Datatype matrix
Below table shows supported data types across PostgreSQL, 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 toNUMBER(38,20)
.numeric
with precision OR scale which is beyond 38 and 37 is mapped toNUMBER(38, 20)
.numeric
with precision AND scale within the above limits is mapped toNUMBER(precision, scale)
.
For BigQuery, we map PostgreSQL’s numeric
type as follows:
numeric
with no specified precision and scale is mapped toBIGNUMERIC(38,20)
.numeric
with precision OR scale which is beyond 38 and 37 respectively, is mapped toBIGNUMERIC(38, 20)
.numeric
with precision AND scale within the above limits is mapped toBIGNUMERIC(precision, scale)
.
For Clickhouse, we map PostgreSQL’s numeric
type as follows:
numeric
with no specified precision and scale is mapped toDecimal(76,38)
.numeric
with precision OR scale which is beyond 76 and 38 respectively, is mapped toDecimal(76,38)
.numeric
with precision AND scale within the above limits is mapped toDecimal(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.