This is an automated email from the ASF dual-hosted git repository.
blue pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/iceberg.git
The following commit(s) were added to refs/heads/master by this push:
new b265ccc Docs: Update Spark documentation for Spark 3 SQL (#1172)
b265ccc is described below
commit b265ccc1d8c9d392cc91d3c55ebf4edc27680937
Author: Ryan Blue <[email protected]>
AuthorDate: Tue Jul 7 14:02:04 2020 -0700
Docs: Update Spark documentation for Spark 3 SQL (#1172)
---
site/docs/spark.md | 503 ++++++++++++++++++++++++++++++++++++++++++++++-------
1 file changed, 440 insertions(+), 63 deletions(-)
diff --git a/site/docs/spark.md b/site/docs/spark.md
index 2635a01..b933823 100644
--- a/site/docs/spark.md
+++ b/site/docs/spark.md
@@ -19,42 +19,272 @@
Iceberg uses Apache Spark's DataSourceV2 API for data source and catalog
implementations. Spark DSv2 is an evolving API with different levels of support
in Spark versions.
-| Feature support | Spark 2.4 | Spark 3.0
(unreleased) | Notes |
-|----------------------------------------------|-----------|------------------------|------------------------------------------------|
-| [DataFrame reads](#reading-an-iceberg-table) | ✔️ | ✔️
| |
-| [DataFrame append](#appending-data) | ✔️ | ✔️
| |
-| [DataFrame overwrite](#overwriting-data) | ✔️ | ✔️
| Overwrite mode replaces partitions dynamically |
-| [Metadata tables](#inspecting-tables) | ✔️ | ✔️
| |
-| SQL create table | | ✔️
| |
-| SQL alter table | | ✔️
| |
-| SQL drop table | | ✔️
| |
-| SQL select | | ✔️
| |
-| SQL create table as | | ✔️
| |
-| SQL replace table as | | ✔️
| |
-| SQL insert into | | ✔️
| |
-| SQL insert overwrite | | ✔️
| |
+| Feature support | Spark 3.0| Spark 2.4 |
Notes |
+|--------------------------------------------------|----------|------------|------------------------------------------------|
+| [SQL create table](#create-table) | ✔️ | |
|
+| [SQL create table as](#create-table-as-select) | ✔️ | |
|
+| [SQL replace table as](#replace-table-as-select) | ✔️ | |
|
+| [SQL alter table](#alter-table) | ✔️ | |
|
+| [SQL drop table](#drop-table) | ✔️ | |
|
+| [SQL select](#querying-with-sql) | ✔️ | |
|
+| [SQL insert into](#insert-into) | ✔️ | |
|
+| [SQL insert overwrite](#insert-overwrite) | ✔️ | |
|
+| [DataFrame reads](#querying-with-dataframes) | ✔️ | ✔️ |
|
+| [DataFrame append](#appending-data) | ✔️ | ✔️ |
|
+| [DataFrame overwrite](#overwriting-data) | ✔️ | ✔️ |
⚠ Behavior changed in Spark 3.0 |
+| [DataFrame CTAS and RTAS](#creating-tables) | ✔️ | |
|
+| [Metadata tables](#inspecting-tables) | ✔️ | ✔️ |
|
+
+## Configuring catalogs
+
+Spark 3.0 adds an API to plug in table catalogs that are used to load, create,
and manage Iceberg tables. Spark catalogs are configured by setting Spark
properties under `spark.sql.catalog`.
+
+This creates an Iceberg catalog named `hive_prod` that loads tables from a
Hive metastore:
+
+```plain
+spark.sql.catalog.hive_prod = org.apache.iceberg.spark.SparkCatalog
+spark.sql.catalog.hive_prod.type = hive
+spark.sql.catalog.hive_prod.uri = thrift://metastore-host:port
+```
+
+Iceberg also supports a directory-based catalog in HDFS that can be configured
using `type=hadoop`:
+
+```plain
+spark.sql.catalog.hadoop_prod = org.apache.iceberg.spark.SparkCatalog
+spark.sql.catalog.hadoop_prod.type = hive
+spark.sql.catalog.hadoop_prod.warehouse = hdfs://nn:8020/warehouse/path
+```
+
+!!! Note
+ The Hive-based catalog only loads Iceberg tables. To load non-Iceberg
tables in the same Hive metastore, use a [session
catalog](#replacing-the-session-catalog).
+
+### Using catalogs
+
+Catalog names are used in SQL queries to identify a table. In the examples
above, `hive_prod` and `hadoop_prod` can be used to prefix database and table
names that will be loaded from those catalogs.
+
+```sql
+SELECT * FROM hive_prod.db.table -- load db.table from catalog hive_prod
+```
+
+Spark 3 keeps track of a current catalog and namespace, which can be omitted
from table names.
+
+```sql
+USE hive_prod.db;
+SELECT * FROM table -- load db.table from catalog hive_prod
+```
+
+To see the current catalog and namespace, run `SHOW CURRENT NAMESPACE`.
+
+### Replacing the session catalog
+
+To add Iceberg table support to Spark's built-in catalog, configure
`spark_catalog` to use Iceberg's `SparkSessionCatalog`.
+
+```plain
+spark.sql.catalog.spark_catalog = org.apache.iceberg.spark.SparkSessionCatalog
+spark.sql.catalog.spark_catalog.type = hive
+# omit uri to use the same URI as Spark: hive.metastore.uris in hive-site.xml
+```
+
+Spark's built-in catalog supports existing v1 and v2 tables tracked in a Hive
Metastore. This configures Spark to use Iceberg's `SparkSessionCatalog` as a
wrapper around that session catalog. When a table is not an Iceberg table, the
built-in catalog will be used to load it instead.
+
+This configuration can use same Hive Metastore for both Iceberg and
non-Iceberg tables.
+
+
+## DDL commands
!!! Note
Spark 2.4 can't create Iceberg tables with DDL, instead use the [Iceberg
API](../api-quickstart).
+### `CREATE TABLE`
+
+Spark 3.0 can create tables in any Iceberg catalog with the clause `USING
iceberg`:
+
+```sql
+CREATE TABLE prod.db.sample (
+ id bigint COMMENT 'unique id',
+ data string)
+USING iceberg
+```
+
+Table create commands, including CTAS and RTAS, support the full range of
Spark create clauses, including:
+
+* `PARTITION BY (partition-expressions)` to configure partitioning
+* `LOCATION '(fully-qualified-uri)'` to set the table location
+* `COMMENT 'table documentation'` to set a table description
+* `TBLPROPERTIES ('key'='value', ...)` to set [table
configuration](../configuration)
+
+Create commands may also set the default format with the `USING` clause. This
is only supported for `SparkCatalog` because Spark handles the `USING` clause
differently for the built-in catalog.
+
+#### `PARTITIONED BY`
+
+To create a partitioned table, use `PARTITIONED BY`:
+
+```sql
+CREATE TABLE prod.db.sample (
+ id bigint,
+ data string,
+ category string)
+USING iceberg
+PARTITIONED BY (category)
+```
+
+The `PARTITIONED BY` clause supports transform expressions to create [hidden
partitions](../partitioning).
+
+```sql
+CREATE TABLE prod.db.sample (
+ id bigint,
+ data string,
+ category string,
+ ts timestamp)
+USING iceberg
+PARTITIONED BY (bucket(16, id), days(ts), category)
+```
+
+Supported partition transforms are:
+
+* `years` for yearly partitions
+* `months` for monthly partitions
+* `days` for daily partitions
+* `hours` for hourly partitions
+* `bucket` for bucketing (with width)
+* `truncate` to truncate integers or strings (with length)
+
+### `CREATE TABLE ... AS SELECT`
+
+Iceberg supports CTAS as an atomic operation when using a
[`SparkCatalog`](#configuring-catalogs). CTAS is supported, but is not atomic
when using [`SparkSessionCatalog`](#replacing-the-session-catalog).
+
+```sql
+CREATE TABLE prod.db.sample
+USING iceberg
+AS SELECT ...
+```
+
+### `REPLACE TABLE ... AS SELECT`
+
+Iceberg supports RTAS as an atomic operation when using a
[`SparkCatalog`](#configuring-catalogs). RTAS is supported, but is not atomic
when using [`SparkSessionCatalog`](#replacing-the-session-catalog).
+
+Atomic table replacement creates a new snapshot with the results of the
`SELECT` query, but keeps table history.
+
+```sql
+REPLACE TABLE prod.db.sample
+USING iceberg
+AS SELECT ...
+```
+```sql
+CREATE OR REPLACE TABLE prod.db.sample
+USING iceberg
+AS SELECT ...
+```
+
+The schema and partition spec will be replaced if changed. To avoid modifying
the table's schema and partitioning, use `INSERT OVERWRITE` instead of `REPLACE
TABLE`.
+
+### `ALTER TABLE`
+
+Iceberg has full `ALTER TABLE` support in Spark 3, including:
+
+* Renaming a table
+* Setting or removing table properties
+* Adding, deleting, and renaming columns
+* Adding, deleting, and renaming nested fields
+* Reordering top-level columns and nested struct fields
+* Widening the type of `int`, `float`, and `decimal` fields
+* Making required columns optional
+
+### `ALTER TABLE ... RENAME TO`
+
+```sql
+ALTER TABLE prod.db.sample RENAME TO prod.db.new_name
+```
+
+### `ALTER TABLE ... SET TBLPROPERTIES`
+
+```sql
+ALTER TABLE prod.db.sample SET TBLPROPERTIES (
+ 'read.split.target-size'='268435456'
+)
+```
+
+Iceberg uses table properties to control table behavior. For a list of
available properties, see [Table configuration](../configuration).
+
+`UNSET` is used to remove properties:
-## Spark 2.4
+```sql
+ALTER TABLE prod.db.sample UNSET TBLPROPERTIES ('read.split.target-size')
+```
+
+### `ALTER TABLE ... ADD COLUMN`
+
+```sql
+ALTER TABLE prod.db.sample ADD COLUMN point struct<x: double NOT NULL, y:
double NOT NULL> AFTER data
+ALTER TABLE prod.db.sample ADD COLUMN point.z double FIRST
+```
+
+### `ALTER TABLE ... RENAME COLUMN`
-### Creating a table
+```sql
+ALTER TABLE prod.db.sample RENAME COLUMN data TO payload
+ALTER TABLE prod.db.sample RENAME COLUMN location.lat TO latitude
+```
-Spark 2.4 is limited to reading and writing existing Iceberg tables. Use the
[Iceberg API](../api) to create Iceberg tables.
+Note that nested rename commands only rename the leaf field. The above command
renames `location.lat` to `location.latitude`
-### Reading an Iceberg table
+### `ALTER TABLE ... ALTER COLUMN`
-To read an Iceberg table, use the `iceberg` format in `DataFrameReader`:
+Alter column is used to widen types, make a field optional, set comments, and
reorder fields.
+
+```sql
+ALTER TABLE prod.db.sample ALTER COLUMN id DROP NOT NULL
+ALTER TABLE prod.db.sample ALTER COLUMN location.lat TYPE double
+ALTER TABLE prod.db.sample ALTER COLUMN point.z AFTER y
+ALTER TABLE prod.db.sample ALTER COLUMN id COMMENT 'unique id'
+```
+
+### `ALTER TABLE ... DROP COLUMN`
+
+```sql
+ALTER TABLE prod.db.sample DROP COLUMN id
+ALTER TABLE prod.db.sample DROP COLUMN point.z
+```
+
+### `DROP TABLE`
+
+To delete a table, run:
+
+```sql
+DROP TABLE prod.db.sample
+```
+
+## Querying with SQL
+
+In Spark 3, tables use identifiers that include a [catalog
name](#using-catalogs).
+
+```sql
+SELECT * FROM prod.db.table -- catalog: prod, namespace: db, table: table
+```
+
+Metadata tables, like `history` and `snapshots`, can use the Iceberg table
name as a namespace.
+
+For example, to read from the `files` metadata table for `prod.db.table`, run:
+
+```
+SELECT * FROM prod.db.table.files
+```
+
+## Querying with DataFrames
+
+To load a table as a DataFrame, use `table`:
```scala
-// named metastore table
-spark.read.format("iceberg").load("db.table")
-// Hadoop path table
-spark.read.format("iceberg").load("hdfs://nn:8020/path/to/table")
+val df = spark.table("prod.db.table")
```
+To configure the `DataFrameReader`, use a reader directly:
+
+```sql
+val df = spark.read
+ .option("split-size", "268435456")
+ .table("prod.db.table")
+```
### Time travel
@@ -66,20 +296,31 @@ To select a specific table snapshot or the snapshot at
some time, Iceberg suppor
```scala
// time travel to October 26, 1986 at 01:21:00
spark.read
- .format("iceberg")
.option("as-of-timestamp", "499162860000")
- .load("db.table")
+ .table("prod.db.table")
```
```scala
// time travel to snapshot with ID 10963874102873L
spark.read
- .format("iceberg")
.option("snapshot-id", 10963874102873L)
- .load("db.table")
+ .table("prod.db.table")
+```
+
+Time travel is not yet supported by Spark's SQL syntax.
+
+### Spark 2.4
+
+Spark 2.4 requires using the DataFrame reader with `iceberg` as a format,
becuase 2.4 does not support catalogs:
+
+```scala
+// named metastore table
+spark.read.format("iceberg").load("db.table")
+// Hadoop path table
+spark.read.format("iceberg").load("hdfs://nn:8020/path/to/table")
```
-### Querying with SQL
+#### Spark 2.4 with SQL
To run SQL `SELECT` statements on Iceberg tables in 2.4, register the
DataFrame as a temporary table:
@@ -91,25 +332,146 @@ spark.sql("""select count(1) from table""").show()
```
+## Writing with SQL
+
+Spark 3 supports SQL `INSERT INTO` and `INSERT OVERWRITE`, as well as the new
`DataFrameWriterV2` API.
+
+### `INSERT INTO`
+
+To append new data to a table, use `INSERT INTO`.
+
+```sql
+INSERT INTO prod.db.table VALUES (1, 'a'), (2, 'b')
+```
+```sql
+INSERT INTO prod.db.table SELECT ...
+```
+
+### `INSERT OVERWRITE`
+
+To replace data in the table with the result of a query, use `INSERT
OVERWRITE`. Overwrites are atomic operations for Iceberg tables.
+
+The partitions that will be replaced by `INSERT OVERWRITE` depends on Spark's
partition overwrite mode and the partitioning of a table.
+
+#### Overwrite behavior
+
+Spark's default overwrite mode is **static**, but **dynamic overwrite mode is
recommended when writing to Iceberg tables.** Static overwrite mode determines
which partitions to overwrite in a table by converting the `PARTITION` clause
to a filter, but the `PARTITION` clause can only reference table columns.
+
+Dynamic overwrite mode is configured by setting
`spark.sql.sources.partitionOverwriteMode=dynamic`.
+
+To demonstrate the behavior of dynamic and static overwrites, consider a
`logs` table defined by the following DDL:
+
+```sql
+CREATE TABLE prod.my_app.logs (
+ uuid string NOT NULL,
+ level string NOT NULL,
+ ts timestamp NOT NULL,
+ message string)
+USING iceberg
+PARTITIONED BY (level, hours(ts))
+```
+
+#### Dynamic overwrite
+
+When Spark's overwrite mode is dynamic, partitions that have rows produced by
the `SELECT` query will be replaced.
+
+For example, this query removes duplicate log events from the example `logs`
table.
+
+```sql
+INSERT OVERWRITE prod.my_app.logs
+SELECT uuid, first(level), first(ts), first(message)
+FROM prod.my_app.logs
+WHERE cast(ts as date) = '2020-07-01'
+GROUP BY uuid
+```
+
+In dynamic mode, this will replace any partition with rows in the `SELECT`
result. Because the date of all rows is restricted 1 July, only hours of that
day will be replaced.
+
+#### Static overwrite
+
+When Spark's overwrite mode is static, the `PARTITION` clause is converted to
a filter that is used to delete from the table. If the `PARTITION` clause is
omitted, all partitions will be replaced.
+
+Because there is no `PARTITION` clause in the query above, it will drop all
existing rows in the table when run in static mode, but will only write the
logs from 1 July.
+
+To overwrite just the partitions that were loaded, add a `PARTITION` clause
that aligns with the `SELECT` query filter:
+
+```sql
+INSERT OVERWRITE prod.my_app.logs
+PARTITION (level = 'INFO')
+SELECT uuid, first(level), first(ts), first(message)
+FROM prod.my_app.logs
+WHERE level = 'INFO'
+GROUP BY uuid
+```
+
+Note that this mode cannot replace hourly partitions like the dynamic example
query because the `PARTITION` clause can only reference table columns, not
hidden partitions.
+
+### `DELETE FROM`
+
+Spark 3 added support for `DELETE FROM` queries to remove data from tables.
+
+Delete queries accept a filter to match rows to delete. Iceberg can delete
data as long as the filter matches entire partitions of the table, or it can
determine that all rows of a file match. If a file contains some rows that
should be deleted and some that should not, Iceberg will throw an exception.
+
+```sql
+DELETE FROM prod.db.table
+WHERE ts >= '2020-05-01 00:00:00' and ts < '2020-06-01 00:00:00'
+```
+
+
+## Writing with DataFrames
+
+Spark 3 introduced the new `DataFrameWriterV2` API for writing to tables using
data frames. The v2 API is recommended for several reasons:
+
+* CTAS, RTAS, and overwrite by filter are supported
+* All operations consistently write columns to a table by name
+* Hidden partition expressions are supported in `partitionedBy`
+* Overwrite behavior is explicit, either dynamic or by a user-supplied filter
+* The behavior of each operation corresponds to SQL statements
+ - `df.writeTo(t).create()` is equivalent to `CREATE TABLE AS SELECT`
+ - `df.writeTo(t).replace()` is equivalent to `REPLACE TABLE AS SELECT`
+ - `df.writeTo(t).append()` is equivalent to `INSERT INTO`
+ - `df.writeTo(t).overwritePartitions()` is equivalent to dynamic `INSERT
OVERWRITE`
+
### Appending data
-To append a dataframe to an Iceberg table, use the `iceberg` format with
`append` mode in the `DataFrameWriter`:
+To append a dataframe to an Iceberg table, use `append`:
```scala
val data: DataFrame = ...
+data.writeTo("prod.db.table").append()
+```
+
+#### Spark 2.4
+
+In Spark 2.4, use the v1 API with `append` mode and `iceberg` format:
+
+```scala
data.write
.format("iceberg")
.mode("append")
.save("db.table")
```
-
### Overwriting data
-To overwrite values in an Iceberg table, use `overwrite` mode in the
`DataFrameWriter`:
+To overwrite partitions dynamically, use `overwritePartitions()`:
```scala
val data: DataFrame = ...
+data.writeTo("prod.db.table").overwritePartitions()
+```
+
+To explicitly overwrite partitions, use `overwrite` to supply a filter:
+
+```scala
+data.writeTo("prod.db.table").overwrite($"level" === "INFO")
+```
+
+#### Spark 2.4
+
+In Spark 2.4, overwrite values in an Iceberg table with `overwrite` mode and
`iceberg` format:
+
+```scala
data.write
.format("iceberg")
.mode("overwrite")
@@ -117,24 +479,41 @@ data.write
```
!!! Warning
- **Spark does not define the behavior of DataFrame overwrite**. Like most
sources, Iceberg will dynamically overwrite partitions when the dataframe
contains rows in a partition. Unpartitioned tables are completely overwritten.
+ **The behavior of overwrite mode changed between Spark 2.4 and Spark 3**.
+
+The behavior of DataFrameWriter overwrite mode was undefined in Spark 2.4, but
is required to overwrite the entire table in Spark 3. Because of this new
requirement, the Iceberg source's behavior changed in Spark 3. In Spark 2.4,
the behavior was to dynamically overwrite partitions. To use the Spark 2.4
behavior, add option `overwrite-mode=dynamic`.
+
+### Creating tables
+
+To run a CTAS or RTAS, use `create`, `replace`, or `createOrReplace`
operations:
+
+```scala
+val data: DataFrame = ...
+data.writeTo("prod.db.table").create()
+```
+
+Create and replace operations support table configuration methods, like
`partitionedBy` and `tableProperty`:
+```scala
+data.writeTo("prod.db.table")
+ .tableProperty("write.format.default", "orc")
+ .partitionBy($"level", days($"ts"))
+ .createOrReplace()
+```
-### Inspecting tables
+
+## Inspecting tables
To inspect a table's history, snapshots, and other metadata, Iceberg supports
metadata tables.
Metadata tables are identified by adding the metadata table name after the
original table name. For example, history for `db.table` is read using
`db.table.history`.
-#### History
+### History
To show table history, run:
-```scala
-// named metastore table
-spark.read.format("iceberg").load("db.table.history").show(truncate = false)
-// Hadoop path table
-spark.read.format("iceberg").load("hdfs://nn:8020/path/to/table#history").show(truncate
= false)
+```sql
+SELECT * FROM prod.db.table.history
```
```text
+-------------------------+---------------------+---------------------+---------------------+
@@ -152,15 +531,12 @@
spark.read.format("iceberg").load("hdfs://nn:8020/path/to/table#history").show(t
!!! Note
**This shows a commit that was rolled back.** The example has two
snapshots with the same parent, and one is *not* an ancestor of the current
table state.
-#### Snapshots
+### Snapshots
To show the valid snapshots for a table, run:
-```scala
-// named metastore table
-spark.read.format("iceberg").load("db.table.snapshots").show(truncate = false)
-// Hadoop path table
-spark.read.format("iceberg").load("hdfs://nn:8020/path/to/table#snapshots").show(truncate
= false)
+```sql
+SELECT * FROM prod.db.table.snapshots
```
```text
+-------------------------+----------------+-----------+-----------+----------------------------------------------------+-------------------------------------------------------+
@@ -175,10 +551,6 @@
spark.read.format("iceberg").load("hdfs://nn:8020/path/to/table#snapshots").show
You can also join snapshots to table history. For example, this query will
show table history, with the application ID that wrote each snapshot:
-```scala
-spark.read.format("iceberg").load("db.table.history").createOrReplaceTempView("history")
-spark.read.format("iceberg").load("db.table.snapshots").createOrReplaceTempView("snapshots")
-```
```sql
select
h.made_current_at,
@@ -186,8 +558,8 @@ select
h.snapshot_id,
h.is_current_ancestor,
s.summary['spark.app.id']
-from history h
-join snapshots s
+from prod.db.table.history h
+join prod.db.table.snapshots s
on h.snapshot_id = s.snapshot_id
order by made_current_at
```
@@ -202,15 +574,12 @@ order by made_current_at
+-------------------------+-----------+----------------+---------------------+----------------------------------+
```
-#### Manifests
+### Manifests
To show a table's file manifests and each file's metadata, run:
-```scala
-// named metastore table
-spark.read.format("iceberg").load("db.table.manifests").show(truncate = false)
-// Hadoop path table
-spark.read.format("iceberg").load("hdfs://nn:8020/path/to/table#manifests").show(truncate
= false)
+```sql
+SELECT * FROM prod.db.table.manifests
```
```text
+----------------------------------------------------------------------+--------+-------------------+---------------------+------------------------+---------------------------+--------------------------+---------------------------------+
@@ -220,15 +589,12 @@
spark.read.format("iceberg").load("hdfs://nn:8020/path/to/table#manifests").show
+----------------------------------------------------------------------+--------+-------------------+---------------------+------------------------+---------------------------+--------------------------+---------------------------------+
```
-#### Files
+### Files
To show a table's data files and each file's metadata, run:
-```scala
-// named metastore table
-spark.read.format("iceberg").load("db.table.files").show(truncate = false)
-// Hadoop path table
-spark.read.format("iceberg").load("hdfs://nn:8020/path/to/table#files").show(truncate
= false)
+```sql
+SELECT * FROM prod.db.table.files
```
```text
+-------------------------------------------------------------------------+-------------+--------------+--------------------+--------------------+------------------+-------------------+-----------------+-----------------+--------------+---------------+
@@ -239,3 +605,14 @@
spark.read.format("iceberg").load("hdfs://nn:8020/path/to/table#files").show(tru
| s3:/.../table/data/00002-5-8d6d60e8-d427-4809-bcf0-f5d45a4aad96.parquet |
PARQUET | 1 | 597 | [1 -> 90, 2 -> 62] | [1 -> 1,
2 -> 1] | [1 -> 0, 2 -> 0] | [1 -> , 2 -> a] | [1 -> , 2 -> a] | null
| [4] |
+-------------------------------------------------------------------------+-------------+--------------+--------------------+--------------------+------------------+-------------------+-----------------+-----------------+--------------+---------------+
```
+
+### Inspecting with DataFrames
+
+Metadata tables can be loaded in Spark 2.4 or Spark 3 using the
DataFrameReader API:
+
+```scala
+// named metastore table
+spark.read.format("iceberg").load("db.table.files").show(truncate = false)
+// Hadoop path table
+spark.read.format("iceberg").load("hdfs://nn:8020/path/to/table#files").show(truncate
= false)
+```
