Documentation ¶
Index ¶
- Constants
- Variables
- func NotifyLevel(notifyLevelName string) int
- func NotifyLevelName(notifyLevel int) string
- type ArrayItems
- type Config
- type EntityType
- type Environment
- type Event
- type EventProcessingResult
- type ExcludeEventsWith
- type ExecutorStatus
- type ExtractFields
- type ExtractItemsFromArray
- type Extractor
- type ExtractorFactories
- type ExtractorFactory
- type ExtractorQuery
- type Field
- type ForEventsWith
- type HookAction
- type IdFromItemFields
- type KeyValueFilter
- type Loader
- type LoaderFactories
- type LoaderFactory
- type Metrics
- type NotificationEvent
- type NotifyChan
- type Ops
- type PostTransformHookFunc
- type PreTransformHookFunc
- type ProcessEventFunc
- type Property
- type QueryType
- type Regexp
- type RowItem
- type Sink
- type SinkConfig
- type Source
- type SourceConfig
- type Spec
- type TimeConv
- type Transform
- type Transformed
- type TransformedItemMap
Constants ¶
const ( NotifyLevelInvalid = iota NotifyLevelDebug NotifyLevelInfo NotifyLevelWarn NotifyLevelError )
const ( NotifyLevelStrInvalid = "INVALID" NotifyLevelStrDebug = "DEBUG" NotifyLevelStrInfo = "INFO" NotifyLevelStrWarn = "WARN" NotifyLevelStrError = "ERROR" )
const ( DefaultStreamsPerPod = 1 DefaultMicroBatchSize = 500 DefaultMicroBatchBytes = 5000000 DefaultMicroBatchTimeoutMs = 15000 DefaultMaxEventProcessingRetries = 5 DefaultMaxStreamRetryBackoffIntervalSec = 300 )
General Ops defaults
const ( HoueDefault = "default" HoueDiscard = "discard" HoueDlq = "dlq" HoueFail = "fail" )
Available options for Ops.HandlingOfUnretryableEvents
const ( TransformedKeyKey = "key" TransformedValueKey = "value" )
const GeistIngestionTime = "@GeistIngestionTime"
Data processing and ingestion options
Variables ¶
var ErrEntityShutdownRequested = errors.New("entity shutdown requested")
An entity can request to be shut down. This error code should be returned and it's up to the Executor to decide if entire stream should be shutdown or any other action to be taken.
var ReservedEntityNames = map[string]bool{ string(EntityInvalid): true, string(EntityVoid): true, string(EntityAdmin): true, string(EntityGeistApi): true, string(EntityEventSim): true, }
Functions ¶
func NotifyLevel ¶ added in v0.4.2
func NotifyLevelName ¶ added in v0.4.0
Types ¶
type ArrayItems ¶
type ArrayItems struct { JsonPathToArray string `json:"jsonPathToArray"` IdFromItemFields IdFromItemFields `json:"idFromItemFields"` }
type Config ¶ added in v0.4.0
type Config struct { Spec *Spec ID string NotifyChan NotifyChan Log bool }
Config is the Entity Config to use with Entity factories
type EntityType ¶
type EntityType string
Native stream entity types (sources, sinks or both)
const ( EntityInvalid EntityType = "invalid" EntityVoid EntityType = "void" EntityAdmin EntityType = "admin" EntityGeistApi EntityType = "geistapi" EntityEventSim EntityType = "eventsim" )
type Environment ¶
type Environment string
Some Stream ETL Entities need different configurations based on environements. This is not possible to set in the generic GEIST build config since ETL entities are configured in externally provided ETL Stream Specs. The environment concept is therefore required to be known to the entity and to the stream spec.
The following env types are provided by Geist for consistency across entity plugins, but any type of custom string can be used by plugin entities. For example, a custom plugin extractor could support having "env": "someregion-staging" in the stream spec using that extractor/source, since the extractor implementation can cast the Environment type back to string when matching.
const ( EnvironmentAll Environment = "all" EnvironmentDev Environment = "dev" EnvironmentStage Environment = "stage" EnvironmentProd Environment = "prod" )
type EventProcessingResult ¶
type EventProcessingResult struct { Status ExecutorStatus ResourceId string Error error Retryable bool }
type ExcludeEventsWith ¶
type ExcludeEventsWith struct { Key string `json:"key"` Values []string `json:"values,omitempty"` ValuesNotIn []string `json:"valuesNotIn,omitempty"` ValueIsEmpty *bool `json:"valueIsEmpty,omitempty"` }
ExcludeEventsWith specifies if certain events should be skipped directly, without further processing. If the event field as specified by the Key field matches any of the values in the Values array the event will be excluded. This is the Blacklisting option of this filter. The Key string must be on a JSON path syntax according to github.com/tidwall/gjson (see below). The value field is currently limited to string values.
If Values array is missing or empty a check will be done on ValuesNotIn. If the event field as specified by the Key field does not have a value matching any of the values in the ValuesNotIn field, the event is excluded. This is the Whitelisting option of this filter.
If ValueIsEmpty is set to true and the field string value is empty, the event will be excluded.
type ExecutorStatus ¶
type ExecutorStatus int
const ( ExecutorStatusInvalid ExecutorStatus = iota ExecutorStatusSuccessful ExecutorStatusError ExecutorStatusRetriesExhausted ExecutorStatusShutdown )
type ExtractFields ¶
type ExtractFields struct { // ForEventsWith is used to filter which incoming event the fields should be extracted from // Currently only AND type filter is supported if supplying multiple key-value pairs. // If ForEventsWith is empty or omitted, fields will be taken from all events. ForEventsWith []ForEventsWith `json:"forEventsWith,omitempty"` // ExcludeEventsWith inside ExtractField complements the top level ExcludeEventsWith by // only being applicable for the events filtered with ForEventsWith. // As an example, this simplifies event schema evolution whereby a field containing the // schema version number could be governed by ForEventsWith, and event exclusion (and // field extraction) are handled by this and subsequent specification constructs. ExcludeEventsWith []ExcludeEventsWith `json:"excludeEventsWith,omitempty"` // The Fields a array contains the definitions of which fields to extract for the filtered-out event Fields []Field `json:"fields,omitempty"` }
The ExtractFields transformation type creates root level ID fields, with values retrieved from a json path expression from the input event
type ExtractItemsFromArray ¶
type ExtractItemsFromArray struct { Id string `json:"id"` ForEventsWith []ForEventsWith `json:"forEventsWith,omitempty"` Items ArrayItems `json:"items"` }
ExtractItemsFromArray transformation returns all items in an arbitrary array inside the event json with the ID/key of each item according to the required IdFromItemFields spec. If the resulting ID/key of each item is an empty string the item will be omitted from the output map. The items will be stored inside a map in the transformed output map. It's key is specified by the "Id" field.
type Extractor ¶
type Extractor interface { // StreamExtract (required) continuously consumes events from its source (until ctx is canceled), // and report each consumed event back to Executor with reportEvent(), for further processing. StreamExtract( ctx context.Context, reportEvent ProcessEventFunc, err *error, retryable *bool) // Extract (optional) provides generic extraction from the source based on the provided query, // and returns directly. Extract(ctx context.Context, query ExtractorQuery, result any) (error, bool) // ExtractFromSink (optional) extracts data from the sink used in an ETL Stream, as specified // in the Extractors GEIST spec. Currently only supported by Firestore and BigTable extractors. ExtractFromSink(ctx context.Context, query ExtractorQuery, result *[]*Transformed) (error, bool) // SendToSource (optional) enables external clients to send events directly to the Extractor's // Source with Geist.Publish(). // For source connectors meant to be used in admin streams, this method is required. // Currently known connectors that implement this method are: // * "geistapi" (channel) extractor // * "pubsub" GCP extractor // * "kafka" extractor SendToSource(ctx context.Context, event any) (string, error) }
Extractor is the interface required for stream source extractor implementations and for sink queries. The Extractor implementation should be given its GEIST Spec in a constructor.
For source stream extractors the only function required to be fully functional is StreamExtract().
For sink extractors the only function required to be fully functional is ExtractFromSink().
The others are situational depending on extractor/source entity type, and could be empty, e.g. simply returning nil, false (or someerror, false).
type ExtractorFactories ¶
type ExtractorFactories map[string]ExtractorFactory
type ExtractorFactory ¶
type ExtractorFactory interface { // SourceId returns the source ID for which the extractor is implemented SourceId() string // NewExtractor creates a new extractor entity NewExtractor(ctx context.Context, c Config) (Extractor, error) // Close is called by Geist after client has called Geist API geist.Shutdown() Close(ctx context.Context) error }
ExtractorFactory enables loaders/sinks to be handled as plug-ins to Geist. A factory is registered with Geist API RegisterLoaderType() for a source type to be available for stream specs.
type ExtractorQuery ¶
type ExtractorQuery struct { Type QueryType Key string CompositeKey []KeyValueFilter }
type Field ¶
type Field struct { Id string `json:"id"` // JsonPath defines which field in the JSON that should be extracted. It uses github.com/tidwall/gjson // syntax, such as "myCoolField" if we want to extract that field from json { "myCoolField": "isHere" } // // The full raw JSON event is also regarded as a 'field' and to extract that the JsonPath string should // be empty or omitted in the spec. JsonPath string `json:"jsonPath"` // - For normal fields, Type can be "string", "integer", "number", "boolean" or "float". // If omitted in the spec, string will be used. // // - For raw event fields the default type is []byte, unless Type is explicitly set to "string". // For performance critical streams, type should be omitted (avoiding conversions), especially when // having a stream with BigTable sink, which stores the data as byte anyway. // // - If a field is an iso timestamp string (e.g. "2019-11-30T14:57:23.389Z") the type // "isoTimestamp" can be used, to have a Go time.Time object created as the value for this field key. // // - If a field is a unix timestamp (number or str) (e.g. 1571831226950 or "1571831226950") the type // "unixTimestamp" can be used to have Go time.Time object created as the value for this field. // // - If a field is a User Agent string (e.g. "Mozilla%2F5.0%20(Macintosh%3B%20Intel%2...") the type // "userAgent" can be used to have parsed JSON output as string, with separate fields for each part of UA. Type string `json:"type,omitempty"` }
type ForEventsWith ¶
The Key string must be on a JSON path syntax according to github.com/tidwall/gjson (see below). Note that while the 'Value' field in the ForEventsWith spec is of string type, the actual field in the incoming event can be of for example int type in addition to string, where a match will be made of its string representation. For example, if 'Value' is set to "3" and the field in the incoming event is of JSON number type (int) with a value of 3, a match will be made correctly.
type HookAction ¶
type HookAction int
const ( HookActionInvalid HookAction = iota // default, not to be used HookActionProceed // continue processing of this event HookActionSkip // skip processing of this event and take next HookActionRetryableError // let Geist handle this event as a retryable error HookActionUnretryableError // let Geist handle this event as an unretryable error (e.g. corrupt event to be sent to DLQ) HookActionShutdown // shut down this stream instance )
type IdFromItemFields ¶
type KeyValueFilter ¶
type Loader ¶
type Loader interface { // If successful the event/resource ID of the loaded event is returned. // If input 'data' is nil or empty, an error is to be returned. StreamLoad(ctx context.Context, data []*Transformed) (string, error, bool) // Called by Executor during shutdown of the stream Shutdown(ctx context.Context) }
Loader interface required for stream sink Loader implementations. Only certain types of Loader implementations might support multiple input Transformed object. For example, while a BigTable implementation might only support a single (first) Transformed object, as input to which fields from a single event to insert to the table, a Kafka implementation might receive multiple Transformed object (as the result from a EventSplit transformation type), all of which should be sent as separate events to the specified Kafka topic.
type LoaderFactories ¶
type LoaderFactories map[string]LoaderFactory
type LoaderFactory ¶
type LoaderFactory interface { // Sink returns the sink ID for which the loader is implemented SinkId() string // NewLoader creates a new loader entity NewLoader(ctx context.Context, c Config) (Loader, error) // NewSinkExtractor creates an extractor to enable retrieving data from the sink // as written by the loader. // This functionality is optional and if not implemented the function should return nil, nil. NewSinkExtractor(ctx context.Context, c Config) (Extractor, error) // Close is called by Geist after using Geist API geist.Shutdown() Close(ctx context.Context) error }
LoaderFactory enables loaders/sinks to be handled as plug-ins to Geist. A factory is registered with Geist API RegisterLoaderType() for a sink type to be available for stream specs.
type Metrics ¶ added in v0.4.0
type Metrics struct { // Total number of events sent to Executor's ProcessEvent() by the Extractor, // regardless of the outcome of downstream processing. EventsProcessed int64 // Total time spent by Executor processing all extracted events EventProcessingTimeMicros int64 // Total number of event batches sent from Extractor to Sink loader via Executor Microbatches int64 // Total amount of event data processed (as sent from Extractor) BytesProcessed int64 // Total number of events successfully processed by the sink. EventsStoredInSink int64 // Total time spent ingesting transformed events in the sink successfully SinkProcessingTimeMicros int64 // Total number of successfull calls to the Sink's StreamLoad method SinkOperations int64 // Total amount of data successfully ingested BytesIngested int64 }
Metrics provided by the engine of its operations. Accessible from Geist API with geist.Metrics()
type NotificationEvent ¶ added in v0.4.0
type NotificationEvent struct { // The nofication level Level string // Timestamp of the event on the format "2006-01-02T15:04:05.000000Z" Timestamp string // The entity type of the sender, e.g. "executor", "supervisor", etc Sender string // The unique instance ID of the sender Instance string // The stream ID, if applicable Stream string Message string // Location and stack info, from where notification was sent. // Func is always provided. // File and Line are added when notification level is WARN or above. // StackTrace is added when notification level is ERROR. Func string File string Line int StackTrace string }
NoficationEvent is the type of the events sent by Geist to the notification channel, which is accessible externally with geist.NotificationChannel().
type NotifyChan ¶ added in v0.4.0
type NotifyChan chan NotificationEvent
type Ops ¶
type Ops struct { // StreamsPerPod specifies how many Executors that should execute the stream concurrently // in its own Goroutine. // This is especially important when using Kafka extractors. For max concurrency and highest throughput // it should be set equal to the number of topic partitions divided by expected number of pods. // There is negligible overhead in having more goroutines than partitions. // If omitted it is set to DefaultStreamsPerPod (1). StreamsPerPod int `json:"streamsPerPod"` // MicroBatch specifies if events should be processed in batches, which improves throughput. // If set to 'true' the involved stream entities try their best to process events in batches according // to each ETL entity's capability for micro-batch processing. // If omitted or set to false, the stream will process a single event at a time. // Note: This is an optional feature for each Source/Extractor plugin. See doc for each plugin entity for availability. // Example of a plugin supporting this is 'kafka'. // Verified beneficial effect is when having sink set to 'bigquery', due to BQ API capabilities for this. MicroBatch bool `json:"microBatch"` // MicroBatchSize is the maximum number of events that should be included in the batch. // If omitted it is set to DefaultMicroBatchSize MicroBatchSize int `json:"microBatchSize,omitempty"` // MicroBatchSize specifies the threshold that when reached closes the batch regardless of number of events in // the batch, and forwards it downstream. The final size of the batch will be this threshold + size of next event. // If omitted it is set to DefaultMicroBatchSizeBytes MicroBatchBytes int `json:"microBatchBytes,omitempty"` // MicroBatchTimeout is the maximum time to wait for the batch to fill up if the max size has not been reached. // If the sink is set to Kafka, this value will override the pollTimeout value. // If omitted it is set to DefaultMicroBatchTimeoutMs MicroBatchTimeoutMs int `json:"microBatchTimeoutMs,omitempty"` // MaxEventProcessingRetries specifies how many times an extracted event from the source should be processed // again (transform/load), if deemed retryable, before the Executor restarts the stream on a longer back-off // interval (MaxStreamRetryBackoffInterval). Retryable errors will be retried indefinitely for max self-healing. // If omitted it is set to DefaultMaxEventProcessingRetries. MaxEventProcessingRetries int `json:"maxEventProcessingRetries"` // MaxStreamRetryBackoffInterval specifies the max time between stream restarts after exponential backoff // retries of retryable event processing failures. // If omitted or zero it is set to DefaultMaxStreamRetryBackoffInterval MaxStreamRetryBackoffIntervalSec int `json:"maxStreamRetryBackoffIntervalSec"` // HandlingOfUnretryableEvents specifies what to do with events that can't be properly transformed or loaded // to the sink, e.g. corrupt or otherwise non-compliant events vs the stream spec. // Available options are: // // "default" - Default behaviour depending on Extractor type. For Kafka this means "discard" and for // Pubsub it means Nack (continue retrying later but process other events as well). // If this field is omitted it will take this value. // // "discard" - Discard the event, log it with Warn, and continue processing other events. // // "dlq" - Move the event from the source topic to a DLQ topic specified in DLQ Config. // // "fail" - The stream will be terminated with an error message. // // Note that all source types might not support all available options. See documentation for each source type for details. // HandlingOfUnretryableEvents string `json:"handlingOfUnretryableEvents,omitempty"` // LogEventData is useful for enabling granular event level debugging dynamically for specific streams // without having to redeploy GEIST. To troubleshoot a specific stream a new version of the stream spec // can be uploaded at run-time with this field set to true. LogEventData bool `json:"logEventData"` // CustomProperties can be used to configure stream processing in any type of custom // connector or injected enrichment logic. CustomProperties map[string]string `json:"customProperties"` }
func (*Ops) EnsureValidDefaults ¶ added in v0.5.2
func (o *Ops) EnsureValidDefaults()
type PostTransformHookFunc ¶ added in v0.8.0
type PostTransformHookFunc func(ctx context.Context, spec *Spec, event *[]*Transformed) HookAction
PostTransformHookFunc serves the same purpose and functionality as the PreTransformHookFunc but is called after the event transformations.
type PreTransformHookFunc ¶
type PreTransformHookFunc func(ctx context.Context, spec *Spec, event *[]byte) HookAction
PreTransformHookFunc is a client-provided function which the stream's Executor use prior to sending the event to the Transfomer. This way the client could modifiy/enrich each event before being processed according to the transform part of the spec. Since errors in this func is solely part of the client domain there is no point in returning them to the Geist executor. It is up the the client to decide appropriate actions to take, including optionally returning one of the HookAction error values. The event is provided as a mutable argument to avoid requiring the client to always return data even if not used. The stream spec governing the provided event is provided for context and filtering logic capabilities, since the function is called for all concurrently running streams.
type ProcessEventFunc ¶
type ProcessEventFunc func(context.Context, []Event) EventProcessingResult
ProcessEventFunc is the type of func that an Extractor calls for each extracted event to be processed downstream.
It is important for the Extractor to properly handle the returned EventProcessingResult.
EventProcessingResult.ExecutorStatus values: ExecutorStatusSuccessful --> continue as normal ExecutorStatusError --> handle error depending on Houe mode in stream spec ExecutorStatusRetriesExhausted --> normally a shutdown of extractor is an ok action (will be restarted) ExecutorStatusShutdown --> shut down extractor
type Regexp ¶
type Regexp struct { // The regular expression, in RE2 syntax. Expression string `json:"expression,omitempty"` // If used in conjunction with fieldExtraction, this will be the field to apply regexp on. Field string `json:"field,omitempty"` // If extracted field should be kept in result or omitted. Default is false. KeepField bool `json:"keepField,omitempty"` // Time conversion of date field. Field specified must be extracted before. TimeConversion *TimeConv `json:"timeConversion,omitempty"` }
Regexp specifies an optional transformation type for use in the stream spec. It transforms a string into a JSON based on the groupings in the regular expression. Minimum one grouping needs to be made. The resulting output from the transformation is found with the key "regexppayload". An example use case is when having incoming events on a Kafka topic with certain fields containing plain log text strings, from which certain parts should be extracted into fields for downstream ingestion into the sink on a structured format.
func (*Regexp) CollectGroups ¶
TODO: Shorten, but not important now since there is no performance impact.
type RowItem ¶
type RowItem struct { Column string `json:"column"` Timestamp time.Time `json:"timestamp"` Value any `json:"value"` }
RowItem or an array of row items can be used as map value in a Transformed output from for example the Extractor.ExtractFromSink() function, e.g. providing data from a BigTable row
type Sink ¶
type Sink struct { Type EntityType `json:"type"` Config *SinkConfig `json:"config,omitempty"` }
Sink spec
type SinkConfig ¶
type Source ¶
type Source struct { Type EntityType `json:"type"` Config SourceConfig `json:"config"` }
Source spec
type SourceConfig ¶
type Spec ¶
type Spec struct { // Main metadata (required) Namespace string `json:"namespace"` StreamIdSuffix string `json:"streamIdSuffix"` Description string `json:"description"` Version int `json:"version"` // Operational config (optional) Disabled bool `json:"disabled"` Ops Ops `json:"ops"` OpsPerEnv map[string]Ops `json:"opsPerEnv,omitempty"` // Stream entity config (required) Source Source `json:"source"` Transform Transform `json:"transform"` Sink Sink `json:"sink"` }
Spec implements the GEIST Stream Spec interface and specifies how each ETL stream should be executed from Source to Transform to Sink. Specs are registered and updated through a stream of its own, as specified by the configurable SpecRegistrationSpec. The Namespace + StreamIdSuffix combination must be unique (forming a GEIST Stream ID). To succeed with an upgrade of an existing spec the version number needs to be incremented.
func NewEmptySpec ¶
func NewEmptySpec() *Spec
func NewSpec ¶
NewSpec creates a new Spec from JSON and validates both against JSON schema and the transformation logic on the created spec.
func (*Spec) EnsureValidDefaults ¶ added in v0.5.2
func (s *Spec) EnsureValidDefaults()
func (*Spec) IsDisabled ¶
type TimeConv ¶
type TimeConv struct { // Field where the data is located and should be converted. Field string `json:"field,omitempty"` // Input format of date to be converted. Mandatory. InputFormat string `json:"inputFormat,omitempty"` // Output format of date, if omitted, ISO-8601 is used. OutputFormat string `json:"outputFormat,omitempty"` }
type Transform ¶
type Transform struct { // ImplId denotes the Implementation ID (type of Transform implementation). // The GEIST built-in type is named 'native' and is currently the only one supported // TODO: Change this name to type? or just id? ImplId EntityType `json:"implId,omitempty"` // ExcludeEventsWith will be checked first to exclude events, matching conditions, // from all other transformations. If multiple filter objects are provided they are // handled as OR type of filters. ExcludeEventsWith []ExcludeEventsWith `json:"excludeEventsWith,omitempty"` // The ExtractFields transformation type picks out fields from the input event JSON. // The first ExtractFields object that matches the ForEventsWith filter will be used // to create the resulting Transformed object. ExtractFields []ExtractFields `json:"extractFields,omitempty"` ExtractItemsFromArray []ExtractItemsFromArray `json:"extractItemsFromArray,omitempty"` // The Regexp transformation transforms a string into a JSON based on the groupings in // the regular expression. Minimum one groupings needs to be made. Regexp *Regexp `json:"regexp,omitempty"` }
Transform spec
type Transformed ¶
func NewTransformed ¶
func NewTransformed() *Transformed
func (*Transformed) String ¶
func (t *Transformed) String() string
type TransformedItemMap ¶
TransformedItemMap is the type used for transforms creating a map of items to be stored in the output map. One example is the ExtractItemsFromArray transform, which extracts JSON array items into such a map and stores that map inside the output Transformed map array.