# Data Model

This chapter introduces BanyanDB's data models and covers the following:

* the high-level data organization
* data model
* data retrieval

You can also find [examples](../interacting/bydbctl/schema) of how to interact with BanyanDB using [bydbctl](../interacting/bydbctl/bydbctl.md), how to create and drop groups, or how to create, read, update and drop streams/measures.

## Structure of BanyanDB

The hierarchy that data is organized into **streams**, **measures** and **properties** in groups.

![Structure of BanyanDB](https://skywalking.apache.org/doc-graph/banyandb/v0.2.0/structure.png)

### Groups

`Group` does not provide a mechanism for isolating groups of resources within a single banyand-server but is the minimal unit to manage physical structures. Each group contains a set of options, like retention policy, shard number, etc. Several shards distribute in a group.

```yaml
metadata:
  name: others
```

or

```yaml
metadata:
  name: sw_metric
catalog: CATALOG_MEASURE
resource_opts:
  shard_num: 2
  segment_interval:
    unit: UNIT_DAY
    num: 1
  ttl:
    unit: UNIT_DAY
    num: 7
```

The group creates two shards to store data points. Every day, it would create a segment that will generate a block every 2 hours. The available units are `HOUR` and `DAY`. The data in this group will keep 7 days.

Every other resource should belong to a group. The `catalog` indicates which kind of data model the group contains.

* `UNSPECIFIED`: `Property` or other data models.
* `MEASURE`: [`Measure`](#streams).
* `STREAM`: [`Stream`](#measures).

[Group Registration Operations](../api-reference.md#groupregistryservice)

### Measures

BanyanDB lets you define a measure as follows:

```yaml
metadata:
  name: service_cpm_minute
  group: sw_metric
tag_families:
- name: default
  tags:
  - name: id
    type: TAG_TYPE_STRING
  - name: entity_id
    type: TAG_TYPE_STRING
fields:
- name: total
  field_type: FIELD_TYPE_INT
  encoding_method: ENCODING_METHOD_GORILLA
  compression_method: COMPRESSION_METHOD_ZSTD
- name: value
  field_type: FIELD_TYPE_INT
  encoding_method: ENCODING_METHOD_GORILLA
  compression_method: COMPRESSION_METHOD_ZSTD
entity:
  tag_names:
  - entity_id
interval: 1m
```

`Measure` consists of a sequence of data points. Each data point contains tags and fields.

`Tags` are key-value pairs. The database engine can index tag values by referring to the [index rules and rule bindings](#indexrule--indexrulebinding), confining the query to filtering data points based on tags bound to an index rule.

`Tags` are grouped into unique `tag_families` which are the logical and physical grouping of tags.

`Measure` supports the following tag types:

* **STRING** : Text
* **INT** : 64 bits long integer
* **STRING_ARRAY** : A group of strings
* **INT_ARRAY** : A group of integers
* **DATA_BINARY** : Raw binary

A group of selected tags composite an `entity` that points out a specific time series the data point belongs to. The database engine has capacities to encode and compress values in the same time series. Users should select appropriate tag combinations to optimize the data size. Another role of `entity` is the sharding key of data points, determining how to fragment data between shards.

`Fields` are also key-value pairs like tags. But the value of each field is the actual value of a single data point. The database engine would encode and compress the field's values in the same time series. The query operation is forbidden to filter data points based on a field's value. You could apply aggregation
functions to them.

`Measure` supports the following fields types:

* **STRING** : Text
* **INT** : 64 bits long integer
* **DATA_BINARY** : Raw binary
* **FLOAT** : 64 bits double-precision floating-point number

`Measure` supports the following encoding methods:

* **GORILLA** : GORILLA encoding is lossless. It is more suitable for a numerical sequence with similar values and is not recommended for sequence data with large fluctuations.

`Measure` supports the types of the following fields:

* **ZSTD** : Zstandard is a real-time compression algorithm, that provides high compression ratios. It offers a very wide range of compression/speed trade-offs, while being backed by a very fast decoder. For BanyanDB focus on speed.

Another option named `interval` plays a critical role in encoding. It indicates the time range between two adjacent data points in a time series and implies that all data points belonging to the same time series are distributed based on a fixed interval. A better practice for the naming measure is to append the interval literal to the tail, for example, `service_cpm_minute`. It's a parameter of `GORILLA` encoding method.

[Measure Registration Operations](../api-reference.md#measureregistryservice)

#### TopNAggregation

Find the Top-N entities from a dataset in a time range is a common scenario. We could see the diagrams like "Top 10 throughput endpoints", and "Most slow 20 endpoints", etc on SkyWalking's UI. Exploring and analyzing the top entities can always reveal some high-value information.

BanyanDB introduces the `TopNAggregation`, aiming to pre-calculate the top/bottom entities during the measure writing phase. In the query phase, BanyanDB can quickly retrieve the top/bottom records. The performance would be much better than `top()` function which is based on the query phase aggregation procedure.

> Caveat: `TopNAggregation` is an approximate realization, to use it well you need have a good understanding with the algorithm as well as the data distribution.

```yaml
---
metadata:
  name: endpoint_cpm_minute_top_bottom
  group: sw_metric
source_measure:
  name: endpoint_cpm_minute
  group: sw_metric
field_name: value
field_value_sort: SORT_UNSPECIFIED
group_by_tag_names:
- entity_id
counters_number: 10000
lru_size: 10
```

`endpoint_cpm_minute_top_bottom` is watching the data ingesting of the source measure `endpoint_cpm_minute` to generate both top 1000 and bottom 1000 entity cardinalities. If only Top 1000 or Bottom 1000 is needed, the `field_value_sort` could be `DESC` or `ASC` respectively.

* SORT_DESC: Top-N. In a series of `1,2,3...1000`. Top10's result is `1000,999...991`.
* SORT_ASC: Bottom-N. In a series of `1,2,3...1000`. Bottom10's result is `1,2...10`.

Tags in `group_by_tag_names` are used as dimensions. These tags can be searched (only equality is supported) in the query phase. Tags do not exist in `group_by_tag_names` will be dropped in the pre-calculating phase.

`counters_number` denotes the number of entity cardinality. As the above example shows, calculating the Top 100 among 10 thousands is easier than among 10 millions.

`lru_size` is a late data optimizing flag. The higher the number, the more late data, but the more memory space is consumed.

[TopNAggregation Registration Operations](../api-reference.md#topnaggregationregistryservice)

### Streams

`Stream` shares many details with `Measure` except for abandoning `field`. Stream focuses on high throughput data collection, for example, tracing and logging. The database engine also supports compressing stream entries based on `entity`, but no encoding process is involved.

[Stream Registration Operations](../api-reference.md#streamregistryservice)

### Properties

`Property` is a schema-less or schema-free data model. That means you __DO NOT__ have to define a schema before writing a Property

`Property` is a standard key-value store. Users could store their metadata or items on a property and get a [sequential consistency](https://en.wikipedia.org/wiki/Consistency_model#Sequential_consistency) guarantee. BanyanDB's motivation for introducing such a particular structure is to support most APM scenarios that need to store critical data, especially for a distributed database cluster.

We should create a group before creating a property.

Creating group.

```yaml
metadata:
  name: sw
```
Creating property.

```yaml
metadata:
  container:
    group: sw
    name: temp_data
  id: General-Service
tags:
- key: name
  value:
    str:
      value: "hello"
- key: state
  value:
    str:
      value: "succeed"
```

`Property` supports a three-level hierarchy, `group`/`name`/`id`, that is more flexible than schemaful data models.

The property supports the TTL mechanism. You could set the `ttl` field to specify the time to live.

```yaml
metadata:
  container:
    group: sw
    name: temp_data
  id: General-Service
tags:
- key: name
  value:
    str:
      value: "hello"
- key: state
  value:
    str:
      value: "succeed"
ttl: "1h"
```

"General-Service" will be dropped after 1 hour. If you want to extend the TTL, you could use the "keepalive" operation. The "lease_id" is returned in the apply response. You can use get operation to get the property with the lease_id as well.

```yaml
lease_id: 1
```

"General-Service" lives another 1 hour.

You could Create, Read, Update and Drop a property, and update or drop several tags instead of the entire property.

[Property Operations](../api-reference.md#propertyservice)

## Data Models

Data models in BanyanDB derive from some classic data models.

### TimeSeries Model

A time series is a series of data points indexed in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data.

You can store time series data points through `Stream` or `Measure`. Examples of `Stream` are logs, traces and events. `Measure` could ingest metrics, profiles, etc.

### Key-Value Model

The key-value data model is a subset of the `Property` data model. Every property has a key `<group>/<name>/<id>` that identifies a property within a collection. This key acts as the primary key to retrieve the data. You can set it when creating a key. It cannot be changed later because the attribute is immutable.

There are several Key-Value pairs in a property, named `Tags`. You could add, update and drop them based on the tag's key.

## Data Retrieval

`Queries` and `Writes` are used to filter schemaful data models, `Stream`, `Measure` or `TopNAggregation` based on certain criteria, as well as to compute or store new data.

* `MeasureService` provides `Write`, `Query` and `TopN`
* `StreamService` provides `Write`, `Query`

### IndexRule & IndexRuleBinding

An `IndexRule` indicates which tags are indexed. An `IndexRuleBinding` binds an index rule to the target resources or the `subject`. There might be several rule bindings to a single resource, but their effective time range could NOT overlap.

![Index binding](https://skywalking.apache.org/doc-graph/banyandb/v0.2.0/index-binding.png)

```yaml
metadata:
  name: trace_id
  group: sw_stream
tags:
- trace_id
type: TYPE_INVERTED
```

IndexRule supports selecting two distinct kinds of index structures. The `INVERTED` index is the primary option when users set up an index rule. It's suitable for most tag indexing due to a better memory usage ratio and query performance.

```yaml
metadata:
  name: stream_binding
  group: sw_stream
rules:
- trace_id
- duration
- endpoint_id
- status_code
- http.method
- db.instance
- db.type
- mq.broker
- mq.queue
- mq.topic
- extended_tags
subject:
  catalog: CATALOG_STREAM
  name: sw
begin_at: '2021-04-15T01:30:15.01Z'
expire_at: '2121-04-15T01:30:15.01Z'
```

IndexRuleBinding binds IndexRules to a subject, `Stream` or `Measure`. The time range between `begin_at` and `expire_at` is the effective time.

[IndexRule Registration Operations](../api-reference.md#indexruleregistryservice)

[IndexRuleBinding Registration Operations](../api-reference.md#indexrulebindingregistryservice)

### Index Granularity

In BanyanDB, `Stream` and `Measure` have different levels of index granularity.

For `Measure`, the indexed target is a data point with specific tag values. The query processor uses the tag values defined in the `entity` field of the Measure to compose a series ID, which is used to find the several series that match the query criteria. The `entity` field is a set of tags that defines the unique identity of a time series, and it restricts the tags that can be used as indexed target.

Each series contains a sequence of data points that share the same tag values. Once the query processor has identified the relevant series, it scans the data points between the desired time range in those series to find the data that matches the query criteria.

For example, suppose we have a `Measure` with the following `entity` field: `{service, operation, instance}`. If we get a data point with the following tag values: `service=shopping`, `operation=search`, and `instance=prod-1`, then the query processor would use those tag values to construct a series ID that uniquely identifies the series containing that data point. The query processor would then scan the relevant data points in that series to find the data that matches the query criteria.

The side effect of the measure index is that each indexed value has to represent a unique seriesID. This is because the series ID is constructed by concatenating the indexed tag values in the `entity` field. If two series have the same `entity` field, they would have the same series ID and would be indistinguishable from one another. This means that if you want to index a tag that is not part of the `entity` field, you would need to ensure that it is unique across all series. One way to do this would be to include the tag in the `entity` field, but this may not always be feasible or desirable depending on your use case.

For `Stream`, the indexed target is an element that is a combination of the series ID and timestamp. The `Stream` query processor uses the time range to find target files. The indexed result points to the target element. The processor doesn't have to scan a series of elements in this time range, which reduces the query time.

For example, suppose we have a `Stream` with the following tags: `service`, `operation`, `instance`, and `status_code`. If we get a data point with the following tag values: `service=shopping`, `operation=search`, `instance=prod-1`, and `status_code=200`, and the data point's time is 1:00pm on January 1st, 2022, then the series ID for this data point would be `shopping_search_prod-1_200_1641052800`, where `1641052800` is the Unix timestamp representing 1:00pm on January 1st, 2022.

The indexed target would be the combination of the series ID and timestamp, which in this case would be `shopping_search_prod-1_200_1641052800`. The `Stream` query processor would use the time range specified in the query to find target files and then search within those files for the indexed target.

The following is a comparison of the indexing granularity, performance, and flexibility of `Stream` and `Measure` indices:

| Indexing Granularity                                                                                                                                     | Performance                                | Flexibility                                                                                                     |
|----------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------|-----------------------------------------------------------------------------------------------------------------|
| Measure indices are constructed for each series and are based on the entity field of the Measure. Each indexed value has to represent a unique seriesID. | Measure index is faster than Stream index. | Measure index is less flexible and requires more care when indexing tags that are not part of the entity field. |
| Stream indices are constructed for each element and are based on the series ID and timestamp.                                                            | Stream index is slower than Measure index. | Stream index is more flexible than Measure index and can index any tag value.                                   |

In general, `Measure` indices are faster and more efficient, but they require more care when indexing tags that are not part of the `entity` field. `Stream` indices, on the other hand, are slower and take up more space, but they can index any tag value and do not have the same side effects as `Measure` indices.
