I am trying to create a simple hudi table with **MERGE\_ON\_READ** table type.
After executing the code still in *hoodie.properties* file I see **hoodie.table.type=COPY\_ON\_WRITE**

Am I missing something here ?

**Jupyter Notebook for this code:** <https://github.com/sannidhiteredesai/spark/blob/master/hudi_acct.ipynb>

```
hudi_options = {
    "hoodie.table.name": "hudi_acct",
    "hoodie.table.type": "MERGE_ON_READ",
    "hoodie.datasource.write.operation": "upsert",
    "hoodie.datasource.write.recordkey.field": "acctid",
    "hoodie.datasource.write.precombine.field": "ts",
    "hoodie.datasource.write.partitionpath.field": "date",
    "hoodie.datasource.write.hive_style_partitioning": "true",
    "hoodie.upsert.shuffle.parallelism": 8,
    "hoodie.insert.shuffle.parallelism": 8,
}

input_df = spark.createDataFrame(
    [
        (100, "2015-01-01", "2015-01-01T13:51:39.340396Z", 10),
        (101, "2015-01-01", "2015-01-01T12:14:58.597216Z", 10),
        (102, "2015-01-01", "2015-01-01T13:51:40.417052Z", 10),
        (103, "2015-01-01", "2015-01-01T13:51:40.519832Z", 10),
        (104, "2015-01-02", "2015-01-01T12:15:00.512679Z", 10),
        (104, "2015-01-02", "2015-01-01T12:15:00.512679Z", 10),
        (104, "2015-01-02", "2015-01-02T12:15:00.512679Z", 20),
        (105, "2015-01-02", "2015-01-01T13:51:42.248818Z", 10),
    ],
    ("acctid", "date", "ts", "deposit"),
)

# INSERT
(
    input_df.write.format("org.apache.hudi")
    .options(**hudi_options)
    .mode("append")
    .save(hudi_dataset)
)

update_df = spark.createDataFrame(
    [(100, "2015-01-01", "2015-01-01T13:51:39.340396Z", 20)],
    ("acctid", "date", "ts", "deposit"))

# UPDATE
(
    update_df.write.format("org.apache.hudi")
    .options(**hudi_options)
    .mode("append")
    .save(hudi_dataset)
)

```

**Edit:** After execution of above code I see 2 parquet files created in the date=2015-01-01 partition. On reading the 2nd parquet file I was expecting to get only the updated 1 record, but I can see all other records in that partition as well.