SparkR::gapply return less rows than expected - sparkr

Question

See the example below. I have a dataframe with 2 columns and 1000 rows. Z simple adds 10 to one of the columns using gapply, the output is another SparkDataFrame with 1000 rows -- that's good. newZ does the same, but if key==10, returns NULL.
I would have expected the output to have 999 rows. Why is it less than that?
library(SparkR)
SparkR::sparkR.session()
sdf=as.DataFrame(data.frame(x=1:1000,y=1),numPartitions=10)
Z=gapply(sdf,'x',function(key,d){
data.frame(x=key[[1]],newy=d$y+10)
},schema="x int, newy int")
count(Z)
# [1] 1000
newZ=gapply(sdf,'x',function(key,d){
if(as.integer(key[[1]])==10)return(NULL)
data.frame(x=key[[1]],newy=d$y+10)
},schema="x int, newy int")
count(newZ)
# [1] 993
Some spark config:
> sparkR.conf()
$eventLog.rolloverIntervalSeconds
[1] "3600"
$spark.akka.frameSize
[1] "256"
$spark.app.name
[1] "Databricks Shell"
$spark.databricks.cloudProvider
[1] "Azure"
$spark.databricks.clusterUsageTags.clusterMaxWorkers
[1] "12"
$spark.databricks.clusterUsageTags.clusterMetastoreAccessType
[1] "RDS_DIRECT"
$spark.databricks.clusterUsageTags.clusterMinWorkers
[1] "2"
$spark.databricks.clusterUsageTags.clusterPythonVersion
[1] "3"
$spark.databricks.clusterUsageTags.clusterResourceClass
[1] "Serverless"
$spark.databricks.clusterUsageTags.clusterScalingType
[1] "autoscaling"
$spark.databricks.clusterUsageTags.clusterTargetWorkers
[1] "2"
$spark.databricks.clusterUsageTags.clusterWorkers
[1] "2"
$spark.databricks.clusterUsageTags.driverNodeType
[1] "Standard_E8s_v3"
$spark.databricks.clusterUsageTags.enableElasticDisk
[1] "true"
$spark.databricks.clusterUsageTags.numPerClusterInitScriptsV2
[1] "1"
$spark.databricks.clusterUsageTags.sparkVersion
[1] "latest-stable-scala2.11"
$spark.databricks.clusterUsageTags.userProvidedRemoteVolumeCount
[1] "0"
$spark.databricks.clusterUsageTags.userProvidedRemoteVolumeSizeGb
[1] "0"
$spark.databricks.delta.multiClusterWrites.enabled
[1] "true"
$spark.databricks.driverNodeTypeId
[1] "Standard_E8s_v3"
$spark.databricks.r.cleanWorkspace
[1] "true"
$spark.databricks.workerNodeTypeId
[1] "Standard_DS13_v2"
$spark.driver.maxResultSize
[1] "4g"
$spark.eventLog.enabled
[1] "false"
$spark.executor.id
[1] "driver"
$spark.executor.memory
[1] "40658m"
$spark.hadoop.databricks.dbfs.client.version
[1] "v2"
$spark.hadoop.fs.s3a.connection.maximum
[1] "200"
$spark.hadoop.fs.s3a.multipart.size
[1] "10485760"
$spark.hadoop.fs.s3a.multipart.threshold
[1] "104857600"
$spark.hadoop.fs.s3a.threads.max
[1] "136"
$spark.hadoop.fs.wasb.impl.disable.cache
[1] "true"
$spark.hadoop.fs.wasbs.impl
[1] "shaded.databricks.org.apache.hadoop.fs.azure.NativeAzureFileSystem"
$spark.hadoop.fs.wasbs.impl.disable.cache
[1] "true"
$spark.hadoop.hive.server2.idle.operation.timeout
[1] "7200000"
$spark.hadoop.hive.server2.idle.session.timeout
[1] "900000"
$spark.hadoop.mapreduce.fileoutputcommitter.algorithm.version
[1] "2"
$spark.hadoop.parquet.memory.pool.ratio
[1] "0.5"
$spark.home
[1] "/databricks/spark"
$spark.logConf
[1] "true"
$spark.r.numRBackendThreads
[1] "1"
$spark.rdd.compress
[1] "true"
$spark.scheduler.mode
[1] "FAIR"
$spark.serializer.objectStreamReset
[1] "100"
$spark.shuffle.manager
[1] "SORT"
$spark.shuffle.memoryFraction
[1] "0.2"
$spark.shuffle.reduceLocality.enabled
[1] "false"
$spark.shuffle.service.enabled
[1] "true"
$spark.sql.catalogImplementation
[1] "hive"
$spark.sql.hive.convertCTAS
[1] "true"
$spark.sql.hive.convertMetastoreParquet
[1] "true"
$spark.sql.hive.metastore.jars
[1] "/databricks/hive/*"
$spark.sql.hive.metastore.version
[1] "0.13.0"
$spark.sql.parquet.cacheMetadata
[1] "true"
$spark.sql.parquet.compression.codec
[1] "snappy"
$spark.sql.ui.retainedExecutions
[1] "100"
$spark.sql.warehouse.dir
[1] "/user/hive/warehouse"
$spark.storage.blockManagerTimeoutIntervalMs
[1] "300000"
$spark.storage.memoryFraction
[1] "0.5"
$spark.streaming.driver.writeAheadLog.allowBatching
[1] "true"
$spark.task.reaper.enabled
[1] "true"
$spark.task.reaper.killTimeout
[1] "60s"
$spark.worker.cleanup.enabled
[1] "false"