in write(self, output, overwrite, stage_locally, _codec_spec, _partitions, _checkpoint_file)
/opt/conda/lib/python3.6/site-packages/hail/typecheck/check.py in wrapper(__original_func, *args, **kwargs)
575 def wrapper(__original_func, *args, **kwargs):
576 args_, kwargs_ = check_all(__original_func, args, kwargs, checkers, is_method=is_method)
--> 577 return __original_func(*args_, **kwargs_)
578
579 return wrapper
/opt/conda/lib/python3.6/site-packages/hail/matrixtable.py in write(self, output, overwrite, stage_locally, _codec_spec, _partitions, _checkpoint_file)
2542
2543 writer = ir.MatrixNativeWriter(output, overwrite, stage_locally, _codec_spec, _partitions, _partitions_type, _checkpoint_file)
-> 2544 Env.backend().execute(ir.MatrixWrite(self._mir, writer))
2545
2546 class _Show:
/opt/conda/lib/python3.6/site-packages/hail/backend/py4j_backend.py in execute(self, ir, timed)
108 raise HailUserError(message_and_trace) from None
109
--> 110 raise e
/opt/conda/lib/python3.6/site-packages/hail/backend/py4j_backend.py in execute(self, ir, timed)
84 # print(self._hail_package.expr.ir.Pretty.apply(jir, True, -1))
85 try:
---> 86 result_tuple = self._jhc.backend().executeEncode(jir, stream_codec)
87 (result, timings) = (result_tuple._1(), result_tuple._2())
88 value = ir.typ._from_encoding(result)
/cluster/spark/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py in __call__ (self, *args)
1255 answer = self.gateway_client.send_command(command)
1256 return_value = get_return_value(
-> 1257 answer, self.gateway_client, self.target_id, self.name)
1258
1259 for temp_arg in temp_args:
/opt/conda/lib/python3.6/site-packages/hail/backend/py4j_backend.py in deco(*args, **kwargs)
29 raise FatalError('%s\n\nJava stack trace:\n%s\n'
30 'Hail version: %s\n'
---> 31 'Error summary: %s' % (deepest, full, hail. __version__ , deepest), error_id) from None
32 except pyspark.sql.utils.CapturedException as e:
33 raise FatalError('%s\n\nJava stack trace:\n%s\n'
FatalError: ClassFormatError: Too many arguments in method signature in class file __C60175collect_distributed_array
Java stack trace:
org.apache.spark.SparkException: Job aborted due to stage failure: Task 9 in stage 413.0 failed 4 times, most recent failure: Lost task 9.3 in stage 413.0 (TID 1605, ip-10-60-4-132.eu-west-2.compute.internal, executor 2): java.lang.ClassFormatError: Too many arguments in method signature in class file __C60175collect_distributed_array
at java.lang.ClassLoader.defineClass1(Native Method)
at java.lang.ClassLoader.defineClass(ClassLoader.java:756)
at java.lang.ClassLoader.defineClass(ClassLoader.java:635)
at is.hail.asm4s.package$HailClassLoader$.liftedTree1$1(package.scala:253)
at is.hail.asm4s.package$HailClassLoader$.loadOrDefineClass(package.scala:249)
at is.hail.asm4s.ClassesBytes$$anonfun$load$1.apply(ClassBuilder.scala:65)
at is.hail.asm4s.ClassesBytes$$anonfun$load$1.apply(ClassBuilder.scala:63)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
at is.hail.asm4s.ClassesBytes.load(ClassBuilder.scala:63)
at is.hail.expr.ir.EmitClassBuilder$$anon$1.apply(EmitClassBuilder.scala:669)
at is.hail.expr.ir.EmitClassBuilder$$anon$1.apply(EmitClassBuilder.scala:662)
at is.hail.backend.BackendUtils$$anonfun$collectDArray$1$$anonfun$apply$1.apply(BackendUtils.scala:31)
at is.hail.backend.BackendUtils$$anonfun$collectDArray$1$$anonfun$apply$1.apply(BackendUtils.scala:30)
at is.hail.utils.package$.using(package.scala:638)
at is.hail.annotations.RegionPool.scopedRegion(RegionPool.scala:144)
at is.hail.backend.BackendUtils$$anonfun$collectDArray$1.apply(BackendUtils.scala:30)
at is.hail.backend.BackendUtils$$anonfun$collectDArray$1.apply(BackendUtils.scala:28)
at is.hail.backend.spark.SparkBackendComputeRDD.compute(SparkBackend.scala:730)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:123)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:2001)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1984)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1983)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1983)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:1033)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:1033)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1033)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2223)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2172)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2161)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:823)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2082)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2101)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2126)
at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:945)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
at org.apache.spark.rdd.RDD.collect(RDD.scala:944)
at is.hail.backend.spark.SparkBackend.parallelizeAndComputeWithIndex(SparkBackend.scala:286)
at is.hail.backend.BackendUtils.collectDArray(BackendUtils.scala:28)
at __C58896Compiled.__ m59284split_WriteMetadata_region33_121(Emit.scala)
at __C58896Compiled.__ m59284split_WriteMetadata_region11_125(Emit.scala)
at __C58896Compiled.__ m59284split_WriteMetadata(Emit.scala)
at __C58896Compiled.__ m59097split_Let(Emit.scala)
at __C58896Compiled.apply(Emit.scala)
at is.hail.expr.ir.CompileAndEvaluate$$anonfun$_apply$1.apply$mcV$sp(CompileAndEvaluate.scala:57)
at is.hail.expr.ir.CompileAndEvaluate$$anonfun$_apply$1.apply(CompileAndEvaluate.scala:57)
at is.hail.expr.ir.CompileAndEvaluate$$anonfun$_apply$1.apply(CompileAndEvaluate.scala:57)
at is.hail.utils.ExecutionTimer.time(ExecutionTimer.scala:81)
at is.hail.expr.ir.CompileAndEvaluate$._apply(CompileAndEvaluate.scala:57)
at is.hail.expr.ir.CompileAndEvaluate$.evalToIR(CompileAndEvaluate.scala:30)
at is.hail.expr.ir.LowerOrInterpretNonCompilable$.evaluate$1(LowerOrInterpretNonCompilable.scala:30)
at is.hail.expr.ir.LowerOrInterpretNonCompilable$.is$hail$expr$ir$LowerOrInterpretNonCompilable$$rewrite$1(LowerOrInterpretNonCompilable.scala:67)
at is.hail.expr.ir.LowerOrInterpretNonCompilable$.apply(LowerOrInterpretNonCompilable.scala:72)
at is.hail.expr.ir.lowering.LowerOrInterpretNonCompilablePass$.transform(LoweringPass.scala:69)
at is.hail.expr.ir.lowering.LoweringPass$$anonfun$apply$3$$anonfun$1.apply(LoweringPass.scala:16)
at is.hail.expr.ir.lowering.LoweringPass$$anonfun$apply$3$$anonfun$1.apply(LoweringPass.scala:16)
at is.hail.utils.ExecutionTimer.time(ExecutionTimer.scala:81)
at is.hail.expr.ir.lowering.LoweringPass$$anonfun$apply$3.apply(LoweringPass.scala:16)
at is.hail.expr.ir.lowering.LoweringPass$$anonfun$apply$3.apply(LoweringPass.scala:14)
at is.hail.utils.ExecutionTimer.time(ExecutionTimer.scala:81)
at is.hail.expr.ir.lowering.LoweringPass$class.apply(LoweringPass.scala:14)
at is.hail.expr.ir.lowering.LowerOrInterpretNonCompilablePass$.apply(LoweringPass.scala:64)
at is.hail.expr.ir.lowering.LoweringPipeline$$anonfun$apply$1.apply(LoweringPipeline.scala:15)
at is.hail.expr.ir.lowering.LoweringPipeline$$anonfun$apply$1.apply(LoweringPipeline.scala:13)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.WrappedArray.foreach(WrappedArray.scala:35)
at is.hail.expr.ir.lowering.LoweringPipeline.apply(LoweringPipeline.scala:13)
at is.hail.expr.ir.CompileAndEvaluate$._apply(CompileAndEvaluate.scala:47)
at is.hail.backend.spark.SparkBackend.is$hail$backend$spark$SparkBackend$$_execute(SparkBackend.scala:381)
at is.hail.backend.spark.SparkBackend$$anonfun$8$$anonfun$apply$4.apply(SparkBackend.scala:417)
at is.hail.backend.spark.SparkBackend$$anonfun$8$$anonfun$apply$4.apply(SparkBackend.scala:414)
at is.hail.backend.ExecuteContext$$anonfun$scoped$1$$anonfun$apply$1.apply(ExecuteContext.scala:47)
at is.hail.backend.ExecuteContext$$anonfun$scoped$1$$anonfun$apply$1.apply(ExecuteContext.scala:47)
at is.hail.utils.package$.using(package.scala:638)
at is.hail.backend.ExecuteContext$$anonfun$scoped$1.apply(ExecuteContext.scala:47)
at is.hail.backend.ExecuteContext$$anonfun$scoped$1.apply(ExecuteContext.scala:46)
at is.hail.utils.package$.using(package.scala:638)
at is.hail.annotations.RegionPool$.scoped(RegionPool.scala:17)
at is.hail.backend.ExecuteContext$.scoped(ExecuteContext.scala:46)
at is.hail.backend.spark.SparkBackend.withExecuteContext(SparkBackend.scala:275)
at is.hail.backend.spark.SparkBackend$$anonfun$8.apply(SparkBackend.scala:414)
at is.hail.backend.spark.SparkBackend$$anonfun$8.apply(SparkBackend.scala:413)
at is.hail.utils.ExecutionTimer$.time(ExecutionTimer.scala:52)
at is.hail.backend.spark.SparkBackend.executeEncode(SparkBackend.scala:413)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:282)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:238)
at java.lang.Thread.run(Thread.java:750)
java.lang.ClassFormatError: Too many arguments in method signature in class file __C60175collect_distributed_array
at java.lang.ClassLoader.defineClass1(Native Method)
at java.lang.ClassLoader.defineClass(ClassLoader.java:756)
at java.lang.ClassLoader.defineClass(ClassLoader.java:635)
at is.hail.asm4s.package$HailClassLoader$.liftedTree1$1(package.scala:253)
at is.hail.asm4s.package$HailClassLoader$.loadOrDefineClass(package.scala:249)
at is.hail.asm4s.ClassesBytes$$anonfun$load$1.apply(ClassBuilder.scala:65)
at is.hail.asm4s.ClassesBytes$$anonfun$load$1.apply(ClassBuilder.scala:63)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
at is.hail.asm4s.ClassesBytes.load(ClassBuilder.scala:63)
at is.hail.expr.ir.EmitClassBuilder$$anon$1.apply(EmitClassBuilder.scala:669)
at is.hail.expr.ir.EmitClassBuilder$$anon$1.apply(EmitClassBuilder.scala:662)
at is.hail.backend.BackendUtils$$anonfun$collectDArray$1$$anonfun$apply$1.apply(BackendUtils.scala:31)
at is.hail.backend.BackendUtils$$anonfun$collectDArray$1$$anonfun$apply$1.apply(BackendUtils.scala:30)
at is.hail.utils.package$.using(package.scala:638)
at is.hail.annotations.RegionPool.scopedRegion(RegionPool.scala:144)
at is.hail.backend.BackendUtils$$anonfun$collectDArray$1.apply(BackendUtils.scala:30)
at is.hail.backend.BackendUtils$$anonfun$collectDArray$1.apply(BackendUtils.scala:28)
at is.hail.backend.spark.SparkBackendComputeRDD.compute(SparkBackend.scala:730)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:123)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Hail version: 0.2.78-b17627756568
Error summary: ClassFormatError: Too many arguments in method signature in class file __C60175collect_distributed_array
```
---
### Author:  [@danking](https://discuss.hail.is/u/danking)
#### Post date: [December 19, 2022, 7:52pm UTC](https://discuss.hail.is/t/classformaterror-when-writing-matrixtable/2999/2 "2022-12-19T19:52:14Z")
We’ve made some improvements to Hail since 0.2.78 that should hopefully help with this, but in general, this means the Hail compiler is generating too large an expression. Can you share a bit more information about what code you’re executing?
In general, operations that generate a lot of column aggregations are likely to encounter this error.
---
### Author:  [@danking](https://discuss.hail.is/u/danking)
#### Post date: [December 19, 2022, 7:52pm UTC](https://discuss.hail.is/t/classformaterror-when-writing-matrixtable/2999/3 "2022-12-19T19:52:28Z")
Also the hail log file would be tremendously helpful in debugging this.
---
### Author:  [@jbchang](https://discuss.hail.is/u/jbchang)
#### Post date: [December 19, 2022, 8:19pm UTC](https://discuss.hail.is/t/classformaterror-when-writing-matrixtable/2999/4 "2022-12-19T20:19:31Z")
Yes—mt\_snps is a matrixtable that contains SNPs for one gene, CASR, on chromosome 3. It is heavily annotated in both rows (e.g. VEP, QC metrics, etc.) and columns (many clinical measurements from UKB). The `describe` output is below.
I am running this simple write statement:
`mt_snps.write(f'dnax://{my_database}/output.mt/', overwrite=True)`
Let me try to figure out how to share the log file with you… it’s too large to directly upload.
Best,
Jeremy
**UPDATE: I got rid of some of the row annotation statements and was able to write the matrixtable**. So, I think it was just that some of the statements were too long…
```python
----------------------------------------
Global fields:
None
----------------------------------------
Column fields:
's': str
'sample_data': struct {
p30680_i0: float64,
p30680_i1: float64,
p31: str,
p22027: str,
p22001: str,
p22006: str,
p22009_a1: float64,
p22009_a2: float64,
p22009_a3: float64,
p22009_a4: float64,
p22009_a5: float64,
p22009_a6: float64,
p22009_a7: float64,
p22009_a8: float64,
p22009_a9: float64,
p22009_a10: float64,
p21003_i0: int64,
p21003_i1: int64,
p21003_i2: int64,
p21003_i3: int64,
p30600_i0: float64,
p30600_i1: float64
}
'Ca_adj_i0': float64
'Ca_adj_i1': float64
----------------------------------------
Row fields:
'locus': locus
'alleles': array
'rsid': str
'qual': float64
'filters': set
'info': struct {
AF: array,
AQ: array,
AC: array,
AN: int32
}
'variant_qc': struct {
dp_stats: struct {
mean: float64,
stdev: float64,
min: float64,
max: float64
},
gq_stats: struct {
mean: float64,
stdev: float64,
min: float64,
max: float64
},
AC: array,
AF: array,
AN: int32,
homozygote_count: array,
call_rate: float64,
n_called: int64,
n_not_called: int64,
n_filtered: int64,
n_het: int64,
n_non_ref: int64,
het_freq_hwe: float64,
p_value_hwe: float64
}
'a_index': int32
'was_split': bool
'AC': array
'vep': struct {
assembly_name: str,
allele_string: str,
ancestral: str,
colocated_variants: array,
end: int32,
eas_allele: str,
eas_maf: float64,
ea_allele: str,
ea_maf: float64,
eur_allele: str,
eur_maf: float64,
exac_adj_allele: str,
exac_adj_maf: float64,
exac_allele: str,
exac_afr_allele: str,
exac_afr_maf: float64,
exac_amr_allele: str,
exac_amr_maf: float64,
exac_eas_allele: str,
exac_eas_maf: float64,
exac_fin_allele: str,
exac_fin_maf: float64,
exac_maf: float64,
exac_nfe_allele: str,
exac_nfe_maf: float64,
exac_oth_allele: str,
exac_oth_maf: float64,
exac_sas_allele: str,
exac_sas_maf: float64,
id: str,
minor_allele: str,
minor_allele_freq: float64,
phenotype_or_disease: int32,
pubmed: array,
sas_allele: str,
sas_maf: float64,
somatic: int32,
start: int32,
strand: int32
}>,
context: str,
end: int32,
id: str,
input: str,
intergenic_consequences: array,
impact: str,
minimised: int32,
variant_allele: str
}>,
most_severe_consequence: str,
motif_feature_consequences: array,
high_inf_pos: str,
impact: str,
minimised: int32,
motif_feature_id: str,
motif_name: str,
motif_pos: int32,
motif_score_change: float64,
strand: int32,
variant_allele: str
}>,
regulatory_feature_consequences: array,
impact: str,
minimised: int32,
regulatory_feature_id: str,
variant_allele: str
}>,
seq_region_name: str,
start: int32,
strand: int32,
transcript_consequences: array,
distance: int32,
domains: array,
exon: str,
gene_id: str,
gene_pheno: int32,
gene_symbol: str,
gene_symbol_source: str,
hgnc_id: str,
hgvsc: str,
hgvsp: str,
hgvs_offset: int32,
impact: str,
intron: str,
lof: str,
lof_flags: str,
lof_filter: str,
lof_info: str,
minimised: int32,
polyphen_prediction: str,
polyphen_score: float64,
protein_end: int32,
protein_start: int32,
protein_id: str,
sift_prediction: str,
sift_score: float64,
strand: int32,
swissprot: str,
transcript_id: str,
trembl: str,
tsl: int32,
uniparc: str,
variant_allele: str
}>,
variant_class: str
}
'vep_proc_id': struct {
part_idx: int32,
block_idx: int32
}
'hgvsp_NM_001178065': str
'hgvsp_NM_000388': str
'old_locus': locus
'old_alleles': array
'old_to_new': array
'new_to_old': array
'variant_str': str
'hethom_median_p30680_i0': float64
'hethom_quartiles_p30680_i0': array
'hethom_stats_p30680_i0': struct {
mean: float64,
stdev: float64,
min: float64,
max: float64,
n: int64,
sum: float64
}
'hethom_median_p30680_i1': float64
'hethom_quartiles_p30680_i1': array
'hethom_stats_p30680_i1': struct {
mean: float64,
stdev: float64,
min: float64,
max: float64,
n: int64,
sum: float64
}
'het_median_p30680_i0': float64
'het_quartiles_p30680_i0': array
'het_stats_p30680_i0': struct {
mean: float64,
stdev: float64,
min: float64,
max: float64,
n: int64,
sum: float64
}
'het_median_p30680_i1': float64
'het_quartiles_p30680_i1': array
'het_stats_p30680_i1': struct {
mean: float64,
stdev: float64,
min: float64,
max: float64,
n: int64,
sum: float64
}
'hom_median_p30680_i0': float64
'hom_quartiles_p30680_i0': array
'hom_stats_p30680_i0': struct {
mean: float64,
stdev: float64,
min: float64,
max: float64,
n: int64,
sum: float64
}
'hom_median_p30680_i1': float64
'hom_quartiles_p30680_i1': array
'hom_stats_p30680_i1': struct {
mean: float64,
stdev: float64,
min: float64,
max: float64,
n: int64,
sum: float64
}
'hethom_median_p30680_i0_adj': float64
'hethom_quartiles_p30680_i0_adj': array
'hethom_stats_p30680_i0_adj': struct {
mean: float64,
stdev: float64,
min: float64,
max: float64,
n: int64,
sum: float64
}
'hethom_median_p30680_i1_adj': float64
'hethom_quartiles_p30680_i1_adj': array
'hethom_stats_p30680_i1_adj': struct {
mean: float64,
stdev: float64,
min: float64,
max: float64,
n: int64,
sum: float64
}
'het_median_p30680_i0_adj': float64
'het_quartiles_p30680_i0_adj': array
'het_stats_p30680_i0_adj': struct {
mean: float64,
stdev: float64,
min: float64,
max: float64,
n: int64,
sum: float64
}
'het_median_p30680_i1_adj': float64
'het_quartiles_p30680_i1_adj': array
'het_stats_p30680_i1_adj': struct {
mean: float64,
stdev: float64,
min: float64,
max: float64,
n: int64,
sum: float64
}
'hom_median_p30680_i0_adj': float64
'hom_quartiles_p30680_i0_adj': array
'hom_stats_p30680_i0_adj': struct {
mean: float64,
stdev: float64,
min: float64,
max: float64,
n: int64,
sum: float64
}
'hom_median_p30680_i1_adj': float64
'hom_quartiles_p30680_i1_adj': array
'hom_stats_p30680_i1_adj': struct {
mean: float64,
stdev: float64,
min: float64,
max: float64,
n: int64,
sum: float64
}
'samples_with_variant': struct {
EID: array,
p30680_i0: array,
p30680_i1: array
}
'samples_with_variant_adj': struct {
EID: array,
p30680_i0_adj: array,
p30680_i1_adj: array
}
'linear_regression_p30680_i0': struct {
n: int32,
sum_x: float64,
y_transpose_x: float64,
beta: float64,
standard_error: float64,
t_stat: float64,
p_value: float64
}
----------------------------------------
Entry fields:
'GT': call
'RNC': array
'DP': int32
'AD': array
'GQ': int32
'PL': array
----------------------------------------
Column key: ['s']
Row key: ['locus', 'alleles']
----------------------------------------
```
---
### Author:  [@danking](https://discuss.hail.is/u/danking)
#### Post date: [December 21, 2022, 3:17pm UTC](https://discuss.hail.is/t/classformaterror-when-writing-matrixtable/2999/5 "2022-12-21T15:17:20Z")
Yeah, for better or worse Hail generates code linear in the number of fields.
One trick for dealing with this that works but makes it more annoying to work with is to store these median/quartiles/stats structures as an array:
```python
array
'stats': struct {
mean: float64,
stdev: float64,
min: float64,
max: float64,
n: int64,
sum: float64
}
}>
```
And then just have a convention that the first element of the array is `hethom_i0`, the second is `hethom_i1`, etc.
Alternatively, a lot of folks store their row annotations in separate Hail Table files and only “join” them in when necessary.