are
assumed to already be epoch millis. A blank or whitespace-only watermark / created value
(common in CSV) is treated as missing and leaves lastModified / createdAt unset rather
than raising.
with_all_fields projects a raw dict onto every field in the schema. All original keys are
kept; with fill_missing=True, schema fields absent from the dict are added as None:
with_all_fields(raw, schema, fill_missing=True)
all_field_names(schema) # ['Id', 'Name', ...]
CSV
records = csv_to_records(csv_text, id_field='Id')
csv_text = records_to_csv(records)
csv_to_records(text, delimiter=';') # custom delimiter
All CSV values are strings (CSV carries no types). Fully blank rows are skipped by default
(skip_blank=False keeps them); rows with more cells than headers have the extra unnamed
cells dropped rather than raising. A leading UTF-8 BOM is stripped so the first column name
is not corrupted (and id_field / name_field still match). When fieldnames is omitted,
records_to_csv uses the first-seen-order union of all record value keys.
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