2020-01-08 09:13:12 +00:00
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#pragma once
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#include "config_formats.h"
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2020-01-18 19:29:53 +00:00
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#include "config_core.h"
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2020-01-08 09:13:12 +00:00
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#if USE_AVRO
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#include <unordered_map>
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2020-02-01 04:13:12 +00:00
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#include <map>
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2020-01-08 09:13:12 +00:00
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#include <vector>
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#include <Core/Block.h>
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#include <Formats/FormatSchemaInfo.h>
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#include <Processors/Formats/IRowInputFormat.h>
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#include <avro/DataFile.hh>
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#include <avro/Decoder.hh>
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#include <avro/Schema.hh>
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#include <avro/ValidSchema.hh>
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namespace DB
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{
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class AvroDeserializer
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{
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public:
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AvroDeserializer(const Block & header, avro::ValidSchema schema);
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void deserializeRow(MutableColumns & columns, avro::Decoder & decoder) const;
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private:
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using DeserializeFn = std::function<void(IColumn & column, avro::Decoder & decoder)>;
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using SkipFn = std::function<void(avro::Decoder & decoder)>;
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static DeserializeFn createDeserializeFn(avro::NodePtr root_node, DataTypePtr target_type);
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SkipFn createSkipFn(avro::NodePtr root_node);
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2020-01-08 09:13:12 +00:00
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2020-04-18 20:15:39 +00:00
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struct Action
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{
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enum Type { Deserialize, Skip };
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Type type;
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/// Deserialize
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int target_column_idx;
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DeserializeFn deserialize_fn;
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/// Skip
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SkipFn skip_fn;
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Action(int target_column_idx_, DeserializeFn deserialize_fn_)
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: type(Deserialize)
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, target_column_idx(target_column_idx_)
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, deserialize_fn(deserialize_fn_) {}
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Action(SkipFn skip_fn_)
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: type(Skip)
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, skip_fn(skip_fn_) {}
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void execute(MutableColumns & columns, avro::Decoder & decoder) const
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{
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2020-04-19 03:54:22 +00:00
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switch (type)
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{
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case Deserialize:
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deserialize_fn(*columns[target_column_idx], decoder);
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break;
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case Skip:
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skip_fn(decoder);
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break;
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}
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}
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};
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/// Populate actions by recursively traversing root schema
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void createActions(const Block & header, const avro::NodePtr& node, std::string current_path = "");
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/// Bitmap of columns found in Avro schema
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std::vector<bool> column_found;
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/// Deserialize/Skip actions for a row
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std::vector<Action> actions;
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/// Map from name of named Avro type (record, enum, fixed) to SkipFn.
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/// This is to avoid infinite recursion when Avro schema contains self-references. e.g. LinkedList
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std::map<avro::Name, SkipFn> symbolic_skip_fn_map;
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};
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class AvroRowInputFormat : public IRowInputFormat
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{
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public:
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AvroRowInputFormat(const Block & header_, ReadBuffer & in_, Params params_);
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virtual bool readRow(MutableColumns & columns, RowReadExtension & ext) override;
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String getName() const override { return "AvroRowInputFormat"; }
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private:
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avro::DataFileReaderBase file_reader;
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AvroDeserializer deserializer;
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};
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2020-01-18 19:29:53 +00:00
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#if USE_POCO_JSON
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/// Confluent framing + Avro binary datum encoding. Mainly used for Kafka.
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/// Uses 3 caches:
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/// 1. global: schema registry cache (base_url -> SchemaRegistry)
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/// 2. SchemaRegistry: schema cache (schema_id -> schema)
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/// 3. AvroConfluentRowInputFormat: deserializer cache (schema_id -> AvroDeserializer)
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/// This is needed because KafkaStorage creates a new instance of InputFormat per a batch of messages
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class AvroConfluentRowInputFormat : public IRowInputFormat
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{
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public:
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AvroConfluentRowInputFormat(const Block & header_, ReadBuffer & in_, Params params_, const FormatSettings & format_settings_);
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virtual bool readRow(MutableColumns & columns, RowReadExtension & ext) override;
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String getName() const override { return "AvroConfluentRowInputFormat"; }
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class SchemaRegistry;
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private:
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std::shared_ptr<SchemaRegistry> schema_registry;
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using SchemaId = uint32_t;
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std::unordered_map<SchemaId, AvroDeserializer> deserializer_cache;
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const AvroDeserializer & getOrCreateDeserializer(SchemaId schema_id);
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avro::InputStreamPtr input_stream;
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avro::DecoderPtr decoder;
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};
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2020-01-10 22:46:48 +00:00
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#endif
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2020-01-08 09:13:12 +00:00
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}
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#endif
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