[doc] Fix typos in docs and code comments (#7341)

Author: plusplusjiajia

docs/content/_index.md

@@ -45,7 +45,7 @@ Paimon offers the following core capabilities:
 
 ## Try Paimon
 
-If you’re interested in playing around with Paimon, check out our
+If you're interested in playing around with Paimon, check out our
 quick start guide with [Flink]({{< ref "flink/quick-start" >}}) or [Spark]({{< ref "spark/quick-start" >}}). It provides a step by
 step introduction to the APIs and guides you through real applications.
 

docs/content/append-table/blob.md

@@ -576,7 +576,7 @@ public class BlobDescriptorExample {
             long fileSize = 2L * 1024 * 1024 * 1024;  // 2GB
 
             BlobDescriptor descriptor = new BlobDescriptor(externalUri, 0, fileSize);
-            // file io should be accessable to externalUri
+            // file io should be accessible to externalUri
             FileIO fileIO = Table.fileIO();
             UriReader uriReader = UriReader.fromFile(fileIO);
             Blob blob = Blob.fromDescriptor(uriReader, descriptor);

docs/content/append-table/incremental-clustering.md

@@ -101,7 +101,7 @@ only support running Incremental Clustering in batch mode.
 
 To run a Incremental Clustering job, follow these instructions. 
 
-You don’t need to specify any clustering-related parameters when running Incremental Clustering,
+You don't need to specify any clustering-related parameters when running Incremental Clustering,
 these options are already defined as table options. If you need to change clustering settings, please update the corresponding table options.
 
 {{< tabs "incremental-clustering" >}}

docs/content/concepts/overview.md

@@ -56,7 +56,7 @@ For streaming engines like Apache Flink, there are typically three types of conn
   intermediate stages in this pipeline, to guarantee the latency stay
   within seconds.
 - OLAP system, such as ClickHouse, it receives processed data in
-  streaming fashion and serving user’s ad-hoc queries. 
+  streaming fashion and serving user's ad-hoc queries.
 - Batch storage, such as Apache Hive, it supports various operations
   of the traditional batch processing, including `INSERT OVERWRITE`.
 

docs/content/ecosystem/starrocks.md

@@ -79,7 +79,7 @@ SELECT * FROM paimon_catalog.test_db.partition_tbl$partitions;
 ## StarRocks to Paimon type mapping
 
 This section lists all supported type conversion between StarRocks and Paimon. 
-All StarRocks’s data types can be found in this doc [StarRocks Data type overview](https://docs.starrocks.io/docs/sql-reference/data-types/).
+All StarRocks's data types can be found in this doc [StarRocks Data type overview](https://docs.starrocks.io/docs/sql-reference/data-types/).
 
 <table class="table table-bordered">
     <thead>

docs/content/flink/procedures.md

@@ -703,7 +703,7 @@ All available procedures are listed below.
       <td>
          To expire partitions. Argument:
             <li>table: the target table identifier. Cannot be empty.</li>
-            <li>expiration_time: the expiration interval of a partition. A partition will be expired if it‘s lifetime is over this value. Partition time is extracted from the partition value.</li>
+            <li>expiration_time: the expiration interval of a partition. A partition will be expired if it's lifetime is over this value. Partition time is extracted from the partition value.</li>
             <li>timestamp_formatter: the formatter to format timestamp from string.</li>
             <li>timestamp_pattern: the pattern to get a timestamp from partitions.</li>
             <li>expire_strategy: specifies the expiration strategy for partition expiration, possible values: 'values-time' or 'update-time' , 'values-time' as default.</li>

docs/content/learn-paimon/understand-files.md

@@ -496,5 +496,5 @@ Maybe you think the 5 files for the primary key table are actually okay, but the
 may have 50 small files in a single bucket, which is very difficult to accept. Worse still, partitions that
 are no longer active also keep so many small files.
 
-Configure ‘full-compaction.delta-commits’ perform full-compaction periodically in Flink writing. And it can ensure
+Configure 'full-compaction.delta-commits' perform full-compaction periodically in Flink writing. And it can ensure
 that partitions are full compacted before writing ends.

docs/content/maintenance/filesystems.md

@@ -391,7 +391,7 @@ Please refer to [Trino S3](https://trino.io/docs/current/object-storage/file-sys
 
 ### S3 Compliant Object Stores
 
-The S3 Filesystem also support using S3 compliant object stores such as MinIO, Tencent's COS and IBM’s Cloud Object
+The S3 Filesystem also support using S3 compliant object stores such as MinIO, Tencent's COS and IBM's Cloud Object
 Storage. Just configure your endpoint to the provider of the object store service.
 
 ```yaml

docs/content/primary-key-table/chain-table.md

@@ -30,7 +30,7 @@ Chain table is a new capability for primary key tables that transforms how you p
 Imagine a scenario where you periodically store a full snapshot of data (for example, once a day), even 
 though only a small portion changes between snapshots. ODS binlog dump is a typical example of this pattern.
 
-Taking a daily binlog dump job as an example. A batch job merges yesterday’s full dataset with today’s 
+Taking a daily binlog dump job as an example. A batch job merges yesterday's full dataset with today's
 incremental changes to produce a new full dataset. This approach has two clear drawbacks:
 * Full computation: Merge operation includes all data, and it will involve shuffle, which results in poor performance.
 * Full storage: Store a full set of data every day, and the changed data usually accounts for a very small proportion.

docs/content/primary-key-table/changelog-producer.md

@@ -58,7 +58,7 @@ By specifying `'changelog-producer' = 'input'`, Paimon writers rely on their inp
 
 ## Lookup
 
-If your input can’t produce a complete changelog but you still want to get rid of the costly normalized operator, you
+If your input can't produce a complete changelog but you still want to get rid of the costly normalized operator, you
 may consider using the `'lookup'` changelog producer.
 
 By specifying `'changelog-producer' = 'lookup'`, Paimon will generate changelog through `'lookup'` during compaction (You can also enable [Async Compaction]({{< ref "primary-key-table/compaction#asynchronous-compaction" >}})). By default, lookup compaction is performed before committing written data unless disabled by `write-only` property.