Discover a Delicious Way to Use Delta Lake! Yummy Delta - #1 Introduction

yummy delta

Delta lake is an open source storage framework for building lake house architectures on top of data lakes.

Additionally it brings reliability to data lakes with features like: ACID transactions, scalable metadata handling, schema enforcement, time travel and many more.

Before you will continue reading please watch short introduction:

Delta lake can be used with compute engines like Spark, Flink, Presto, Trino and Hive. It also has API for Scala, Java, Rust , Ruby and Python.

delta lake

To simplify integrations with delta lake I have built a REST API layer called Yummy Delta.

Which abstracts multiple delta lake tables providing operations like: creating new delta table, writing and querying, but also optimizing and vacuuming. I have coded an overall solution in rust based on the delta-rs project.

Delta lake keeps the data in parquet files which is an open source, column-oriented data file format.

Additionally it writes the metadata in the transaction log, json files containing information about all performed operations.

The transaction log is stored in the delta lake _delta_log subdirectory.

delta lake

For example, every data write will create a new parquet file. After data write is done a new transaction log file will be created which finishes the transaction. Update and delete operations will be conducted in a similar way. On the other hand when we read data from delta lake at the first stage transaction files are read and then according to the transaction data appropriate parquet files are loaded.

Thanks to this mechanism the delta lake guarantees ACID transactions.

There are several delta lake integrations and one of them is delta-rs rust library.

Currently in delta-rs implementation we can use multiple storage backends including: Local filesystem, AWS S3, Azure Blob Storage and Azure Deltalake Storage Gen 2 and also Google Cloud Storage.

To be able to manage multiple delta tables on multiple stores I have built Yummy delta server which expose Rest API.

delta lake

Using API we can: list and create delta tables, inspect delta tables schema, append or override data in delta tables and additional operations like optimize or vacuum.

You can find API reference here:

Moreover we can query data using Data Fusion sql-s. Query results will be returned as a stream thus we can process it in batches.

You can simply install Yummy delta as a python package:

pip3 install yummy[delta]

Then we need to prepare config file:

  - name: local
    path: "/tmp/delta-test-1/"
  - name: az
    path: "az://delta-test-1/"

And you are ready run server using command line:

yummy delta server -h -p 8080 -f config.yaml

Now we are able to perform all operations using the REST API.

Improve the performance of MLflow models with Rust


Realtime models deployment is a stage where performance is critical. In this article I will show how to speedup MLflow models serving and decrease resource consumption.

Additionally benchmark results will be presented.

Before you will continue reading please watch short introduction:

The Mlflow is opensource platform which covers end to end machine learning lifecycle

Including: Tracking experiments, Organizing code into reusable projects, Models versioning and finally models deployment.

mlops circle

With Mlflow we can easily serve versioned models.

Moreover it supports multiple ML frameworks and abstracts them with consistent Rest API.

Thanks to this we can experiment with multiple models flavors without affecting existing integration.

mlflow serving

Mlflow is written in python and uses python to serve real-time models. This simplifies the integration with ML frameworks which expose python API.

On the other hand real-time models serving is a stage where prediction latency and resource consumption is critical.

Additionally serving robustness is required even for higher load.

To check how the rust implementation will perform I have implemented the ML models server which can read Mlflow models and expose the same Rest API.

For test purposes I have implemented integration with LightGBM and Catboost models flavors. Where I have used Rust bindings to the native libraries.


I have used Vegeta attack to perform load tests and measure p99 response time for a different number of requests per seconds. Additionally I have measured the CPU and memory usage of the model serving container. All tests have been performed on my local machine.

The performance tests show that rust implementation is very promising.

benchmark results

For all models even for 1000 requests per second the response time is low. CPU usage increases linearly as traffic increases. And memory usage is constant.

On the other hand Mlflow serving python implementation performs much worse and for higher traffic the response times are higher than 5 seconds which exceeds timeout value. CPU usage quickly consumes available machine resources. The memory usage is stable for all cases.

The Rust implementation is wrapped with the python api and available in yummy. Thus you can simply install and run it through the command line or using python code.

pip install yummy-mlflow
import yummy_mlflow

# yummy_mlflow.model_serve(MODEL_PATH, HOST, POST, LOG_LEVEL)

yummy_mlflow.model_serve(model_path, '', 8080, 'error')

Example requests:

curl -X POST "http://localhost:8080/invocations" \
-H "Content-Type: application/json" \
-d '{
    "columns": ["0","1","2","3","4","5","6","7","8","9","10",
    "data": [
     [ 0.913333, -0.598156, -0.425909, -0.929365,  1.281985,
       0.488531,  0.874184, -1.223610,  0.050988,  0.342557,
      -0.164303,  0.830961,  0.997086,

Example response:

[[0.9849612333276241, 0.008531186707393178, 0.006507579964982725]]

The whole implementation and benchmark code is available on Github. Currently LightGBM and Catboost local models are supported.

The Yummy mlflow models server usage description is available on:

Speedup features serving with Rust - Yummy serve


In this article I will introduce Yummy feature server implemented in Rust. The feature server is fully compatible with Feast implementation. Additionally benchmark results will be presented.

Before you will continue reading please watch short introduction:

Another step during MLOps process creation is features serving.

A historical feature store is used during model training to fetch a large range of entities and a large dataset with small numbers of queries. For this process the data fetch latency is important but not critical.

On the other hand when we serve the model features, fetching latency is crucial and determines prediction time.

feature store

That’s why we use very fast online stores like Redis or DynamoDb.


The question which appears at this point is shall we call online store directly or use feature server ?

Because multiple models can reuse already prepared features we don’t want to add feature store dependencies to the models. Thus we abstract an online store with a feature server which serves features using for example REST api.


On the other hand latency due to additional layer should be minimized.

Using Feast, we can manage features lifecycle and we can serve features using built-in features server implemented in: python, java or go.


According to the provided benchmark Feast feature server is very fast. But can we go faster with the smaller number of computing resources ?

To answer this question I have implemented feature server using Rust which is known for its speed and safety.

One of the basic assumptions was to ensure full compatibility with Feast and usage simplicity.

I have also decided to start implementation with Redis as an online store.

The whole benchmark code is available on github.

To reproduce benchmark we will clone the repository:

git clone
cd feature-servers-benchmarks

For simplicity I will use docker. Thus in the first step we will prepare all required images: Feast and Yummy feature server, Vegeta attack load generator and Redis.


Then I will use data generator to prepare dataset apply feature store and materialize it to Redis.


Now we are ready to start the benchmark.

In contrast to the Feast benchmark where they used sixteen feature store server instances I will perform it with a single instance to simulate behavior on the smaller number of resources.

The whole benchmark contains multiple scenarios like changing number of entities, number of features or increasing number of requests per second.

# single_run <entities> <features> <concurrency> <rps>

echo "Change only number of rows"

single_run 1 50 $CONCURRENCY 10

for i in $(seq 10 10 100); do single_run $i 50 $CONCURRENCY 10; done

echo "Change only number of features"

for i in $(seq 50 50 250); do single_run 1 $i $CONCURRENCY 10; done

echo "Change only number of requests"

for i in $(seq 10 10 100); do single_run 1 50 $CONCURRENCY $i; done

for i in $(seq 100 100 1000); do single_run 1 50 $CONCURRENCY $i; done

for i in $(seq 10 10 100); do single_run 1 250 $CONCURRENCY $i; done

for i in $(seq 10 10 100); do single_run 100 50 $CONCURRENCY $i; done

for i in $(seq 10 10 100); do single_run 100 250 $CONCURRENCY $i; done

All results are available on GitHub but here I will limit it to p99 response time analysis for different numbers of requests.

All results were performed on my local machine with 6 cpu cores 2.59 GHz and 32 GB of memory.

During these tests I will fetch a single entity with fifty features using feature service.

To run Rust feature server benchmark we will run:


For Rust implementation p99 response times are stable and less than 4 ms going from 10 requests per seconds to 100 requests per second.

yummy benchmark results

For Feast following documentation I have set go_feature_retrieval to True in feature_store.yaml

registry: registry.db
project: feature_repo
provider: local
  type: redis
  connection_string: redis:6379
  type: file
go_feature_retrieval: True
entity_key_serialization_version: 2

Additionally go option in feast serve command line.

feast serve --host "" --port 6566 --no-access-log --no-feature-log --go

Thus I assume that go implementation of the feature server will be used. In this part I have used the official feastdev/feature-server:0.26.0 Feast docker image.

Again I will fetch a single entity with fifty features using feature service. For 10 requests per second the p99 response time is 92 ms.

Unfortunately for 20 requests per seconds and above the p99 response time is above 5s which exceeds our timeout value.

feast benchmark results

Additionally during Feast benchmark run I have noticed increasing memory allocation which can be caused by the memory leak.

This benchmark indicates that rust implementation is very promising because response times are small and stable, additionally the resources consumption is low.

The Yummy feature server usage description is available on:

Graph Embeddings with Feature Store


In this video I will show how to generate and use graph embeddings with feature store.

Before you will continue reading please watch short introduction:

Graphs are structures, which contain sets of entity nodes and edges, which represent the interaction between them. Such data structures, can be used in many areas like social networks, web data, or even molecular biology, for modeling real-life interactions.

To use properties contained in the graphs, in the machine learning algorithms, we need to map them, to more accessible representations, called embeddings.


In contrast to the graphs, the embeddings are structures, representing the nodes features, and can be easily used, as an input of the machine learning algorithms.

Because graphs are frequently represented by the large datasets, embeddings calculation can be challenging. To solve this problem, I will use a very efficient open source project, Cleora which is entirely written in rust.


Let’s follow the Cleora algorithm. In the first step we need to determine the number of features which will determine the embedding dimensionality. Then we initialize the embeddings matrix. In the next step based on the input data we calculate the random walk transition matrix. The matrix describes the relations between nodes and is defined as a ratio of number of edges running from first to second node, and the degree of the first node. The training phase is iterative multiplication of the embeddings matrix and the transition matrix followed by L2 normalization of the embeddings rows.

Finally we get embedding matrix for the defined number of iterations.


Moreover, to be able to simply build a solution, I have extended the project, with possibility of reading and writing to S3 store, and Apache Parquet format usage, which significantly reduce embedding size.


Additionally, I have wrapped the rust code, with the python bindings, thus we can simply install it and use it as a python package.

Based on the Cleora example, I will use the Facebook dataset from SNAP, to calculate embeddings from page to page graph, and train a machine learning model, which classifies page category.

curl -LO

As a s3 store we will use minio storage:

docker run --rm -it -p 9000:9000 \
 -p 9001:9001 --name minio \
 -v $(pwd)/minio-data:/data \
 --network app_default \
 minio/minio server /data --console-address ":9001"
import os 
import boto3
from botocore.client import Config

os.environ["AWS_ACCESS_KEY_ID"]= "minioadmin"
os.environ["AWS_SECRET_ACCESS_KEY"]= "minioadmin"
os.environ["S3_ENDPOINT_URL"]= "http://minio:9000"

s3 = boto3.resource('s3', endpoint_url='http://minio:9000')

In the first step, we need to prepare the input file, in the appropriate click, or star expansion format.

# based on:
import pandas as pd
import s3fs
import numpy as np
import random
from sklearn.model_selection import train_test_split

df_cleora = pd.read_csv("./facebook_large/musae_facebook_edges.csv")
train_cleora, test_cleora = train_test_split(df_cleora, test_size=0.2)

fb_cleora_input_clique_filename = "s3://input/fb_cleora_input_clique.txt"
fb_cleora_input_star_filename = "s3://input/fb_cleora_input_star.txt"

fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': "http://minio:9000"})

with, "w") as f_cleora_clique,, "w") as f_cleora_star:
    grouped_train = train_cleora.groupby('id_1')
    for n, (name, group) in enumerate(grouped_train):
        group_list = group['id_2'].tolist()
        group_elems = list(map(str, group_list))
        f_cleora_clique.write("{} {}\n".format(name, ' '.join(group_elems)))
        f_cleora_star.write("{}\t{}\n".format(n, name))
        for elem in group_elems:
            f_cleora_star.write("{}\t{}\n".format(n, elem))

Then, we use Cleora python bindings, to calculate embeddings, and write them as a parquet file in the s3 minio store.

Cleora star expansion training:

import time
import cleora
output_dir = 's3://output'
fb_cleora_input_star_filename = "s3://input/fb_cleora_input_star.txt"

start_time = time.time()
    cols_str="transient::cluster_id StarNode",
print("--- %s seconds ---" % (time.time() - start_time))

Cleora clique expansion training

fb_cleora_input_clique_filename = "s3://input/fb_cleora_input_clique.txt"
start_time = time.time()
print("--- %s seconds ---" % (time.time() - start_time))

For each node, I have added an additional column datetime which represents timestamp, and will help to check how calculated embeddings, will change over time. Additionaly every embeddings recalculation will be saved as a separate parquet file eg. emb__CliqueNode__CliqueNode_20220910T204145.parquet. Thus we will be able to follow embeddings history.

Now, we are ready to consume the calculated embeddings, with Feast feature store, and Yummy extension.


project: repo
registry: s3://data/registry.db
provider: yummy.YummyProvider
backend: polars
    type: sqlite
    path: data/online_store.db
    type: yummy.YummyOfflineStore

from datetime import timedelta
from feast import Entity, Field, FeatureView
from yummy import ParquetSource
from feast.types import Float32, Int32

my_stats_parquet = ParquetSource(

my_entity = Entity(name="entity", description="entity",)

schema = [Field(name="entity", dtype=Int32)] + [Field(name=f"f{i}", dtype=Float32) for i in range(0,1024)]

mystats_view_parquet = FeatureView(
    online=True, source=my_stats_parquet, tags={},)

Then we apply feature store definition:

feast apply

Now we are ready to fetch ebeddings for defined timestamp.

from feast import FeatureStore
import polars as pl
import pandas as pd
import time
import os
from datetime import datetime
import yummy

store = FeatureStore(repo_path=".")
start_time = time.time()

features = [f"my_statistics_parquet:f{i}" for i in range(0,1024)]

training_df = store.get_historical_features(
    entity_df=yummy.select_all(datetime(2022, 9, 14, 23, 59, 42)),
    features = features,

print("--- %s seconds ---" % (time.time() - start_time))

Moreover I have introduced method:

yummy.select_all(datetime(2022, 9, 14, 23, 59, 42))

which will fetch all entities.

Then we prepare training data for data for the SNAP dataset:

import numpy as np
from sklearn.model_selection import train_test_split
df = pd.read_csv("../facebook_large/musae_facebook_target.csv")

classes = df['page_type'].unique()
class_ids = list(range(0, len(classes)))
class_dict = {k:v for k,v in zip(classes, class_ids)}
df['page_type'] = [class_dict[item] for item in df['page_type']]

train_filename = "fb_classification_train.txt"
test_filename = "fb_classification_test.txt"

train, test = train_test_split(df, test_size=0.2)

training_df=training_df.astype({"entity": "int32"})

entities = training_df["entity"].to_numpy()

train = train[["id","page_type"]].to_numpy()
test = test[["id","page_type"]].to_numpy()

    .rename(columns={ f"f{i}":i+2 for i in range(1024) })\
    .rename(columns={"entity": 0}).set_index(0)

valid_idx = df_embeddings.index.to_numpy()
train = np.array(train[np.isin(train[:,0], valid_idx) & np.isin(train[:,1], valid_idx)])
test = np.array([t for t in test if (t[0] in valid_idx) and (t[1] in valid_idx)])

Finally, we will train page classifiers.

from sklearn.linear_model import SGDClassifier
from sklearn.metrics import f1_score
from tqdm import tqdm
batch_size = 256
test_batch_size = 1000
y_train = train[:, 1]
y_test = test[:, 1]

clf = SGDClassifier(random_state=0, loss='log_loss', alpha=0.0001)
for e in tqdm(range(0, max(epochs))):
    for idx in range(0,train.shape[0],batch_size):
        ex_emb_in = embeddings.loc[ex[:,0]].to_numpy()
        ex_y = y_train[idx:min(idx+batch_size,train.shape[0])]
        clf.partial_fit(ex_emb_in, ex_y, classes=[0,1,2,3])
    if e+1 in epochs:
        acc = 0.0
        y_pred = []
        for n, idx in enumerate(range(0,test.shape[0],test_batch_size)):
            ex_emb_in = embeddings.loc[ex[:,0]].to_numpy()
            pred = clf.predict_proba(ex_emb_in)
            classes = np.argmax(pred, axis=1)

        f1_micro = f1_score(y_test, y_pred, average='micro')
        f1_macro = f1_score(y_test, y_pred, average='macro')
        print(' epochs: {}, micro f1: {}, macro f1:{}'.format( e+1, f1_micro, f1_macro))

Because feature store can merge multiple sources, we can easily enrich graph embeddings, with additional features like additional page information.

We can also track, embeddings historical changes.


Moreover, using feature store we can materialize embeddings to online store, which simplifies building a comprehensive MLOps process.

You can find the whole example.ipynb on github and yummy documentation.

Real-time ingested historical feature store with Iceberg, Feast and Yummy.


In this video I will show how to use Apache Iceberg as a store for historical feature store. Moreover we will build end to end real-time ingestion example with:

  • Postgres
  • Kafka connect
  • Iceberg on Minio
  • Feast with Yummy extension

Before you will continue reading please watch short introduction:

Apache Iceberg, is an high-performance table format, which can be used for huge analytic datasets.

Iceberg offers several features like: schema evolution, partition evolution and hidden partitioning, and many more, which can be used to effectively process, petabytes of data.

Read more if you want to learn more about Iceberg features and how it compares to the other lake formats (Delta Lake and Hudi).

Apache Iceberg, is perfect candidate to use as an historical store thus I have decided to integrate it, with the Feast feature store through, Yummy extension.

To show how to use it I will describe end to end solution with the real-time Iceberg ingestion from the other data sources.

To do this, I will use Kafka connect, with Apache Iceberg Sink This can be used, to build Iceberg lake on on-premise s3 store, or to move your data and build a feature store in the cloud.

The Kafka connect inegration is based on the article. The source code of the Iceberg sink is available on getindata/kafka-connect-iceberg-sink.

You can follow the solution in the notebook: example.ipynb and simply reproduce using docker.


Suppose, we have our transactional system based on the postgres database, where we keep current clients features. We will track features changes, to build historical feature store.

The Kafka Connect, will use debezium postgres connector, to track every data change and put it to the Iceberg using Iceberg sink.

We will store iceberg tables, on the minio s3 store, but of course you can use AWS S3.

Kafka Connect, is based on Kafka, thus we will also need a Kafka instance and zookeeper.

We will setup selected components using docker.

To start clone the repository:

git clone
cd yummy-iceberg-kafka-connect

Then run ./

docker run -it --name postgres --rm --network=app_default \
 -e POSTGRES_PASSWORD=postgres \
 -p 5432:5432 postgres:12.11 -c wal_level=logical


docker run -it --rm --name zookeeper --network app_default \


docker run -it --rm --name kafka --network app_default -p 9092:9092 \


docker run --rm -it -p 9000:9000 \
 -p 9001:9001 --name minio \
 -v $(pwd)/minio-data:/data \
 --network app_default \
 minio/minio server /data --console-address ":9001"


docker run -it --name connect --rm --network=app_default -p 8083:8083 \
        -e GROUP_ID=1 \
        -e CONFIG_STORAGE_TOPIC=my-connect-configs \
        -e OFFSET_STORAGE_TOPIC=my-connect-offsets \
        -e BOOTSTRAP_SERVERS=kafka:9092 \
        -v $(pwd)/kafka-connect-iceberg-sink/kafka-connect-iceberg-sink-0.1.3-shaded.jar:/kafka/connect/kafka-connect-iceberg-sink/kafka-connect-iceberg-sink-0.1.3-shaded.jar \

Please note that components setup is not production ready and you should use only for testing purposes.

Finally we will run the local jupyter notebooks with the local spark: ./

docker run -it -p 8887:8888 --rm --shm-size=5.09gb --name yummy \
	--network app_default \
	-v $(pwd)/notebooks:/home/jovyan/notebooks \
	qooba/yummy:v0.0.2_spark /home/jovyan/notebooks/

where is:


export FEAST_USAGE=False
export PYSPARK_PYTHON=/opt/conda/bin/python3 
export PYSPARK_DRIVER_PYTHON_OPTS="notebook --notebook-dir=/home/jovyan --ip='' --port=8888 --no-browser --allow-root --NotebookApp.password='' --NotebookApp.token=''"

#pip3 install rise

pyspark \
    --packages org.apache.iceberg:iceberg-spark-runtime-3.2_2.12:0.13.2,org.apache.hadoop:hadoop-aws:3.3.1, \
    --conf "spark.driver.memory=5g" \
    --conf "spark.executor.memory=5g" \
    --conf "spark.sql.extensions=org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions" \
    --conf "spark.sql.catalog.local=org.apache.iceberg.spark.SparkCatalog" \
    --conf "spark.sql.catalog.local.type=hadoop" \
    --conf "spark.sql.catalog.local.warehouse=s3a://mybucket" \
    --conf "spark.hadoop.fs.s3a.endpoint=http://minio:9000" \
    --conf "spark.hadoop.fs.s3a.access.key=minioadmin" \
    --conf "spark.hadoop.fs.s3a.secret.key=minioadmin" \
    --conf "spark.hadoop.fs.s3a.impl=org.apache.hadoop.fs.s3a.S3AFileSystem" \
    --conf "" \
    --conf "spark.hadoop.fs.s3a.connection.ssl.enabled=false"

Now open the browser url: http://localhost:8887/tree/notebooks

All below commands are already in the example.ipynb notebook but I will explain all of them.

Kafka Connect, will publish database changes to the kafka, thus we also need to create appropriate topics, if we don’t have topics auto-creation enabled.

from confluent_kafka.admin import AdminClient, NewTopic

admin_client = AdminClient({
    "bootstrap.servers": "kafka:9092"

topic_list = []
topic_list.append(NewTopic("postgres.public.mystats_fv1", 1, 1))
topic_list.append(NewTopic("postgres.public.mystats_fv2", 1, 1))

I have created two topics because we will track the two postgress tables.

Now, we can setup a postgres connector, and Iceberg sink through, Kafka connect api.
In the postgres connector, we need to specify a list of tables, which we want to track.

import requests
import json

data = {
  "name": "postgres-connector",  
  "config": {
    "connector.class": "io.debezium.connector.postgresql.PostgresConnector", 
    "database.hostname": "postgres", 
    "database.port": "5432", 
    "database.user": "postgres", 
    "database.password": "postgres", 
    "database.dbname" : "postgres", 
    "": "postgres",
    "": "debezium",
    "": "pgoutput",
    "table.include.list": "public.mystats_fv1,public.mystats_fv2"

headers = { "Content-Type": "application/json" }
url="http://connect:8083/connectors", headers=headers, data=json.dumps(data))

Because debezium, has a wide range of integrations you can also use other databases like: mysql, mongodb, oracle, sql server or db2.

In the next step, we will post iceberg sink configuration, where we specify the topics to read, but also table and s3 store configuration.

import requests
import json
data = {
  "name": "iceberg-sink",
  "config": {
    "connector.class": "com.getindata.kafka.connect.iceberg.sink.IcebergSink",
    "topics": "postgres.public.mystats_fv1,postgres.public.mystats_fv2",
    "upsert": False,
    "upsert.keep-deletes": True,
    "": True,
    "table.write-format": "parquet",
    "table.namespace": "mytable_dbz",
    "table.prefix": "debeziumcdc_",
    "iceberg.warehouse": "s3a://mybucket",
    "iceberg.fs.defaultFS": "s3a://mybucket", 
    "iceberg.catalog-name": "mycatalog", 
    "iceberg.catalog-impl": "org.apache.iceberg.hadoop.HadoopCatalog", 
    "": True,
    "iceberg.fs.s3a.endpoint": "http://minio:9000",
    "iceberg.fs.s3a.impl": "org.apache.hadoop.fs.s3a.S3AFileSystem",
    "iceberg.fs.s3a.access.key": "minioadmin",
    "iceberg.fs.s3a.secret.key": "minioadmin",

headers = { "Content-Type": "application/json" }
url="http://connect:8083/connectors", headers=headers, data=json.dumps(data))

Kafka connect is ready, thus we will simulate database changes, using generated data. We will split features, into two tables.

import pandas as pd
import numpy as np
from datetime import datetime, timezone
from sklearn.datasets import make_hastie_10_2
import warnings
import psycopg2
import pandas as pd
from sqlalchemy import create_engine
warnings.filterwarnings("ignore", category=DeprecationWarning)


def generate_entities(size):
    return np.random.choice(size, size=size, replace=False)

def generate_data(entities, year=2021, month=10, day=1) -> pd.DataFrame:
    X, y = make_hastie_10_2(n_samples=n_samples, random_state=0)
    df = pd.DataFrame(X, columns=["f0", "f1", "f2", "f3", "f4", "f5", "f6", "f7", "f8", "f9"])
    df['entity_id'] = entities
    df['datetime'] = pd.to_datetime(
                datetime(year, month, day, 0,tzinfo=timezone.utc).timestamp(),
                datetime(year, month, day, 22,tzinfo=timezone.utc).timestamp(),
    df['created'] = pd.to_datetime(
    return df

alchemyEngine = create_engine('postgresql+psycopg2://postgres:postgres@postgres', pool_recycle=3600);
dbConnection = alchemyEngine.connect();

for d in range(1,15):
    data=generate_data(entities,month=1, day=d)
    fv1 = data[["entity_id", "datetime", "f0", "f1", "f2", "f3", "f4"]]
    fv2 = data[["entity_id", "datetime", "f5", "f6", "f7", "f8", "f9", "y"]]
    fv1.to_sql('mystats_fv1', dbConnection, if_exists='replace')
    fv2.to_sql('mystats_fv2', dbConnection, if_exists='replace')

The historical features, will be saved into an iceberg on minio.



Now we are ready to fetch historical features, using feast and yummy.

To use Yummy with the Iceberg you need to install it:

pip install yummy

Then we need to prepare feature store configuration yaml.

project: example_feature_repo
registry: data/registry.db
provider: local
  type: yummy.YummyOfflineStore
  backend: spark
    spark.master: "local[*]"
    spark.ui.enabled: "false"
    spark.eventLog.enabled: "false"
    spark.sql.session.timeZone: "UTC"
    spark.sql.extensions: "org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions"
    spark.sql.catalog.local: "org.apache.iceberg.spark.SparkCatalog"
    spark.sql.catalog.local.type: "hadoop"
    spark.sql.catalog.local.warehouse: "s3a://mybucket"
    spark.hadoop.fs.s3a.endpoint: "http://minio:9000"
    spark.hadoop.fs.s3a.access.key: "minioadmin"
    spark.hadoop.fs.s3a.secret.key: "minioadmin"
    spark.hadoop.fs.s3a.impl: "org.apache.hadoop.fs.s3a.S3AFileSystem" "true"
    spark.hadoop.fs.s3a.connection.ssl.enabled: "false"
  path: data/online_store.db

Currently, you can use Iceberg, only with the spark backend. You can also, add additional spark configuration, such as catalog configuration or s3 store configuration.

In the next step, you have to add Iceberg Data Source. In the feature store definition, you specify a path to the iceberg table or table name, which you want to consume on filesystem or s3 store respectively.

from datetime import datetime, timezone, timedelta
from google.protobuf.duration_pb2 import Duration
from feast import Entity, Feature, FeatureView, ValueType
from yummy import IcebergDataSource

entity = Entity(name="entity_id", value_type=ValueType.INT64, description="entity id",)

fv1 = FeatureView(
        Feature(name="f0", dtype=ValueType.FLOAT), Feature(name="f1", dtype=ValueType.FLOAT),
        Feature(name="f2", dtype=ValueType.FLOAT), Feature(name="f3", dtype=ValueType.FLOAT),
        Feature(name="f4", dtype=ValueType.FLOAT), ],
    ), tags={},)

fv2 = FeatureView(
        Feature(name="f5", dtype=ValueType.FLOAT), Feature(name="f6", dtype=ValueType.FLOAT),
        Feature(name="f7", dtype=ValueType.FLOAT), Feature(name="f8", dtype=ValueType.FLOAT),
        Feature(name="f9", dtype=ValueType.FLOAT), Feature(name="y", dtype=ValueType.FLOAT), ],
    ), tags={},)

Of course, you can combine the Iceberg data source, with the other data sources like parquets, csv files or even delta lake if needed. Here you see how to do this.

Now, we are ready to apply feature store definition, and fetch historical features.

feast apply
import pandas as pd
import numpy as np
from datetime import datetime, timezone, timedelta
from feast import FeatureStore

def generate_entities(size: int):
    return np.random.choice(size, size=size, replace=False)

def entity_df(size:int = 10):
    entity_df = pd.DataFrame(data=entities, columns=['entity_id'])
    return entity_df

entity_df = entity_df()
        "debeziumcdc_postgres_public_mystats_fv1:f0", "debeziumcdc_postgres_public_mystats_fv1:f1",
        "debeziumcdc_postgres_public_mystats_fv1:f2", "debeziumcdc_postgres_public_mystats_fv1:f3",
        "debeziumcdc_postgres_public_mystats_fv1:f4", "debeziumcdc_postgres_public_mystats_fv2:f5",
        "debeziumcdc_postgres_public_mystats_fv2:f6", "debeziumcdc_postgres_public_mystats_fv2:f7",
        "debeziumcdc_postgres_public_mystats_fv2:f8", "debeziumcdc_postgres_public_mystats_fv2:f9",
    ], entity_df=entity_df, full_feature_names=True).to_df()