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comet.json | PrimitiveType | Timestamp based on `RFC 1123 / RFC 822` patterns (Tue, 3 Jun 2008 11:05:30 GMT) | {"const": "rfc_1123_date_time"} |
comet.json | PrimitiveType | date/time that match the 'yyyy-MM-dd HH:mm:ss' regex s (2019-12-31 23:59:02).
For epoch timestamp, set pattern attribute to 'epoch_second' or 'epoch_milli' | {"const": "timestamp"} |
comet.json | PrimitiveType | Any floating value that match the '-?\d*\.{0,1}\d+' regex | {"const": "decimal"} |
comet.json | PrimitiveType | Any attribute that has children. Set the array to true if this attribute is made of a list of attributes | {"const": "struct"} |
comet.json | IndexMapping | {"const": "text"} |
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comet.json | IndexMapping | {"const": "keyword"} |
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comet.json | IndexMapping | {"const": "long"} |
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comet.json | IndexMapping | {"const": "integer"} |
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comet.json | IndexMapping | {"const": "short"} |
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comet.json | IndexMapping | {"const": "byte"} |
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comet.json | IndexMapping | {"const": "double"} |
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comet.json | IndexMapping | {"const": "float"} |
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comet.json | IndexMapping | {"const": "half_float"} |
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comet.json | IndexMapping | {"const": "scaled_float"} |
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comet.json | IndexMapping | {"const": "date"} |
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comet.json | IndexMapping | {"const": "boolean"} |
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comet.json | IndexMapping | {"const": "binary"} |
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comet.json | IndexMapping | {"const": "integer_rang"} |
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comet.json | IndexMapping | {"const": "float_range"} |
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comet.json | IndexMapping | {"const": "long_range"} |
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comet.json | IndexMapping | {"const": "double_range"} |
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comet.json | IndexMapping | {"const": "date_range"} |
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comet.json | IndexMapping | {"const": "geo_point"} |
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comet.json | IndexMapping | {"const": "geo_shape"} |
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comet.json | IndexMapping | {"const": "ip"} |
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comet.json | IndexMapping | {"const": "completion"} |
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comet.json | IndexMapping | {"const": "token_count"} |
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comet.json | IndexMapping | {"const": "object"} |
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comet.json | IndexMapping | {"const": "array"} |
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comet.json | WriteMode | Append to or overwrite existing data | {"type": "string", "oneOf": [{"const": "OVERWRITE"}, {"const": "APPEND"}, {"const": "ERROR_IF_EXISTS"}, {"const": "IGNORE"}]} |
comet.json | WriteMode | That data will overwrite the existing data or create it if it does not exist | {"const": "OVERWRITE"} |
comet.json | WriteMode | Append the data to an existing table or create it if it does not exist | {"const": "APPEND"} |
comet.json | WriteMode | Fail if teh table already exist | {"const": "ERROR_IF_EXISTS"} |
comet.json | WriteMode | Do not save at all. Useful in interactive / test mode. | {"const": "IGNORE"} |
comet.json | UserType | Service account | {"const": "SA"} |
comet.json | UserType | End user | {"const": "USER"} |
comet.json | UserType | Group of users / service accounts | {"const": "GROUP"} |
comet.json | Trim | Remove all leading space chars from the input | {"const": "LEFT"} |
comet.json | Trim | Remove all trailing spaces from the input | {"const": "RIGHT"} |
comet.json | Trim | Remove all leading and trailing spaces from the input | {"const": "BOTH"} |
comet.json | Trim | Do not remove leading or trailing spaces from the input | {"const": "NONE"} |
comet.json | TableDdl | DDL used to create a table | {"type": "object", "properties": {"createSql": {"type": "string"}, "pingSql": {"type": "string"}}, "required": ["createSql"]} |
comet.json | createSql | SQL CREATE DDL statement | {"type": "string"} |
comet.json | pingSql | How to test if the table exist.
Use the following statement by default: 'select count(*) from tableName where 1=0' | {"type": "string"} |
comet.json | TableType | Table types supported by the Extract module | {"type": "string", "oneOf": [{"const": "TABLE"}, {"const": "VIEW"}, {"const": "SYSTEM TABLE"}, {"const": "GLOBAL TEMPORARY"}, {"const": "LOCAL TEMPORARY"}, {"const": "ALIAS"}, {"const": "SYNONYM"}]} |
comet.json | TableType | SQl Table | {"const": "TABLE"} |
comet.json | TableType | SQl View | {"const": "VIEW"} |
comet.json | TableType | Database specific system table | {"const": "SYSTEM TABLE"} |
comet.json | TableType | {"const": "GLOBAL TEMPORARY"} |
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comet.json | TableType | {"const": "LOCAL TEMPORARY"} |
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comet.json | TableType | Table alias | {"const": "ALIAS"} |
comet.json | TableType | Table synonym | {"const": "SYNONYM"} |
comet.json | Type | Custom type definition. Custom types are defined in the types/types.comet.yml file | {"type": "object", "properties": {"name": {"type": "string"}, "primitiveType": {}, "pattern": {"type": "string"}, "zone": {"type": "string"}, "sample": {"type": "string"}, "comment": {"type": "string"}, "indexMapping": {"type": "string"}, "ddlMapping": {}}, "required": ["name", "pattern", "primitiveType"]} |
comet.json | name | unique id for this type | {"type": "string"} |
comet.json | primitiveType | To what primitive type should this type be mapped.
This is the memory representation of the type, When saving, this primitive type is mapped to the database specific type | {} |
comet.json | pattern | Regex used to validate the input field | {"type": "string"} |
comet.json | zone | useful when parsing specific string:
- double: To parse a french decimal (comma as decimal separator) set it to fr_FR locale.
- decimal: to set the precision and scale of this number, '38,9' by default.
- | {"type": "string"} |
comet.json | sample | This field makes sure that the pattern matches the value you want to match. This will be checked on startup | {"type": "string"} |
comet.json | comment | Describes this type | {"type": "string"} |
comet.json | indexMapping | How this type is indexed in your datawarehouse | {"type": "string"} |
comet.json | ddlMapping | Configure here the type mapping for each datawarehouse.\nWill be used when inferring DDL from schema. | {} |
comet.json | Partition | Partition columns, no partitioning by default | {"type": "object", "properties": {"sampling": {"type": "number"}, "attributes": {"type": "array", "items": {"type": "string"}}}, "required": []} |
comet.json | sampling | 0.0 means no sampling, > 0 && < 1 means sample dataset, >=1 absolute number of partitions. Used exclusively on Hadoop & databricks warehouses | {"type": "number"} |
comet.json | items | Attributes used to partition de dataset. | {"type": "string"} |
comet.json | first | Zero based position of the first character for this attribute | {"type": "number"} |
comet.json | last | Zero based position of the last character to include in this attribute | {"type": "number"} |
comet.json | Connection | Connection | {"type": "object", "properties": {"type": {"type": "string"}, "sparkFormat": {"type": "string"}, "mode": {}, "options": {}}, "required": ["type"]} |
comet.json | type | aka jdbc, bigquery, snowflake, redshift ... | {"type": "string"} |
comet.json | sparkFormat | Set only if you want to use the Spark engine | {"type": "string"} |
comet.json | mode | Used for JDBC connections only. Write mode, APPEND by default | {} |
comet.json | options | Connection options | {} |
comet.json | RowLevelSecurity | Row level security policy to apply to the output data. | {"type": "object", "properties": {"name": {"type": "string"}, "predicate": {"type": "string"}, "grants": {"type": "array", "items": {"type": "string"}}}, "required": ["name", "grants"]} |
comet.json | name | This Row Level Security unique name | {"type": "string"} |
comet.json | description | Description for this access policy | {"type": "string"} |
comet.json | predicate | The condition that goes to the WHERE clause and limit the visible rows. | {"type": "string"} |
comet.json | grants | user / groups / service accounts to which this security level is applied.
ex : user:[email protected],group:[email protected],serviceAccount:[email protected] | {"type": "array", "items": {"type": "string"}} |
comet.json | AccessControlEntry | Column level security policy to apply to the attribute. | {"type": "object", "properties": {"role": {"type": "string"}, "grants": {"type": "array", "items": {"type": "string"}}}, "required": ["role", "grants"]} |
comet.json | role | This role to give to the granted users | {"type": "string"} |
comet.json | grants | user / groups / service accounts to which this security level is applied.
ex : user:[email protected],group:[email protected],serviceAccount:[email protected] | {"type": "array", "items": {"type": "string"}} |
comet.json | key | list of attributes to join an existing and incoming dataset. Use renamed columns if any here. | {"type": "array", "items": {"type": "string"}} |
comet.json | delete | Optional valid delete condition on the incoming dataset. Use renamed column here. | {"type": "string"} |
comet.json | timestamp | Timestamp column used to identify last version, if not specified currently ingested row is considered the last | {"type": "string"} |
comet.json | queryFilter | Useful when you want to merge only on a subset of the existing partitions, thus improving performance and reducing costs.
You may use here:
- Any SQL condition
- latest which will be translated to the last existing partition
- column in last(10) which will apply the merge on the last 10 partitions of your dataset.
last and latest assume that your table is partitioned by day. | {"type": "string"} |
comet.json | Format | DSV by default. Supported file formats are :\n- DSV : Delimiter-separated values file. Delimiter value is specified in the "separator" field.\n- POSITION : FIXED format file where values are located at an exact position in each line.\n- SIMPLE_JSON : For optimisation purpose, we differentiate JSON with top level values from JSON\n with deep level fields. SIMPLE_JSON are JSON files with top level fields only.\n- JSON : Deep JSON file. Use only when your json documents contain sub-documents, otherwise prefer to\n use SIMPLE_JSON since it is much faster.\n- XML : XML files | {"type": "string", "oneOf": [{"const": "DSV"}, {"const": "POSITION"}, {"const": "JSON"}, {"const": "ARRAY_JSON"}, {"const": "SIMPLE_JSON"}, {"const": "XML"}]} |
comet.json | Format | any single or multiple character delimited file. Separator is specified in the separator field | {"const": "DSV"} |
comet.json | Format | any fixed position file. Positions are specified in the position field | {"const": "POSITION"} |
comet.json | Format | any deep json file.
To improve performance, prefer the SIMPLE_JSON format if your json documents are flat | {"const": "JSON"} |
comet.json | Format | any json file containing an array of json objects. | {"const": "ARRAY_JSON"} |
comet.json | Format | any flat json file.
To improve performance, prefer this format if your json documents are flat | {"const": "SIMPLE_JSON"} |
comet.json | Format | any xml file. Use the metadata.xml.rowTag field to specify the root tag of your xml file | {"const": "XML"} |
comet.json | MapString | Map of string | {"type": "object", "additionalProperties": {"type": "string"}} |
comet.json | MapConnection | Map of connections | {"type": "object", "additionalProperties": {}} |
comet.json | MapJdbcEngine | Map of jdbc engines | {"type": "object", "additionalProperties": {}} |
comet.json | MapTableDdl | Map of table ddl | {"type": "object", "additionalProperties": {}} |
comet.json | JdbcEngine | Jdbc engine | {"type": "object", "properties": {"tables": {"type": "array", "items": {}}}} |
comet.json | tables | List of all SQL create statements used to create audit tables for this JDBC engine.
Tables are created only if the execution of the pingSQL statement fails | {"type": "array", "items": {}} |
comet.json | options | Privacy strategies. The following default strategies are defined by default:
- none: Leave the data as is
- hide: replace the data with an empty string
- hideX("s", n): replace the string with n occurrences of the string 's'
- md5: Redact the data using the MD5 algorithm
- sha1: Redact the data using the SHA1 algorithm
- sha256: Redact the data using the SHA256 algorithm
- sha512: Redact the data using the SHA512 algorithm
- initials: keep only the first char of each word in the data | {} |
comet.json | Internal | configure Spark internal options | {"type": "object", "properties": {"cacheStorageLevel": {"type": "string"}, "intermediateBigqueryFormat": {"type": "string"}, "temporaryGcsBucket": {"type": "string"}, "substituteVars": {"type": "boolean"}}} |
comet.json | cacheStorageLevel | How the RDD are cached. Default is MEMORY_AND_DISK_SER.
Available options are (https://spark.apache.org/docs/latest/api/java/index.html?org/apache/spark/storage/StorageLevel.html):
- MEMORY_ONLY
- MEMORY_AND_DISK
- MEMORY_ONLY_SER
- MEMORY_AND_DISK_SER
- DISK_ONLY
- OFF_HEAP | {"type": "string"} |
comet.json | intermediateBigqueryFormat | May be parquet or ORC. Default is parquet. Used for BigQuery intermediate storage. Use ORC for for JSON files to keep the original data structure.
https://stackoverflow.com/questions/53674838/spark-writing-parquet-arraystring-converts-to-a-different-datatype-when-loadin | {"type": "string"} |
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