pub struct KafkaSourceReader {Show 16 fields
topic_name: String,
source_name: String,
id: GlobalId,
consumer: Arc<BaseConsumer<TunnelingClientContext<GlueConsumerContext>>>,
partition_consumers: Vec<PartitionConsumer>,
worker_id: usize,
worker_count: usize,
last_offsets: BTreeMap<usize, BTreeMap<PartitionId, i64>>,
start_offsets: BTreeMap<PartitionId, i64>,
stats_rx: Receiver<Jsonb>,
progress_statistics: Arc<Mutex<PartialProgressStatistics>>,
partition_info: Arc<Mutex<Option<(Timestamp, BTreeMap<PartitionId, WatermarkOffsets>)>>>,
_metadata_thread_handle: UnparkOnDropHandle<()>,
partition_metrics: KafkaSourceMetrics,
health_status: Arc<Mutex<HealthStatus>>,
partition_capabilities: BTreeMap<PartitionId, PartitionCapability>,
}
Expand description
Contains all information necessary to ingest data from Kafka
Fields§
§topic_name: String
Name of the topic on which this source is backed on
source_name: String
Name of the source (will have format kafka-source-id)
id: GlobalId
Source global ID
consumer: Arc<BaseConsumer<TunnelingClientContext<GlueConsumerContext>>>
Kafka consumer for this source
partition_consumers: Vec<PartitionConsumer>
List of consumers. A consumer should be assigned per partition to guarantee fairness
worker_id: usize
Worker ID
worker_count: usize
Total count of workers
last_offsets: BTreeMap<usize, BTreeMap<PartitionId, i64>>
The most recently read offset for each partition known to this source reader by output-index. An offset of -1 indicates that no prior message has been read for the given partition.
start_offsets: BTreeMap<PartitionId, i64>
The offset to start reading from for each partition.
stats_rx: Receiver<Jsonb>
Channel to receive Kafka statistics JSON blobs from the stats callback.
progress_statistics: Arc<Mutex<PartialProgressStatistics>>
Progress statistics as collected from the resume_uppers
stream and the partition metadata
thread.
partition_info: Arc<Mutex<Option<(Timestamp, BTreeMap<PartitionId, WatermarkOffsets>)>>>
The last partition info we received. For each partition we also fetch the high watermark.
_metadata_thread_handle: UnparkOnDropHandle<()>
A handle to the spawned metadata thread
partition_metrics: KafkaSourceMetrics
A handle to the partition specific metrics
health_status: Arc<Mutex<HealthStatus>>
The latest status detected by the metadata refresh thread.
partition_capabilities: BTreeMap<PartitionId, PartitionCapability>
Per partition capabilities used to produce messages
Implementations§
Source§impl KafkaSourceReader
impl KafkaSourceReader
Sourcefn ensure_partition(&mut self, pid: PartitionId)
fn ensure_partition(&mut self, pid: PartitionId)
Ensures that a partition queue for pid
exists.
Sourcefn create_partition_queue(
&mut self,
partition_id: PartitionId,
initial_offset: Offset,
)
fn create_partition_queue( &mut self, partition_id: PartitionId, initial_offset: Offset, )
Creates a new partition queue for partition_id
.
Sourcefn update_stats(&mut self)
fn update_stats(&mut self)
Read any statistics JSON blobs generated via the rdkafka statistics callback.
Sourcefn handle_message(
&mut self,
message: Result<SourceMessage, KafkaHeaderParseError>,
(partition, offset): (PartitionId, MzOffset),
output_index: &usize,
) -> Option<(Result<SourceMessage, KafkaHeaderParseError>, KafkaTimestamp, Diff)>
fn handle_message( &mut self, message: Result<SourceMessage, KafkaHeaderParseError>, (partition, offset): (PartitionId, MzOffset), output_index: &usize, ) -> Option<(Result<SourceMessage, KafkaHeaderParseError>, KafkaTimestamp, Diff)>
Checks if the given message is viable for emission. This checks if the message offset is past the expected offset and returns None if it is not.
Auto Trait Implementations§
impl Freeze for KafkaSourceReader
impl !RefUnwindSafe for KafkaSourceReader
impl !Send for KafkaSourceReader
impl !Sync for KafkaSourceReader
impl Unpin for KafkaSourceReader
impl !UnwindSafe for KafkaSourceReader
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