parisni opened a new issue, #6068:
URL: https://github.com/apache/hudi/issues/6068
hudi 0.11.1
spark 3.2.1
-----------
I have a huge performance drop when disabling metadata table at read time.
Here is a reproductible example with 2k partitions. (spotted in production
with 40k partitions)
```
basePath = "/tmp/test_table"
df = spark.range(1,2000).selectExpr("id", "id as part", "id as combine")
hudi_options = {
"hoodie.table.name": "test_table",
"hoodie.datasource.write.recordkey.field": "id",
"hoodie.datasource.write.partitionpath.field": "part",
"hoodie.datasource.write.table.name": "test_table",
"hoodie.datasource.write.operation": "bulk_insert",
"hoodie.datasource.write.precombine.field": "combine",
"hoodie.datasource.write.keygenerator.class":
"org.apache.hudi.keygen.ComplexKeyGenerator",
"hoodie.datasource.write.hive_style_partitioning": "true",
"hoodie.datasource.hive_sync.enable": "false",
"hoodie.datasource.write.keygenerator.class":
"org.apache.hudi.keygen.ComplexKeyGenerator",
"hoodie.metadata.enable": "true",
}
(df.write.format("hudi").options(**hudi_options).mode("overwrite").save(basePath))
```
Then try both (restating spark between two tests)
```
spark.read.format("hudi").option("hoodie.metadata.enable","true").load(basePath).filter("part=1").show()
spark.read.format("hudi").option("hoodie.metadata.enable","false").load(basePath).filter("part=1").show()
```
the former is fast, while the later, is as slow as reading the whole table
(no partition prunning)
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