Comments (6)
Can you give an example of properties you're trying to set here?
We can cache file systems in HadoopTableOperations, but most of the systems I've worked on use this pattern of getting the right file system for the URI and using the FileSystem level cache.
from iceberg.
We are experimenting with using Iceberg as a temporary representation of the tables that are backed by our internal data warehouse solution. When we do so, however, we need to put the Iceberg table metadata somewhere. We want to put it on local disk, but when we put it on local disk we need to encrypt it with a one-time encryption key that only exists for the lifetime of the Spark dataset that is being read / written; So for example we're doing something like this:
Key encryptionKey = generateKey();
Configuration conf = new Configuration();
conf.set("encryption.key", encryptionKey.toString());
HadoopTables tables = new HadoopTables(conf);
// create table and insert all metadata
sparkSession.read().option("iceberg.spark.hadoop.encryption.key", encryptionKey.toString()).load(tempTablePath);
In such a case, we don't want the same file system instance - probably a local FS instance wrapped with some encryption layer - to be cached, because every time we run this code we want a different encryption key every time.
from iceberg.
Okay, how about adding the support you're talking about to HadoopTableOperations and opening a PR? That would unblock you because you'd have the caching level you need and we could further evaluate the feature.
from iceberg.
Also, why do all of the properties include "spark"?
from iceberg.
The properties here assume being injected into sparkSession.read.option
. If we wanted to include them in the Table properties set instead it should be iceberg.hadoop
.
from iceberg.
Properties set through Spark wouldn't need to be specific to Spark. You might use the same ones as session properties in Presto.
from iceberg.
Related Issues (20)
- Allow Specifying Partitioning Function for External Mappings HOT 1
- Document Spark Issues Affecting the Iceberg Implementation
- Snapshot-Level Metrics and Statistics
- Cannot convert unknown primitive type: required int96 `timestamp` HOT 2
- Add More Documentation HOT 4
- Custom metadata in data files
- Custom InputFile / OutputFile providers for Spark HOT 4
- Support for AWS Glue as an alternative Hive metastore implementation HOT 5
- .travis.yml: The 'sudo' tag is now deprecated in Travis CI
- Port to Azure or Google Cloud?
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- Problem inserting data into a table with structs (iceberg-spark)
- java.lang.AbstractMethodError: Method org/apache/iceberg/spark/source/SparkTable.newWriteBuilder HOT 1
- Long wait time for retry when refreshing table metadata HOT 1
- Support Customizing The Location Of Data Files Written By The Spark Data Source HOT 3
- Upgrade to Spark 2.4.0 HOT 2
- iceberg-runtime jar on JitPack is empty HOT 4
- Snapshot cryptographic integrity HOT 1
- Replace Literals with Stateless Functions
- Predicate Compiler for Evaluators
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from iceberg.