Superskyyy commented on code in PR #435: URL: https://github.com/apache/skywalking-banyandb/pull/435#discussion_r1575638980
########## docs/concept/tsdb.md: ########## @@ -2,37 +2,58 @@ TSDB is a time-series storage engine designed to store and query large volumes of time-series data. One of the key features of TSDB is its ability to automatically manage data storage over time, optimize performance and ensure that the system can scale to handle large workloads. TSDB empowers `Measure` and `Stream` relevant data. - ## Shard In TSDB, the data in a group is partitioned into shards based on a configurable sharding scheme. Each shard is assigned to a specific set of storage nodes, and those nodes store and process the data within that shard. This allows BanyanDB to scale horizontally by adding more storage nodes to the cluster as needed. +Within each shard, data is stored in different segments based on time ranges. The series index generated based on entities, and the indexes generated based on indexing rules of the `Measure` types are also stored under the shard. Review Comment: ```suggestion Within each shard, data is stored in different segments based on time ranges. The series indexes are generated based on entities, and the indexes generated based on indexing rules of the `Measure` types are also stored under the shard. ``` ########## docs/concept/tsdb.md: ########## @@ -2,37 +2,58 @@ TSDB is a time-series storage engine designed to store and query large volumes of time-series data. One of the key features of TSDB is its ability to automatically manage data storage over time, optimize performance and ensure that the system can scale to handle large workloads. TSDB empowers `Measure` and `Stream` relevant data. - ## Shard In TSDB, the data in a group is partitioned into shards based on a configurable sharding scheme. Each shard is assigned to a specific set of storage nodes, and those nodes store and process the data within that shard. This allows BanyanDB to scale horizontally by adding more storage nodes to the cluster as needed. +Within each shard, data is stored in different segments based on time ranges. The series index generated based on entities, and the indexes generated based on indexing rules of the `Measure` types are also stored under the shard. + + + +## Segment + +Each segment is composed of multiple parts. Whenever SkyWalking sends a batch of data, BanyanDB writes this batch of data into a new Part. For data of the `Stream` type, the inverted indexes generated based on the indexing rules are also stored in the segment. Since BanyanDB adopts a snapshot approach for data read and write operations, the segment also needs to maintain additional snapshot information to record the validity of the parts. + + + +## Part + +Within a part, data is split into multiple files in a columnar manner. The timestamps are stored in the `timestamps.bin` file, tags are organized in persistent tag families as various files with the `.tf` suffix, and fields are stored separately in the `fields.bin` file. -[shard](https://skywalking.apache.org/doc-graph/banyandb/v0.4.0/tsdb-shard.png) +In addition, each part maintains several metadata files. Among them, `metadata.json` is the metadata file for the part, storing descriptive information, such as start and end times, part size, etc. -* Buffer: It is typically implemented as an in-memory queue managed by a shard. When new time-series data is ingested into the system, it is added to the end of the queue, and when the buffer reaches a specific size, the data is flushed to disk in batches. -* SST: When a bucket of buffer becomes full or reaches a certain size threshold, it is flushed to disk as a new Sorted String Table (SST) file. This process is known as compaction. -* Segments and Blocks: Time-series data is stored in data segments/blocks within each shard. Blocks contain a fixed number of data points and are organized into time windows. Each data segment includes an index that efficiently retrieves data within the block. -* Block Cache: It manages the in-memory cache of data blocks, improving query performance by caching frequently accessed data blocks in memory. +The `meta.bin` is a skipping index file serves as the entry file for the entire part, helping to index the `primary.bin` file. + +The `primary.bin` file contains the index of each block. Through it, the actual data files or the tagFamily metadata files ending with `.tfm` can be indexed, which in turn helps locate the data in blocks. Review Comment: ```suggestion The `primary.bin` file contains the index of each block. Through it, the actual data files or the tagFamily metadata files ending with `.tfm` can be indexed, which in turn helps locating the data in blocks. ``` ########## docs/concept/tsdb.md: ########## @@ -2,37 +2,58 @@ TSDB is a time-series storage engine designed to store and query large volumes of time-series data. One of the key features of TSDB is its ability to automatically manage data storage over time, optimize performance and ensure that the system can scale to handle large workloads. TSDB empowers `Measure` and `Stream` relevant data. - ## Shard In TSDB, the data in a group is partitioned into shards based on a configurable sharding scheme. Each shard is assigned to a specific set of storage nodes, and those nodes store and process the data within that shard. This allows BanyanDB to scale horizontally by adding more storage nodes to the cluster as needed. +Within each shard, data is stored in different segments based on time ranges. The series index generated based on entities, and the indexes generated based on indexing rules of the `Measure` types are also stored under the shard. + + + +## Segment + +Each segment is composed of multiple parts. Whenever SkyWalking sends a batch of data, BanyanDB writes this batch of data into a new Part. For data of the `Stream` type, the inverted indexes generated based on the indexing rules are also stored in the segment. Since BanyanDB adopts a snapshot approach for data read and write operations, the segment also needs to maintain additional snapshot information to record the validity of the parts. + + + +## Part + +Within a part, data is split into multiple files in a columnar manner. The timestamps are stored in the `timestamps.bin` file, tags are organized in persistent tag families as various files with the `.tf` suffix, and fields are stored separately in the `fields.bin` file. -[shard](https://skywalking.apache.org/doc-graph/banyandb/v0.4.0/tsdb-shard.png) +In addition, each part maintains several metadata files. Among them, `metadata.json` is the metadata file for the part, storing descriptive information, such as start and end times, part size, etc. -* Buffer: It is typically implemented as an in-memory queue managed by a shard. When new time-series data is ingested into the system, it is added to the end of the queue, and when the buffer reaches a specific size, the data is flushed to disk in batches. -* SST: When a bucket of buffer becomes full or reaches a certain size threshold, it is flushed to disk as a new Sorted String Table (SST) file. This process is known as compaction. -* Segments and Blocks: Time-series data is stored in data segments/blocks within each shard. Blocks contain a fixed number of data points and are organized into time windows. Each data segment includes an index that efficiently retrieves data within the block. -* Block Cache: It manages the in-memory cache of data blocks, improving query performance by caching frequently accessed data blocks in memory. +The `meta.bin` is a skipping index file serves as the entry file for the entire part, helping to index the `primary.bin` file. + +The `primary.bin` file contains the index of each block. Through it, the actual data files or the tagFamily metadata files ending with `.tfm` can be indexed, which in turn helps locate the data in blocks. + +Notably, for data of the `Stream` type, since there are no field columns, the `fields.bin` file does not exist, while the rest of the structure is entirely consistent with the `Measure` type. + + + + +## Block + +The diagram below shows the detailed fields within each block. The block is the minimal unit of tsdb, which contains several rows of data. Due to the column-based design, each block is spread over several files. + + + ## Write Path The write path of TSDB begins when time-series data is ingested into the system. TSDB will consult the schema repository to check if the group exists, and if it does, then it will hash the SeriesID to determine which shard it belongs to. -Each shard in TSDB is responsible for storing a subset of the time-series data, and it uses a write-ahead log to record incoming writes in a durable and fault-tolerant manner. The shard also holds an in-memory index allowing fast lookups of time-series data. +Each shard in TSDB is responsible for storing a subset of the time-series data. The shard also holds an in-memory index allowing fast lookups of time-series data. -When a shard receives a write request, the data is written to the buffer as a series of buckets. Each bucket is a fixed-size chunk of time-series data typically configured to be several minutes or hours long. As new data is written to the buffer, it is appended to the current bucket until it is full. Once the bucket is full, it is closed, and a new bucket is created to continue buffering writes. +When a shard receives a write request, the data is written to the buffer as a memory part and the series index and inverted index will also be updated. The worker in the background periodically flushes data, writing the memory part to the disk. After the flush operation is completed, it triggers a merge operation to combine the parts and remove invalid data. -Once a bucket is closed, it is stored as a single SST in a shard. The file is indexed and added to the index for the corresponding time range and resolution. +Whenever a new memory part is generated or a flush and merge operation is triggered, it initiates an update of the snapshot and deletes outdated snapshots. ## Read Path -The read path in TSDB retrieves time-series data from disk or memory and returns it to the query engine. The read path comprises several components: the buffer, cache, and SST file. The following is a high-level overview of how these components work together to retrieve time-series data in TSDB. - -The first step in the read path is to perform an index lookup to determine which blocks contain the desired time range. The index contains metadata about each data block, including its start and end time and its location on disk. +The read path in TSDB retrieves time-series data from disk or memory and returns it to the query engine. The read path comprises several components: the buffer and parts. The following is a high-level overview of how these components work together to retrieve time-series data in TSDB. Review Comment: ```suggestion The read path in TSDB retrieves time-series data from disk or memory, and returns it to the query engine. The read path comprises several components: the buffer and parts. The following is a high-level overview of how these components work together to retrieve time-series data in TSDB. ``` ########## docs/concept/tsdb.md: ########## @@ -2,37 +2,58 @@ TSDB is a time-series storage engine designed to store and query large volumes of time-series data. One of the key features of TSDB is its ability to automatically manage data storage over time, optimize performance and ensure that the system can scale to handle large workloads. TSDB empowers `Measure` and `Stream` relevant data. - ## Shard In TSDB, the data in a group is partitioned into shards based on a configurable sharding scheme. Each shard is assigned to a specific set of storage nodes, and those nodes store and process the data within that shard. This allows BanyanDB to scale horizontally by adding more storage nodes to the cluster as needed. +Within each shard, data is stored in different segments based on time ranges. The series index generated based on entities, and the indexes generated based on indexing rules of the `Measure` types are also stored under the shard. + + + +## Segment + +Each segment is composed of multiple parts. Whenever SkyWalking sends a batch of data, BanyanDB writes this batch of data into a new Part. For data of the `Stream` type, the inverted indexes generated based on the indexing rules are also stored in the segment. Since BanyanDB adopts a snapshot approach for data read and write operations, the segment also needs to maintain additional snapshot information to record the validity of the parts. Review Comment: Let's be consistent with the capitalization of "Part" or define it first before segment. ########## docs/concept/tsdb.md: ########## @@ -2,37 +2,58 @@ TSDB is a time-series storage engine designed to store and query large volumes of time-series data. One of the key features of TSDB is its ability to automatically manage data storage over time, optimize performance and ensure that the system can scale to handle large workloads. TSDB empowers `Measure` and `Stream` relevant data. - ## Shard In TSDB, the data in a group is partitioned into shards based on a configurable sharding scheme. Each shard is assigned to a specific set of storage nodes, and those nodes store and process the data within that shard. This allows BanyanDB to scale horizontally by adding more storage nodes to the cluster as needed. +Within each shard, data is stored in different segments based on time ranges. The series index generated based on entities, and the indexes generated based on indexing rules of the `Measure` types are also stored under the shard. + + + +## Segment + +Each segment is composed of multiple parts. Whenever SkyWalking sends a batch of data, BanyanDB writes this batch of data into a new Part. For data of the `Stream` type, the inverted indexes generated based on the indexing rules are also stored in the segment. Since BanyanDB adopts a snapshot approach for data read and write operations, the segment also needs to maintain additional snapshot information to record the validity of the parts. + + + +## Part + +Within a part, data is split into multiple files in a columnar manner. The timestamps are stored in the `timestamps.bin` file, tags are organized in persistent tag families as various files with the `.tf` suffix, and fields are stored separately in the `fields.bin` file. -[shard](https://skywalking.apache.org/doc-graph/banyandb/v0.4.0/tsdb-shard.png) +In addition, each part maintains several metadata files. Among them, `metadata.json` is the metadata file for the part, storing descriptive information, such as start and end times, part size, etc. -* Buffer: It is typically implemented as an in-memory queue managed by a shard. When new time-series data is ingested into the system, it is added to the end of the queue, and when the buffer reaches a specific size, the data is flushed to disk in batches. -* SST: When a bucket of buffer becomes full or reaches a certain size threshold, it is flushed to disk as a new Sorted String Table (SST) file. This process is known as compaction. -* Segments and Blocks: Time-series data is stored in data segments/blocks within each shard. Blocks contain a fixed number of data points and are organized into time windows. Each data segment includes an index that efficiently retrieves data within the block. -* Block Cache: It manages the in-memory cache of data blocks, improving query performance by caching frequently accessed data blocks in memory. +The `meta.bin` is a skipping index file serves as the entry file for the entire part, helping to index the `primary.bin` file. + +The `primary.bin` file contains the index of each block. Through it, the actual data files or the tagFamily metadata files ending with `.tfm` can be indexed, which in turn helps locate the data in blocks. + +Notably, for data of the `Stream` type, since there are no field columns, the `fields.bin` file does not exist, while the rest of the structure is entirely consistent with the `Measure` type. + + + + +## Block + +The diagram below shows the detailed fields within each block. The block is the minimal unit of tsdb, which contains several rows of data. Due to the column-based design, each block is spread over several files. + + + ## Write Path The write path of TSDB begins when time-series data is ingested into the system. TSDB will consult the schema repository to check if the group exists, and if it does, then it will hash the SeriesID to determine which shard it belongs to. -Each shard in TSDB is responsible for storing a subset of the time-series data, and it uses a write-ahead log to record incoming writes in a durable and fault-tolerant manner. The shard also holds an in-memory index allowing fast lookups of time-series data. +Each shard in TSDB is responsible for storing a subset of the time-series data. The shard also holds an in-memory index allowing fast lookups of time-series data. -When a shard receives a write request, the data is written to the buffer as a series of buckets. Each bucket is a fixed-size chunk of time-series data typically configured to be several minutes or hours long. As new data is written to the buffer, it is appended to the current bucket until it is full. Once the bucket is full, it is closed, and a new bucket is created to continue buffering writes. +When a shard receives a write request, the data is written to the buffer as a memory part and the series index and inverted index will also be updated. The worker in the background periodically flushes data, writing the memory part to the disk. After the flush operation is completed, it triggers a merge operation to combine the parts and remove invalid data. Review Comment: ```suggestion When a shard receives a write request, the data is written to the buffer as a memory part. Meanwhile, the series index and inverted index will also be updated. The worker in the background periodically flushes data, writing the memory part to the disk. After the flush operation is completed, it triggers a merge operation to combine the parts and remove invalid data. ``` ########## docs/concept/tsdb.md: ########## @@ -2,37 +2,58 @@ TSDB is a time-series storage engine designed to store and query large volumes of time-series data. One of the key features of TSDB is its ability to automatically manage data storage over time, optimize performance and ensure that the system can scale to handle large workloads. TSDB empowers `Measure` and `Stream` relevant data. - ## Shard In TSDB, the data in a group is partitioned into shards based on a configurable sharding scheme. Each shard is assigned to a specific set of storage nodes, and those nodes store and process the data within that shard. This allows BanyanDB to scale horizontally by adding more storage nodes to the cluster as needed. +Within each shard, data is stored in different segments based on time ranges. The series index generated based on entities, and the indexes generated based on indexing rules of the `Measure` types are also stored under the shard. + + + +## Segment + +Each segment is composed of multiple parts. Whenever SkyWalking sends a batch of data, BanyanDB writes this batch of data into a new Part. For data of the `Stream` type, the inverted indexes generated based on the indexing rules are also stored in the segment. Since BanyanDB adopts a snapshot approach for data read and write operations, the segment also needs to maintain additional snapshot information to record the validity of the parts. + + + +## Part + +Within a part, data is split into multiple files in a columnar manner. The timestamps are stored in the `timestamps.bin` file, tags are organized in persistent tag families as various files with the `.tf` suffix, and fields are stored separately in the `fields.bin` file. -[shard](https://skywalking.apache.org/doc-graph/banyandb/v0.4.0/tsdb-shard.png) +In addition, each part maintains several metadata files. Among them, `metadata.json` is the metadata file for the part, storing descriptive information, such as start and end times, part size, etc. -* Buffer: It is typically implemented as an in-memory queue managed by a shard. When new time-series data is ingested into the system, it is added to the end of the queue, and when the buffer reaches a specific size, the data is flushed to disk in batches. -* SST: When a bucket of buffer becomes full or reaches a certain size threshold, it is flushed to disk as a new Sorted String Table (SST) file. This process is known as compaction. -* Segments and Blocks: Time-series data is stored in data segments/blocks within each shard. Blocks contain a fixed number of data points and are organized into time windows. Each data segment includes an index that efficiently retrieves data within the block. -* Block Cache: It manages the in-memory cache of data blocks, improving query performance by caching frequently accessed data blocks in memory. +The `meta.bin` is a skipping index file serves as the entry file for the entire part, helping to index the `primary.bin` file. + +The `primary.bin` file contains the index of each block. Through it, the actual data files or the tagFamily metadata files ending with `.tfm` can be indexed, which in turn helps locate the data in blocks. + +Notably, for data of the `Stream` type, since there are no field columns, the `fields.bin` file does not exist, while the rest of the structure is entirely consistent with the `Measure` type. + + + + +## Block + +The diagram below shows the detailed fields within each block. The block is the minimal unit of tsdb, which contains several rows of data. Due to the column-based design, each block is spread over several files. + + + ## Write Path The write path of TSDB begins when time-series data is ingested into the system. TSDB will consult the schema repository to check if the group exists, and if it does, then it will hash the SeriesID to determine which shard it belongs to. -Each shard in TSDB is responsible for storing a subset of the time-series data, and it uses a write-ahead log to record incoming writes in a durable and fault-tolerant manner. The shard also holds an in-memory index allowing fast lookups of time-series data. +Each shard in TSDB is responsible for storing a subset of the time-series data. The shard also holds an in-memory index allowing fast lookups of time-series data. -When a shard receives a write request, the data is written to the buffer as a series of buckets. Each bucket is a fixed-size chunk of time-series data typically configured to be several minutes or hours long. As new data is written to the buffer, it is appended to the current bucket until it is full. Once the bucket is full, it is closed, and a new bucket is created to continue buffering writes. +When a shard receives a write request, the data is written to the buffer as a memory part and the series index and inverted index will also be updated. The worker in the background periodically flushes data, writing the memory part to the disk. After the flush operation is completed, it triggers a merge operation to combine the parts and remove invalid data. -Once a bucket is closed, it is stored as a single SST in a shard. The file is indexed and added to the index for the corresponding time range and resolution. +Whenever a new memory part is generated or a flush and merge operation is triggered, it initiates an update of the snapshot and deletes outdated snapshots. Review Comment: ```suggestion Whenever a new memory part is generated, or when a flush and merge operation is triggered, they initiate an update of the snapshot and delete outdated snapshots. ``` -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
