mwa28 opened a new issue, #2138:
URL: https://github.com/apache/iceberg-python/issues/2138
### Apache Iceberg version
0.9.0
### Please describe the bug 🐞
Hello,
I am trying out the new upsert method on a table created as follows on the
AWS Glue Catalog :
```python
catalog = load_catalog("glue", **{"type": "glue"})
schema = Schema(
NestedField(1, "dt_insert", StringType(), required=True),
NestedField(2, "controller_id", StringType(), required=True),
NestedField(3, "timestamp", StringType(), required=True),
NestedField(4, "data_type", StringType(), required=True),
NestedField(5, "parameter", StringType(), required=True),
NestedField(6, "value", StringType(), required=True),
NestedField(7, "data_source", StringType(), required=True),
NestedField(8, "unique_id", StringType(), required=True),
NestedField(9, "dt_import_utc", StringType(), required=True),
identifier_field_ids=[8], # 'unique_id' is the primary key
)
catalog.create_table(
identifier=<database>.<table_name>,
schema=schema,
partition_spec=PartitionSpec(
PartitionField(
source_id=9,
field_id=1000,
transform=IdentityTransform(),
name="dt_import_utc",
),
),
sort_order=SortOrder(
SortField(source_id=2), # controller_id
SortField(source_id=4), # data_type
SortField(source_id=3), # dt_insert
SortField(source_id=1), # timestamp
),
location="s3://bucket/prefix/catalog",
properties={
"write.format.default" : "parquet",
"write.target-file-size-bytes" : 134217728, # 128 MB
"write.metadata.delete-after-commit.enabled" : True,
"write.metadata.previous-versions-max" : 5
})
```
`unique_id` is a the merged string of columns 1, 2, 3, 4 and 5 with an
underscore.
My upsertion is a simple pandas dataframe being transformed in pyarrow for
upsertion.
```python
iceberg_table = catalog.load_table(table_name)
table = pa.Table.from_pydict(to_upsert,
schema=iceberg_table.schema().as_arrow())
iceberg_table.upsert(df=table, join_cols=["unique_id"])
```
With AWS Athena, a MERGE INTO statement takes about 3 seconds to run on the
table and scans 1.11MB of data before completion.
```sql
MERGE INTO <database>.<table> target
USING <database>.<temp_table> source
ON (target."unique_id" = source."unique_id")
WHEN MATCHED THEN
UPDATE SET "dt_insert" = source."dt_insert", "controller_id"
= source."controller_id", "timestamp" = source."timestamp", "data_type" =
source."data_type", "parameter" = source."parameter", "value" = source."value",
"data_source" = source."data_source", "dt_import_utc" = source."dt_import_utc",
"unique_id" = source."unique_id", "sort_id" = source."sort_id"
WHEN NOT MATCHED THEN
INSERT ("dt_insert", "controller_id", "timestamp",
"data_type", "parameter", "value", "data_source", "dt_import_utc", "unique_id",
"sort_id")
VALUES (source."dt_insert", source."controller_id",
source."timestamp", source."data_type", source."parameter", source."value",
source."data_source", source."dt_import_utc", source."unique_id",
source."sort_id")
```
(statement generated by using the `to_iceberg` from `awswrangler`)
Meanwhile when i try with pyiceberg upsert, it is using more than 10240MB. I
am running on AWS lambda and it is causing an out of memory error.
I have no issue with the `append()` function, it completes fairly quickly
but it seems that the upsert needs further optimization to be able to
efficiently retrieve only relevant data.
Current table size is at 18.5GB for both Athena based statement and
pyiceberg upsert function call.
### Willingness to contribute
- [ ] I can contribute a fix for this bug independently
- [ ] I would be willing to contribute a fix for this bug with guidance from
the Iceberg community
- [x] I cannot contribute a fix for this bug at this time
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