Forum Discussion
Lakehouse Table Generate Create Table
One final update on this.
The logfile containing the schema is not necessarily the most recent, and there is not necessarily only one version of the schema. There is a schema associated with every modification made to the table structure, be that the original creation or subsequent alterations. Consequently, the logfile we need is the most recent with a schema.
from delta.tables import DeltaTableimport json# Note that the table name must be lowercasetable_path = '<Path_To_Table>'# Identify log fileslog_dir = f"{table_path}/_delta_log"files = [f.path for f in notebookutils.fs.ls(log_dir) if f.name.endswith(".json")]# Identify log files with a schemafilesWithSchema = []for file in sorted(files, reverse=True) :content = notebookutils.fs.head(file, 5000000)JSONdocs = content.split('\n')for doc in JSONdocs:if 'schemaString' in doc:filesWithSchema.append(file)# Load the header for the latest log file containing a schemalatest = sorted(filesWithSchema, reverse=True)[0]content = notebookutils.fs.head(latest, 5000000)# Extract the schemaJSONdocs = content.split('\n')for doc in JSONdocs:if 'schemaString' in doc:schemaString = json.loads(doc).get("metaData", {}).get("schemaString")# Extract Field MetadataFieldList = []OrdinalPosition = 0for field in json.loads(schemaString).get("fields") :OrdinalPosition += 1FieldDetails = {}FieldDetails['FieldName'] = field.get("name")FieldDetails['Nullable'] = field.get("nullable")FieldDetails['OrdinalPosition'] = OrdinalPositionmatch field.get("type").split('(')[0]:case 'string':FieldDetails['SQLType'] = field.get("metadata").get("__CHAR_VARCHAR_TYPE_STRING")case 'timestamp':FieldDetails['SQLType'] = 'timestamp'case 'date':FieldDetails['SQLType'] = 'date'case 'integer':FieldDetails['SQLType'] = 'int'case 'short':FieldDetails['SQLType'] = 'smallint'case 'long':FieldDetails['SQLType'] = 'bigint'case 'decimal':FieldDetails['SQLType'] = field.get("type")case 'boolean':FieldDetails['SQLType'] = 'boolean'FieldList.append(FieldDetails)display(FieldList)
Whilst I understand everything that you are saying, I would like to describe another scenario which suggests that some of the above is not actually correct.
Using only SparkSQL in a notebook I have created a lakehouse table with a single varchar(10) field. If I try to insert any value with more than 10 characters I get the following error:
[DELTA_EXCEED_CHAR_VARCHAR_LIMIT] Exceeds char/varchar type length limitation. Failed check: (isnull('String) OR (length('String) <= 10)).
Obviously something in the spark engine or the delta table metadata ia storing the size restriction
Hi JonBFabric , Thank you for reaching out to the Microsoft Community Forum.
Yes, Spark/Delta can record and enforce CHAR(n) / VARCHAR(n) when a table is created through Spark or other Delta-aware APIs; the engine stores that constraint in the Delta metadata and will reject writes that exceed the declared width (hence the DELTA_EXCEED_CHAR_VARCHAR_LIMIT error). The authoritative place to get that declaration is the Spark/Delta surface (for example, run SHOW CREATE TABLE or DESCRIBE TABLE EXTENDED in a Spark notebook or read the Delta transaction log/Delta Table API). Those commands return the DDL/metadata that Spark/Delta actually enforces.
Do not rely on the Fabric SQL analytics endpoint or INFORMATION_SCHEMA alone to recover declared widths. Those surfaces present a T-SQL compatibility projection that can inflate, cap or otherwise transform reported lengths (the 4×/8000 behaviour you saw) and therefore are not a trustworthy source of the original Spark declared sizes. If you cannot run Spark against the table, your fallback is to inspect the _delta_log or compute observed max character/byte lengths and reconstruct conservative VARCHAR widths and for long term safety you must version-control the DDL or persist it as table metadata at creation time.
Good Morning,
I'm not looking for a way to access the metadata through the SQL endpoint, by using INFORMATION_SCHEMA or any other objects/functions, I just used it as an example of the only place that displayed anything other than string. I have already tried both SHOW CREATE TABLE and DESCRIBE TABLE EXTENDED, the first is not supported by Fabric ([DELTA_OPERATION_NOT_ALLOWED] Operation not allowed: `SHOW CREATE TABLE` is not supported for Delta tables) and the 2nd only shows string.
Please could you provide me with an example of how to access the delta metadata responsible for enforcing the DELTA_EXCEED_CHAR_VARCHAR_LIMIT error. It doesn't need to be pretty.
Thanks again
Hi JonBFabric , Thank you for reaching out to the Microsoft Community Forum.
If Fabric is blocking SHOW CREATE TABLE and DESCRIBE TABLE EXTENDED only shows string, the next step is to read the Delta metadata directly through Spark, because the enforcement you are seeing (DELTA_EXCEED_CHAR_VARCHAR_LIMIT) comes from the schema stored in the Delta transaction log, not from the SQL endpoint. The length constraint is kept in the Delta log under metaData.schemaString and Spark/Delta will surface it correctly when you query the table through the Delta APIs. The simplest approach is to use a Spark notebook and load the table with DeltaTable.forPath(...).toDF(), which will show VarcharType(n) in the schema if the table was created with varchar(n). If that surface is not available, you can read the latest commit JSON in the _delta_log folder and print the metaData.schemaString field; that text contains the exact schema Spark is enforcing, including declared lengths.
Spark example you can run in a Fabric notebook to retrieve the metadata responsible for the enforcement:
from delta.tables import DeltaTable import json
table_path = "/lakehouses/<your-lakehouse>/Tables/<your-table>" # update this
dt = DeltaTable.forPath(spark, table_path) print(dt.toDF().schema) # shows VarcharType(n) if declared
log_dir = f"{table_path}/_delta_log" files = [f.path for f in dbutils.fs.ls(log_dir) if f.name.endswith(".json")] latest = sorted(files)[-1]
content = dbutils.fs.head(latest, 500000) commit = json.loads(content) print(commit.get("metaData", {}).get("schemaString"))This will show you the exact schema stored in Delta and the constraint that triggers the length violation error. If the table is large and uses checkpoint parquet files, the same field appears in the checkpoint’s metaData struct. In short, the SQL endpoint cannot return the declared widths, but Spark and the Delta log always can.
Explore the lakehouse data with a notebook - Microsoft Fabric | Microsoft Learn
Data Types in Fabric Data Warehouse - Microsoft Fabric | Microsoft Learn
Delta Lake Logs in Warehouse - Microsoft Fabric | Microsoft Learn
Thanks.
This didn't quite work out of the box, possibly because it was originally written for DataBricks rather than Fabric, but I have got it working. I will explan the differences as I go:
from delta.tables import DeltaTableimport jsontable_path = "<Path_To_Table>" # update thisdt = DeltaTable.forPath(spark, table_path)print(dt.toDF().schema) # shows VarcharType(n) if declared.schema shows only string as type, so the last 2 lines can be removed.log_dir = f"{table_path}/_delta_log"files = [f.path for f in notebookutils.fs.ls(log_dir) if f.name.endswith(".json")]latest = sorted(files)[-1]content = notebookutils.fs.head(latest, 5000000)Note that dbutils is now replaced by notebookutils.content can not however be read as JSON, as it is infact 3 JSON documents seperated by'\n', and not just 1, and the document which contains the field metadata is the 2nd.schemaString = json.loads(content.split('\n')[1]).get("metaData", {}).get("schemaString")schemaString is actually an embedded JSON document held as a string, and so has to be converted also. The following snippet then prints the field name and the actual sql type.for field in json.loads(schemaString).get("fields"):print("FieldName:", field.get("name"), ", Type:", field.get("metadata").get("__CHAR_VARCHAR_TYPE_STRING"))Certainly not a finished article, but it gives me what I need to build around.Thanks for your help and patience.