docs/generated/spark_connector_configuration.html

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<table class="configuration table table-bordered">
    <thead>
        <tr>
            <th class="text-left" style="width: 20%">Key</th>
            <th class="text-left" style="width: 15%">Default</th>
            <th class="text-left" style="width: 10%">Type</th>
            <th class="text-left" style="width: 55%">Description</th>
        </tr>
    </thead>
    <tbody>
        <tr>
            <td><h5>read.allow.fullScan</h5></td>
            <td style="word-wrap: break-word;">true</td>
            <td>Boolean</td>
            <td>Whether to allow full scan when reading a partitioned table.</td>
        </tr>
        <tr>
            <td><h5>read.changelog</h5></td>
            <td style="word-wrap: break-word;">false</td>
            <td>Boolean</td>
            <td>Whether to read row in the form of changelog (add rowkind column in row to represent its change type).</td>
        </tr>
        <tr>
            <td><h5>read.stream.maxBytesPerTrigger</h5></td>
            <td style="word-wrap: break-word;">(none)</td>
            <td>Long</td>
            <td>The maximum number of bytes returned in a single batch.</td>
        </tr>
        <tr>
            <td><h5>read.stream.maxFilesPerTrigger</h5></td>
            <td style="word-wrap: break-word;">(none)</td>
            <td>Integer</td>
            <td>The maximum number of files returned in a single batch.</td>
        </tr>
        <tr>
            <td><h5>read.stream.maxRowsPerTrigger</h5></td>
            <td style="word-wrap: break-word;">(none)</td>
            <td>Long</td>
            <td>The maximum number of rows returned in a single batch.</td>
        </tr>
        <tr>
            <td><h5>read.stream.maxTriggerDelayMs</h5></td>
            <td style="word-wrap: break-word;">(none)</td>
            <td>Long</td>
            <td>The maximum delay between two adjacent batches, which used to create MinRowsReadLimit with read.stream.minRowsPerTrigger together.</td>
        </tr>
        <tr>
            <td><h5>read.stream.minRowsPerTrigger</h5></td>
            <td style="word-wrap: break-word;">(none)</td>
            <td>Long</td>
            <td>The minimum number of rows returned in a single batch, which used to create MinRowsReadLimit with read.stream.maxTriggerDelayMs together.</td>
        </tr>
        <tr>
            <td><h5>requiredSparkConfsCheck.enabled</h5></td>
            <td style="word-wrap: break-word;">true</td>
            <td>Boolean</td>
            <td>Whether to verify SparkSession is initialized with required configurations.</td>
        </tr>
        <tr>
            <td><h5>source.split.target-size-with-column-pruning</h5></td>
            <td style="word-wrap: break-word;">false</td>
            <td>Boolean</td>
            <td>Whether to adjust the target split size based on pruned (projected) columns. If enabled, split size estimation uses only the columns actually being read.</td>
        </tr>
        <tr>
            <td><h5>write.merge-schema</h5></td>
            <td style="word-wrap: break-word;">false</td>
            <td>Boolean</td>
            <td>If true, evolve the table schema to accept new columns from the incoming data. Existing column types are preserved and incoming values are cast to them; to also widen existing types, enable 'write.merge-schema.type-widening'.</td>
        </tr>
        <tr>
            <td><h5>write.merge-schema.explicit-cast</h5></td>
            <td style="word-wrap: break-word;">false</td>
            <td>Boolean</td>
            <td>Only effective when 'write.merge-schema.type-widening' is true. If true, also allow lossy type changes between compatible types (e.g. BIGINT -&gt; INT, STRING -&gt; DATE).</td>
        </tr>
        <tr>
            <td><h5>write.merge-schema.type-widening</h5></td>
            <td style="word-wrap: break-word;">false</td>
            <td>Boolean</td>
            <td>Only effective when 'write.merge-schema' is true. If true, widen an existing column type when the incoming data has a wider compatible type (e.g. INT -&gt; BIGINT, DECIMAL precision increase). Lossy changes are still rejected unless 'write.merge-schema.explicit-cast' is also true.</td>
        </tr>
        <tr>
            <td><h5>write.use-v2-write</h5></td>
            <td style="word-wrap: break-word;">false</td>
            <td>Boolean</td>
            <td>If true, v2 write will be used. Currently, only HASH_FIXED and BUCKET_UNAWARE bucket modes are supported. Will fall back to v1 write for other bucket modes. Currently, Spark V2 write does not support TableCapability.STREAMING_WRITE.</td>
        </tr>
    </tbody>
</table>