Datatype matrix

Below table shows supported data types across PostgreSQL, ClickHouse, BigQuery and Snowflake.

Primitive Data Types

Source	Destinations
PostgreSQL	PostgreSQL	BigQuery	Snowflake	Clickhouse	ElasticSearch
smallint	smallint	INTEGER	INTEGER	Int16	long
integer	integer	INTEGER	INTEGER	Int32	long
bigint	bigint	INTEGER	INTEGER	Int64	long
float4	float4	FLOAT	FLOAT	Float32	float
double precision	double precision	FLOAT	FLOAT	Float64	float
boolean	bool	BOOLEAN	BOOLEAN	Bool	boolean
"char"	CHAR	STRING	STRING	FixedString(1)	text
varchar	varchar	STRING	STRING	String	text
date	date	DATE	DATE	Date	date
json	json	JSON	VARIANT	String	unnested subdocument
jsonb	jsonb	JSON	VARIANT	String	unnested subdocument
numeric	numeric	BIGNUMERIC	NUMBER	Decimal	text
text	text	STRING	STRING	String	text
timestamp	timestamp	TIMESTAMP	TIMESTAMP_NTZ	DateTime64(6)	date
timestamp with time zone	timestamp with time zone	TIMESTAMP	TIMESTAMP_TZ	DateTime64(6)	date
time	time	TIME	TIME	String	date
bit	bit	BYTES	BINARY	String	text
bit varying	varbit	BYTES	BINARY	String	text
bytea	bytea	BYTES	BINARY	String	text
geography	geography	GEOGRAPHY	GEOGRAPHY	String	Coming soon!
geometry	geometry	GEOGRAPHY	GEOMETRY	String	Coming soon!
inet	inet	STRING	STRING	String	text
macaddr	macaddr	STRING	STRING	String	text
cidr	cidr	STRING	STRING	String	text
hstore	hstore	JSON	VARIANT	String	Coming soon!
uuid	uuid	STRING	STRING	uuid	Coming soon!

Array Data Types

Source	Destinations
PostgreSQL Type	PostgreSQL	BigQuery	Snowflake	Clickhouse
ARRAY<INT2>	ARRAY<INT2>	ARRAY<INT>	VARIANT	Array<Int16>
ARRAY<INT4>	ARRAY<INT4>	ARRAY<INT>	VARIANT	Array<Int32>
ARRAY<INT8>	ARRAY<INT8>	ARRAY<INT>	VARIANT	Array<Int64>
ARRAY<FLOAT4>	ARRAY<FLOAT4>	ARRAY<FLOAT>	VARIANT	Array<Float32>
ARRAY<DOUBLE PRECISION>	ARRAY<DOUBLE PRECISION>	ARRAY<DOUBLE PRECISION>	VARIANT	Array<Float64>
ARRAY<BOOL>	ARRAY<BOOL>	ARRAY<BOOL>	VARIANT	Array<Bool>
ARRAY<VARCHAR>	ARRAY<VARCHAR>	ARRAY<STRING>	VARIANT	Array<String>
ARRAY<TEXT>	ARRAY<TEXT>	ARRAY<STRING>	VARIANT	Array<String>
ARRAY<DATE>	ARRAY<DATE>	ARRAY<DATE>	VARIANT	String
ARRAY<TIMESTAMP>	ARRAY<TIMESTAMP>	ARRAY<TIMESTAMP>	VARIANT	String
ARRAY<TIMESTAMPTZ>	ARRAY<TIMESTAMPTZ>	ARRAY<TIMESTAMP>	VARIANT	String

Design Choices

We recognise that there are various approaches to handling certain data types. Here are some decisions we’ve taken for PeerDB.

Numeric Type

For Snowflake, we map PostgreSQL’s numeric type as follows:

• numeric with no specified precision and scale is mapped to NUMBER(38,20).
• numeric with precision OR scale which is beyond 38 and 37 is mapped to NUMBER(38, 20).
• numeric with precision AND scale within the above limits is mapped to NUMBER(precision, scale).

For BigQuery, we map PostgreSQL’s numeric type as follows:

• numeric with no specified precision and scale is mapped to BIGNUMERIC(38,20).
• numeric with precision OR scale which is beyond 38 and 37 respectively, is mapped to BIGNUMERIC(38, 20).
• numeric with precision AND scale within the above limits is mapped to BIGNUMERIC(precision, scale).

For Clickhouse, we map PostgreSQL’s numeric type as follows:

• numeric with no specified precision and scale is mapped to Decimal(76,38).
  • Values with more than 38 fractional digits will be truncated to 38 fractional digits and log a warning.
  • Values with more than 38 integer digits will be set to 0 and log a warning.
• numeric with precision OR scale which is beyond 76 and 38 respectively, is mapped to Decimal(76,38).
• numeric with precision AND scale within the above limits is mapped to Decimal(precision, scale).

Geospatial Data

PeerDB detects invalid shapes (for example, a linestring with only one point) among PostGIS values it pulls, and writes them as null on the destination. We keep a log of this data and it can be retrieved if needed.

Valid geospatial data is written on BigQuery and Snowflake in Well-Known Text (WKT) format, while to PostgreSQL destinations it is written as it is received.

HStore Data

PeerDB writes HSTORE data as JSON on BigQuery. All intricaces of the HSTORE data type are preserved, such as:

• NULL values. Example: '"a"=>NULL' will be written as {"a":null}
• Empty keys. Example: '""=>1' will be written as {"":1}
• Overriding duplicate key values. Example: '"a"=>"1", "a"=>"2"' will be written as {"a":2}

To Snowflake, it is written as a VARIANT data type, although it is formatted as a JSON and can be queried as such - snowflake_hstore_column:key.

Nulls in BigQuery Arrays

PeerDB removes null values from BigQuery arrays. This is because BigQuery does not support null values in arrays during their insertion.

Elasticsearch

We currently rely on Elasticsearch dynamic mapping for data type mappings, so this mapping may not be accurate for all cases. We are working on enabling explicit mappings for Elasticsearch.