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BlogTry Demo
SearchK
QUERY
STREAMING
DATAFRAMES
Open-core* query engine for building apps and analytics with real-time streams
and batch data
Try Live Demo
or start with docker
curl https://raw.githubusercontent.com/deephaven/deephaven-core/main/containers/python/base/docker-compose.yml -O
docker-compose pull
docker-compose up
11/t = quotesAll.aj(quotesSpy, "Timestamp", "WtdMid_Spy = WtdMid")\
.updateView("Ratio = WtdMid_Spy / WtdMid")
Deephaven has been battle-tested inside prominent hedge funds, investment banks,
and stock exchanges, managing billions in assets. Every day.
DATA SYSTEM
A powerful query engine and framework providing tools and experiences for the
whole team
DATA SOURCES
Access and ingest data directly from popular, standard formats.
DATA PROCESSING
Build applications and do analytics using Python, Java, or C++, with a single
abstraction for batches and streams. Use time-series features and complex joins.
Combine custom functions with table operations on both ticking and static data.
Works with familiar libraries like Pandas, TensorFlow, Numba.
DATA CONSUMERS
Exhaust new streams or write to persistent stores, build and share real-time
visualizations and monitors. Explore massive and ticking datasets with built in
tools. Build enterprise apps.
WHY DEEPHAVEN?
STREAMING DATA
DONE RIGHT
SERIOUS
PERFORMANCE
Engineered to track table additions, removals, modifications, and shifts, users
benefit from Deephaven’s highly-optimized, incremental-update model. A
chunk-oriented architecture delivers best-of-class table methods and amortizes
the cost of moving between languages.
Client-server interfaces are designed with large-scale, dense data in mind --
moving compute to the server and providing lazy updates.
BUILD, JOIN, AND PUBLISH STREAMS WITH EASE
Build streams on streams to empower applications and do analysis. Use table
operations or marry them to custom and third-party libraries. Query and combine
batch and real-time data.
HIGHLY
INTUITIVE
New data and events seamlessly arrive as simple table updates. Queries establish
an acyclic graph, with data logically flowing to downstream nodes. Simply name a
source or derived table to make it available to clients via multi-language APIs.
Use easy methods to stripe and pipeline workloads.
FAMILIAR &
POWERFUL TOOLS
Leverage gRPC and Arrow. Use Jupyter, Visual Studio, JetBrains, or [soon] R
Studio. Bring your custom or 3rd-party libraries and functions to the data for
faster and well-integrated execution. Enjoy the data interrogation experiences
of the Code Studio, with dynamic dashboards and an evolving suite of
capabilities.
EXPRESSIVE LANGUAGE
BUILT FOR DEVELOPERS, LOVED BY DATA SCIENTISTS
COMBINE YOUR STATIC AND REAL-TIME DATA SOURCES
Join and merge Kafka streams with Parquet files. Use identical operations on
batch and stream.
JOIN YOUR TIME SERIES AND AGGREGATE
STREAMLINE YOUR DATA SCIENCE
LEVERAGE GRPC AND ARROW FLIGHT LIBRARIES
from deephaven import ConsumeKafka, ParquetTools, TableTools
from deephaven2.parquet import read_table
# data-ingestion integrations (Kafka, Parquet, and many more)
table_today_live = ConsumeKafka.consumeToTable(
{"bootstrap.servers": "kafka:9092"}, "metrics"
)
table_yesterday = ParquetTools.readTable("/data/metrics.parquet")
# merging dynamic with static is easy; the updating table will continue to update
table_merged = TableTools.merge(table_today_live, table_yesterday)
# operators can be used identically on dynamic and static tables (or merges of the two)
table_joined = table_today_live.sumBy("ProcessKey").naturalJoin(
table_yesterday.sumBy("ProcessKey"), "ProcessKey", "YestTotal = Metric"
)
bitcoin = ConsumeKafka.consumeToTable({"bootstrap.servers": "kafka:9092"}, "bitcoin")
ethereum = ConsumeKafka.consumeToTable({"bootstrap.servers": "kafka:9092"}, "ethereum")
# time series joins update as source tables update
priceRatio = (
bitcoin.aj(ethereum, "Timestamp", "SizeEth = Size, PriceEth = Price")
.update("Ratio = Price / PriceEth")
.renameColumns("SizeBtc = Size")
)
# time-bin by minute and aggregate accordingly
agg = priceRatio.update("TimeBin = upperBin(Timestamp, MINUTE)").by(
["TimeBin"],
[
AggAvg("Ratio"),
AggMin("MinRatio = Ratio"),
AggMax("MaxRatio = Ratio"),
AggSum("Size", "SizeBtc"),
AggWAvg("SizeBtc", "VwapBtc = Price"),
],
)
import numpy as np
from sklearn.linear_model import LinearRegression
# write a custom function
def computeBeta(value1, value2):
stat1 = np.diff(np.array(value1), n=1).reshape(-1, 1)
stat2 = np.diff(np.array(value2), n=1).reshape(-1, 1)
reg = LinearRegression(fit_intercept=True)
reg.fit(value1, value2)
return reg.coef_[0][0]
# filter, sort and do time-series joins on source tables
iot = source.where("MeasureName = `Example`").view(
"TimeInterval", "DeviceId", "MeasureValue"
)
iot_joined = iot.aj(iot.where("DeviceId = `Master`"), "TimeInterval", "Measure_Master")
# use the custom function within the deephaven object directly
# no client-server or copy
betas = (
iot_joined.by("DeviceId")
.select(
"DeviceId",
"Beta = (double) computeBeta.call(Measure_Master.toArray(), MeasureValue.toArray())",
)
.sort("DeviceId")
)
* Java Client
* Python Client
* C++ Client
* JavaScript Client
FlightSession session = newSession();
TableSpec trades = readQst("trades.qst");
TableSpec quotes = readCsv("quotes.csv");
TableSpec topTenTrades = trades
.aj(quotes, "Timestamp", "Mid")
.updateView("Edge=abs(Price-Mid)")
.sortDescending("Edge")
.head(100);
try (
final Export export = session.export(topTenTrades);
final FlightStream flightStream = session.getStream(export)) {
while (flightStream.next()) {
System.out.println(flightStream.getRoot().contentToTSVString());
}
}
from pydeephaven import Session
from pyarrow import csv
session = Session() # assuming DH is running locally with the default config
table1 = session.import_table(csv.read_csv("data1.csv"))
table2 = session.import_table(csv.read_csv("data2.csv"))
joined_table = table1.join(
table2, keys=["key_col_1", "key_col_2"], columns_to_add=["data_col1"]
)
df = joined_table.snapshot().to_pandas()
print(df)
session.close()
auto client = Client::connect(server);
auto manager = client.getManager();
auto trades = manager.fetchTable("trades");
auto quotes = manager.readCsv("quotes.csv");
auto topTenTrades = trades
.aj(quotes, "Timestamp", "Mid")
.updateView("Edge=abs(Price-Mid)")
.sortDescending("Edge")
.head(100);
std::cout << topTenTrades.stream(true) << '\n';
class TableView {
setFilter() {
this._filters = Array.prototype.slice.apply(arguments);
return this._table.applyFilter(this._filters);
}
addFilter(filter) {
this._filters.push(filter);
return this._table.applyFilter(this._filters);
}
// Use cloning when you want to create a new table
// to apply filters without modifying the existing table.
clone(name) {
if (!name) {
name = `${this._name}Clone`;
}
return this._table.copy().then((newTable) => new TableView(name, newTable));
}
}
UI TOOLS
OPEN-SOURCE CODE STUDIO FOR ACCELERATED DATA EXPLORATION
Browser based interactive REPL for immediate feedback.
Industry leading data-grid, handles billions of rows with ease.
Plot large data sets with automatic downsampling.
Auto-complete column names for rapid data exploration.
BUILD WITH DEEPHAVEN
WHAT CAN YOU BUILD WITH DEEPHAVEN?
Use one of the following example apps or starter projects to get going fast
INHERIT KAFKA STREAMS AS UPDATING TABLES
A demo of two ways to consume events.
Use an interactive demo
COMBINE STREAMING FEEDS WITH PYTHON
A machine that uses Twitter and colors to solve WORDLE.
Watch the video
DRIVE UX WITH WEB-SCRAPED CONTENT
A dashboard for live sports betting lines.
See project's GitHub
BUILD APPS WITH REAL-TIME DATA
A stock monitor using Redpandas & DX-Feed.
Read blog linked to code
DO AI IN REAL TIME
Dynamic unsupervised learning to detect fraud.
Read blog linked to code
SOURCE TICKING DATA FROM CUSTOM APIS
A framework for trading via Interactive Brokers.
See project's GitHub
INTEROPERATE WITH YOUR TOOLS
A PlugIn demo for matplotlib.
Watch the video
PULL DATA FROM REST APIS
An integration with Prometheus.
Read blog linked to code
SCALE UP
ENTERPRISE DEPLOYMENT
Deephaven Enterprise has been battle-tested inside the demanding environment of
hedge funds, stock exchanges and banks. Its collection of enterprise-ready tools
and exclusive add-ons helps your team scale up quickly and benefit from the
mutualization of enhancement requests. Professional services are available if
you’d like more hands on deck.
BATTERIES INCLUDED DATA MANAGEMENT
DATA MANAGEMENT
Systems for ingesting, storing and disseminating data focus on throughput and
efficiency. Utilities exist to support cleaning, validation, and transformation.
Sophisticated control systems limit user or team access to source and derived
data, by directory and table; as well as granularly by row or column key.
SCALE ACROSS 1000S OF CORES, PBS OF DATA, AND TBS OF STREAMS
QUERY & COMPUTE
The Deephaven Enterprise platform comprises the machinery, operations, and
workflows to develop and support applications and analytics at scale --
real-time and otherwise. It is readily deployed on commoditized cloud or
physical Linux resources using modern techniques. Ingest, storage, and compute
scale independently.
CREATE AND SHARE APPLICATIONS AND INTERACTIVE DASHBOARDS QUICKLY
UI & TOOLING
Deephaven Enterprise has premiere experiences in Jupyter, Excel, R-Studio and
classic IDE’s and its REPL, but it also includes a zero-time UX for launching,
scheduling, and monitoring applications. These feed dependent enterprise apps
and empower the quick configuration and sharing of real-time dashboards.
INTEGRATIONS
INTEGRATES WITH FAMILIAR AND POWERFUL TOOLS
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