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roboquant-avro/src/main/kotlin/org/roboquant/avro/AvroFeed2.kt
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/* | ||
* Copyright 2020-2024 Neural Layer | ||
* | ||
* Licensed under the Apache License, Version 2.0 (the "License"); | ||
* you may not use this file except in compliance with the License. | ||
* You may obtain a copy of the License at | ||
* | ||
* https://www.apache.org/licenses/LICENSE-2.0 | ||
* | ||
* Unless required by applicable law or agreed to in writing, software | ||
* distributed under the License is distributed on an "AS IS" BASIS, | ||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
* See the License for the specific language governing permissions and | ||
* limitations under the License. | ||
*/ | ||
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package org.roboquant.avro | ||
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import kotlinx.coroutines.channels.ClosedReceiveChannelException | ||
import kotlinx.coroutines.launch | ||
import kotlinx.coroutines.runBlocking | ||
import org.apache.avro.Schema | ||
import org.apache.avro.file.CodecFactory | ||
import org.apache.avro.file.DataFileConstants | ||
import org.apache.avro.file.DataFileReader | ||
import org.apache.avro.file.DataFileWriter | ||
import org.apache.avro.generic.GenericData | ||
import org.apache.avro.generic.GenericDatumReader | ||
import org.apache.avro.generic.GenericDatumWriter | ||
import org.apache.avro.generic.GenericRecord | ||
import org.apache.avro.io.DatumWriter | ||
import org.apache.avro.util.Utf8 | ||
import org.roboquant.common.Asset | ||
import org.roboquant.common.Logging | ||
import org.roboquant.common.Timeframe | ||
import org.roboquant.common.compareTo | ||
import org.roboquant.feeds.* | ||
import java.nio.file.Files | ||
import java.nio.file.Path | ||
import java.time.Instant | ||
import java.util.* | ||
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/** | ||
* Read price data from a single file in Avro format. This feed loads data lazy and disposes of it afterwards, so | ||
* memory footprint is low. Compared to CSV files, Avro files are parsed more efficient, making it a good fit for large | ||
* back tests. Additionally, an Avro file can be compressed, reducing the overall disk space required. | ||
* | ||
* The internal resolution is nanoseconds and stored as a single Long value | ||
* | ||
* @property path the path where the Avro file can be found | ||
* @property template template to use to convert the stored symbols into assets | ||
* | ||
* @constructor Create new Avro Feed | ||
*/ | ||
class AvroFeed2(private val path: Path, private val template: Asset = Asset("TEMPLATE")) : Feed { | ||
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/** | ||
* Instantiate an Avro Feed based on the Avro file at [path] | ||
*/ | ||
constructor(path: String) : this(Path.of(path)) | ||
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private val logger = Logging.getLogger(AvroFeed2::class) | ||
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private val index by lazy { createIndex() } | ||
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override val timeframe: Timeframe by lazy { calcTimeframe() } | ||
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init { | ||
logger.info { "New AvroFeed path=$path exist=${exists()}" } | ||
} | ||
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fun exists(): Boolean = Files.exists(path) | ||
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private fun getReader(): DataFileReader<GenericRecord> { | ||
return DataFileReader(path.toFile(), GenericDatumReader()) | ||
} | ||
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private fun ofEpochNano(value: Long): Instant { | ||
return if (value >= 0L) | ||
Instant.ofEpochSecond(value / 1_000_000_000L, value % 1_000_000_000L) | ||
else | ||
Instant.ofEpochSecond(value / 1_000_000_000L, -value % 1_000_000_000L) | ||
} | ||
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/** | ||
* (Re)play the events of the feed using the provided [EventChannel] | ||
* | ||
* @param channel | ||
* @return | ||
*/ | ||
override suspend fun play(channel: EventChannel) { | ||
val timeframe = channel.timeframe | ||
var last = Instant.MIN | ||
var items = ArrayList<PriceItem>() | ||
val serializer = PriceActionSerializer() | ||
val cache = mutableMapOf<String, Asset>() | ||
getReader().use { | ||
if (timeframe.isFinite()) position(it, timeframe.start) | ||
while (it.hasNext()) { | ||
val rec = it.next() | ||
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// Optimize unnecessary parsing of the whole record | ||
val now = ofEpochNano(rec[0] as Long) | ||
if (now < timeframe) continue | ||
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if (now != last) { | ||
channel.sendNotEmpty(Event(last, items)) | ||
last = now | ||
items = ArrayList<PriceItem>(items.size) | ||
} | ||
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if (now > timeframe) break | ||
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// Parse the remaining attributes | ||
val symbol = rec.get(1).toString() | ||
val asset = cache.getOrPut(symbol) { template.copy(symbol = symbol) } | ||
val actionType = rec.get(2) as Int | ||
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@Suppress("UNCHECKED_CAST") | ||
val values = rec.get(3) as List<Double> | ||
val other = rec.get("other") as Utf8? | ||
val item = serializer.deserialize(asset, actionType, values, other?.toString()) | ||
items.add(item) | ||
} | ||
channel.sendNotEmpty(Event(last, items)) | ||
} | ||
} | ||
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private fun position(r: DataFileReader<GenericRecord>, time: Instant) { | ||
val key = index.floorKey(time) | ||
if (key != null) r.seek(index.getValue(key)) | ||
} | ||
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private fun createIndex() : TreeMap<Instant, Long> { | ||
val index = TreeMap<Instant, Long>() | ||
getReader().use { | ||
while (it.hasNext()) { | ||
val position = it.tell() | ||
val t = ofEpochNano(it.next().get(0) as Long) | ||
it.seek(position) | ||
if (it.hasNext()) { | ||
index.putIfAbsent(t,position) | ||
it.nextBlock() | ||
} | ||
} | ||
} | ||
return index | ||
} | ||
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private fun calcTimeframe() : Timeframe { | ||
if (index.isEmpty()) return Timeframe.EMPTY | ||
val start = index.firstKey() | ||
getReader().use { | ||
position(it, index.lastKey()) | ||
var timestamp = index.lastKey().toEpochNano() | ||
while (it.hasNext()) { | ||
timestamp = it.next().get(0) as Long | ||
} | ||
return Timeframe(start, ofEpochNano(timestamp), true) | ||
} | ||
} | ||
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private fun Instant.toEpochNano(): Long { | ||
var currentTimeNano = epochSecond * 1_000_000_000L | ||
currentTimeNano += if (currentTimeNano > 0) nano else -nano | ||
return currentTimeNano | ||
} | ||
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/** | ||
* Record the price-actions in a [feed] and store them in an Avro file that can be later used as input for | ||
* an AvroFeed. The provided [feed] needs to implement the [AssetFeed] interface. | ||
* | ||
* [compression] can be enabled, which results in a smaller file. The `snappy` compression codec is used, that | ||
* achieves decent compression ratio while using limited CPU usage. | ||
* | ||
* Additionally, you can filter on a [timeframe]. Default is to apply no filtering. | ||
*/ | ||
@Suppress("LongParameterList") | ||
fun record( | ||
feed: Feed, | ||
compression: Boolean = true, | ||
timeframe: Timeframe = Timeframe.INFINITE, | ||
append: Boolean = false, | ||
syncInterval: Int = DataFileConstants.DEFAULT_SYNC_INTERVAL | ||
) = runBlocking { | ||
val schemaDef = """{ | ||
"namespace": "org.roboquant.avro.schema", | ||
"type": "record", | ||
"name": "PriceItemV2", | ||
"fields": [ | ||
{"name": "timestamp_ns", "type": "long"}, | ||
{"name": "symbol", "type": "string"}, | ||
{"name": "type", "type": "int"}, | ||
{"name": "values", "type": {"type": "array", "items" : "double"}}, | ||
{"name": "other", "type": ["null", "string"], "default": null} | ||
] | ||
}""" | ||
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val channel = EventChannel(timeframe = timeframe) | ||
val schema = Schema.Parser().parse(schemaDef) | ||
val datumWriter: DatumWriter<GenericRecord> = GenericDatumWriter(schema) | ||
val dataFileWriter = DataFileWriter(datumWriter) | ||
val file = path.toFile() | ||
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if (append) { | ||
require(exists()) {"File $file doesn't exist yet, cannot append"} | ||
dataFileWriter.appendTo(file) | ||
} else { | ||
if (compression) dataFileWriter.setCodec(CodecFactory.snappyCodec()) | ||
dataFileWriter.setSyncInterval(syncInterval) | ||
dataFileWriter.create(schema, file) | ||
} | ||
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val job = launch { | ||
feed.play(channel) | ||
channel.close() | ||
} | ||
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val arraySchema = Schema.createArray(Schema.create(Schema.Type.DOUBLE)) | ||
try { | ||
val record = GenericData.Record(schema) | ||
val serializer = PriceActionSerializer() | ||
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while (true) { | ||
val event = channel.receive() | ||
val now = event.time.toEpochNano() | ||
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for (action in event.items.filterIsInstance<PriceItem>()) { | ||
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val asset = action.asset | ||
record.put(0, now) | ||
record.put(1, asset.symbol) | ||
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val serialization = serializer.serialize(action) | ||
record.put(2, serialization.type) | ||
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val arr = GenericData.Array<Double>(serialization.values.size, arraySchema) | ||
arr.addAll(serialization.values) | ||
record.put(3, arr) | ||
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record.put(4, serialization.other) | ||
dataFileWriter.append(record) | ||
} | ||
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} | ||
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} catch (_: ClosedReceiveChannelException) { | ||
// On purpose left empty, expected exception | ||
} finally { | ||
channel.close() | ||
if (job.isActive) job.cancel() | ||
dataFileWriter.sync() | ||
dataFileWriter.close() | ||
} | ||
} | ||
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} | ||
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