Apache Phoenix with Actor Model (Akka.io) for real-time Big Data Programming...Trieu Nguyen
Apache Phoenix with Actor Model (Akka.io) for real-time Big Data Programming Stack
Why we still need SQL for Big Data ?
How to make Big Data more responsive and faster ?
This document summarizes a presentation about Apache Phoenix, an open-source project that allows HBase to be queried with SQL. It discusses what Phoenix is, why tracing is important, and the features of a new tracing web app created for Phoenix, including listing traces, visualizing trace distributions and individual trace details. Programming challenges in creating the app and new issues filed are also summarized.
Apache Phoenix: Use Cases and New FeaturesHBaseCon
James Taylor (Salesforce) and Maryann Xue (Intel)
This talk with be broken into two parts: Phoenix use cases and new Phoenix features. Three use cases will be presented as lightning talks by individuals from 1) Sony about its social media NewsSuite app, 2) eHarmony on its matching service, and 3) Salesforce.com on its time-series metrics engine. Two new features will be discussed in detail by the engineers who developed them: ACID transactions in Phoenix through Apache Tephra. and cost-based query optimization through Apache Calcite. The focus will be on helping end users more easily develop scalable applications on top of Phoenix.
Apache Phoenix and Apache HBase: An Enterprise Grade Data WarehouseJosh Elser
An overview of Apache Phoenix and Apache HBase from the angle of a traditional data warehousing solution. This talk focuses on where this open-source architect fits into the market outlines the features and integrations of the product, showing that it is a viable alternative to traditional data warehousing solutions.
This talk will be an overview of the new features and improvements currently implemented for the Apache Accumulo 1.8.0 release. This will be a discussion about some of these exciting changes with a focus on what is of the most importance for users.
- The document summarizes the state of Apache HBase, including recent releases, compatibility between versions, and new developments.
- Key releases include HBase 1.1, 1.2, and 1.3, which added features like async RPC client, scan improvements, and date-tiered compaction. HBase 2.0 is targeting compatibility improvements and major changes to data layout and assignment.
- New developments include date-tiered compaction for time series data, Spark integration, and ongoing work on async operations, replication 2.0, and reducing garbage collection overhead.
Hortonworks Technical Workshop: HBase and Apache Phoenix Hortonworks
This document provides an overview of Apache HBase and Apache Phoenix. It discusses how HBase is a scalable, non-relational database that can store large volumes of data across commodity servers. Phoenix provides a SQL interface for HBase, allowing users to interact with HBase data using familiar SQL queries and functions. The document outlines new features in Phoenix for HDP 2.2, including improved support for secondary indexes and basic window functions.
- Hive originally only supported updating partitions by overwriting entire files, which caused issues for concurrent readers and limited functionality like row-level updates.
- The need for ACID transactions in Hive arose from wanting to support updating data in near real-time as it arrives and making ad hoc data changes without complex workarounds.
- Hive's ACID implementation stores changes as delta files, uses the metastore to manage transactions and locks, and runs compactions to merge deltas into base files.
- There were initial issues around correctness, performance, usability and resilience, but many have been addressed with ongoing work focused on further improvements and new features like multi-statement transactions and better integration with LLAP.
This talk will give an overview of two exciting releases for Apache HBase 2.0 and Phoenix 5.0. HBase provides a NoSQL column store on Hadoop for random, real-time read/write workloads. Phoenix provides SQL on top of HBase. HBase 2.0 contains a large number of features that were a long time in development, including rewritten region assignment, performance improvements (RPC, rewritten write pipeline, etc), async clients and WAL, a C++ client, offheaping memstore and other buffers, shading of dependencies, as well as a lot of other fixes and stability improvements. We will go into details on some of the most important improvements in the release, as well as what are the implications for the users in terms of API and upgrade paths. Phoenix 5.0 is the next big Phoenix release because of its integration with HBase 2.0 and a lot of performance improvements in support of secondary Indexes. It has many important new features such as encoded columns, Kafka and Hive integration, and many other performance improvements. This session will also describe the uses cases that HBase and Phoenix are a good architectural fit for.
Speaker: Alan Gates, Co-Founder, Hortonworks
Apache Phoenix is a SQL skin over HBase that allows for low latency SQL queries over HBase data. It transforms SQL queries into native HBase APIs like scans and puts. Phoenix supports features like secondary indexing, multi-tenancy, and limited hash joins. It aims to leverage existing SQL tooling while providing performance optimizations like parallel scans. Upcoming features include improved secondary indexing and transaction support. Phoenix maps to existing HBase tables and allows dynamic columns to extend schemas during queries.
Yifeng Jiang presented on Apache Hive's present and future capabilities. Hive has achieved 100x performance improvements through technologies like ORC file format, Tez execution engine, and vectorized processing. Upcoming features like LLAP caching and a persistent Hive server aim to provide sub-second query response times for interactive analytics. Hive continues to evolve as the standard SQL interface for Hadoop, supporting a wide range of use cases from ETL and reporting to real-time analytics.
The Evolution of a Relational Database Layer over HBaseDataWorks Summit
Apache Phoenix is a SQL query layer over Apache HBase that allows users to interact with HBase through JDBC and SQL. It transforms SQL queries into native HBase API calls for efficient parallel execution on the cluster. Phoenix provides metadata storage, SQL support, and a JDBC driver. It is now a top-level Apache project after originally being developed at Salesforce. The speaker discussed Phoenix's capabilities like joins and subqueries, new features like HBase 1.0 support and functional indexes, and future plans like improved optimization through Calcite and transaction support.
The document discusses bringing multi-tenancy to Apache Zeppelin through the use of Apache Livy. Livy is an open-source REST interface that allows interacting with Spark from anywhere and enables features like multi-user sessions and security. It improves on previous versions of interactive analysis in Zeppelin by allowing custom user sessions through Livy and improving security and isolation between users through mechanisms like SPNEGO and impersonation. The integration of Livy provides multi-tenancy, security, and isolation for interactive analysis in Zeppelin.
The document summarizes Apache Phoenix and HBase as an enterprise data warehouse solution. It discusses how Phoenix provides OLTP and analytics capabilities over HBase. It then covers various use cases where companies are using Phoenix and HBase, including for web analytics and time series data. Finally, it discusses optimizations that can be made to the schema design, queries, and writes in Phoenix to improve performance.
This document summarizes a presentation about Apache Phoenix and HBase. It discusses the past, present, and future of SQL on HBase. In the past section, it describes Phoenix's architecture and key features like secondary indexes, joins, and aggregation. The present section highlights recent Phoenix releases including row timestamps, transactions using Tephra, and the new Phoenix Query Server. The future section mentions upcoming integrations with Calcite and Hive.
ORC files were originally introduced in Hive, but have now migrated to an independent Apache project. This has sped up the development of ORC and simplified integrating ORC into other projects, such as Hadoop, Spark, Presto, and Nifi. There are also many new tools that are built on top of ORC, such as Hive’s ACID transactions and LLAP, which provides incredibly fast reads for your hot data. LLAP also provides strong security guarantees that allow each user to only see the rows and columns that they have permission for.
This talk will discuss the details of the ORC and Parquet formats and what the relevant tradeoffs are. In particular, it will discuss how to format your data and the options to use to maximize your read performance. In particular, we’ll discuss when and how to use ORC’s schema evolution, bloom filters, and predicate push down. It will also show you how to use the tools to translate ORC files into human-readable formats, such as JSON, and display the rich metadata from the file including the type in the file and min, max, and count for each column.
HBaseCon2017 Spark HBase Connector: Feature Rich and Efficient Access to HBas...HBaseCon
Both Spark and HBase are widely used, but how to use them together with high performance and simplicity is a very hard topic. Spark HBase Connector(SHC) provides feature rich and efficient access to HBase through Spark SQL. It bridges the gap between the simple HBase key value store and complex relational SQL queries and enables users to perform complex data analytics on top of HBase using Spark.
SHC implements the standard Spark data source APIs, and leverages the Spark catalyst engine for query optimization. To achieve high performance, SHC constructs the RDD from scratch instead of using the standard HadoopRDD. With the customized RDD, all critical techniques can be applied and fully implemented, such as partition pruning, column pruning, predicate pushdown and data locality. The design makes the maintenance very easy, while achieving a good tradeoff between performance and simplicity.
Also, SHC has supported Phoenix data as input to HBase in addition to Avro data. Defaulting to a simple native binary encoding seems susceptible to future changes and is a risk for users who write data from SHC into HBase. For example, with SHC going forward, backwards compatibility needs to be properly handled. So the default, SHC needs to support a more standard and well tested format like Phoenix.
In this talk, we will demo how SHC works, how to use SHC in secure/non-secure clusters, how SHC works with multi-HBase clusters, etc. This talk will also benefit people who use Spark and other data sources (besides HBase) as it inspires them with ideas of how to support high performance data source access at the Spark DataFrame level.
This document discusses Spark security and provides an overview of authentication, authorization, encryption, and auditing in Spark. It describes how Spark leverages Kerberos for authentication and uses services like Ranger and Sentry for authorization. It also outlines how communication channels in Spark are encrypted and some common issues to watch out for related to Spark security.
This document discusses new features in Apache Hive 2.0, including:
1) Adding procedural SQL capabilities through HPLSQL for writing stored procedures.
2) Improving query performance through LLAP which uses persistent daemons and in-memory caching to enable sub-second queries.
3) Speeding up query planning by using HBase as the metastore instead of a relational database.
4) Enhancements to Hive on Spark such as dynamic partition pruning and vectorized operations.
5) Default use of the cost-based optimizer and continued improvements to statistics collection and estimation.
Apache Hive 2.0 provides major new features for SQL on Hadoop such as:
- HPLSQL which adds procedural SQL capabilities like loops and branches.
- LLAP which enables sub-second queries through persistent daemons and in-memory caching.
- Using HBase as the metastore which speeds up query planning times for queries involving thousands of partitions.
- Improvements to Hive on Spark and the cost-based optimizer.
- Many bug fixes and under-the-hood improvements were also made while maintaining backwards compatibility where possible.
This talk will give an overview of two exciting releases for Apache HBase 2.0 and Phoenix 5.0. HBase provides a NoSQL column store on Hadoop for random, real-time read/write workloads. Phoenix provides SQL on top of HBase. HBase 2.0 contains a large number of features that were a long time in development, including rewritten region assignment, performance improvements (RPC, rewritten write pipeline, etc), async clients and WAL, a C++ client, offheaping memstore and other buffers, shading of dependencies, as well as a lot of other fixes and stability improvements. We will go into details on some of the most important improvements in the release, as well as what are the implications for the users in terms of API and upgrade paths. Phoenix 5.0 is the next big Phoenix release because of its integration with HBase 2.0 and a lot of performance improvements in support of secondary Indexes. It has many important new features such as encoded columns, Kafka and Hive integration, and many other performance improvements. This session will also describe the uses cases that HBase and Phoenix are a good architectural fit for.
SQL on Hadoop Batch, Interactive and Beyond.
Public Presentation showing history and where Hortonworks is looking to go with 100% Open Source Technology.
Apache Hive, Apache SparkSQL, Apache Pheonix, and Apache Druid
Future of Data New Jersey - HDF 3.0 Deep DiveAldrin Piri
This document provides an overview and agenda for an HDF 3.0 Deep Dive presentation. It discusses new features in HDF 3.0 like record-based processing using a record reader/writer and QueryRecord processor. It also covers the latest efforts in the Apache NiFi community like component versioning and introducing a registry to enable capabilities like CI/CD, flow migration, and auditing of flows. The presentation demonstrates record processing in NiFi and concludes by discussing the evolution of Apache NiFi and its ecosystem.
This document discusses the evolution of Hadoop and its use cases in the adtech industry. It describes how Hadoop was initially used primarily for batch processing via Hive and MapReduce. Over time, improvements like Tez, Presto, and Impala enabled faster interactive SQL queries on big data. The document also outlines how the Hadoop ecosystem is now used for real-time log collection, reporting, model generation, and more across the entire adtech stack. Key recent developments discussed include improvements in Hive like LLAP that enable sub-second SQL and ACID transactions, as well as tools like Cloudbreak for deploying Hadoop clusters in the cloud.
This document discusses extending the functionality of Apache NiFi through custom processors and controller services. It provides an overview of the NiFi architecture and repositories, describes how to create extensions with minimal dependencies using Maven archetypes, and notes that most extensions can be developed within hours. Quick prototyping of data flows is possible using existing binaries, applications, and scripting languages. Resources for the NiFi developer guide and example Maven projects are also listed.
Apache Deep Learning 101 - DWS Berlin 2018Timothy Spann
Apache Deep Learning 101 with Apache MXNet, Apache NiFi, MiniFi, Apache Tika, Apache Open NLP, Apache Spark, Apache Hive, Apache HBase, Apache Livy and Apache Hadoop. Using Python we run various existing models via MXNet Model Server and via Python APIs. We also use NLP for entity resolution
This document provides an introduction to Apache Kafka. It begins with an overview of Kafka as a distributed messaging system that is real-time, scalable, low latency, and fault tolerant. It then covers key concepts such as topics, partitions, producers, consumers, and replication. The document explains how Kafka achieves fast reads and writes through its design and use of disk flushing and replication for durability. It also discusses how Kafka can be used to build real-time systems and provides examples like connected cars. Finally, it introduces Apache Metron as an example of a cyber security solution built on Kafka.
IoT with Apache MXNet and Apache NiFi and MiniFiDataWorks Summit
1) The document discusses using Apache MXNet for industrial IoT applications. MiniFi ingests camera images and sensor data at the edge and runs Apache MXNet to recognize objects in images. The data is then stored in Hadoop.
2) It describes using Apache MXNet on edge devices like the Raspberry Pi and Nvidia Jetson TX1 to perform tasks like image recognition from cameras and sensors.
3) The document provides information on setting up Apache MXNet on various IoT devices and edge servers to enable machine learning and deep learning capabilities for industrial IoT applications.
MiniFi and Apache NiFi : IoT in Berlin Germany 2018Timothy Spann
Future of Data : Berlin
Apache NiFi and MiniFi with Apache MXNet and Tensorfor for IoT from edge devices like Raspberry Pis. Including Python and other tools.
Apache MXNet for IoT with Apache NiFi. Using Apache MXNet with Apache NiFi and MiniFi for IoT use cases. Ingesting, managing, orchestration and running IoT workloads.
This document provides an agenda and overview for a presentation on deep learning on Hortonworks Data Platform (HDP). The presentation will cover using TensorFlow with Apache NiFi, running TensorFlow on YARN, using pre-built models with Apache MXNet, running an MXNet model server with NiFi, and running MXNet in Zeppelin notebooks and on YARN. It recommends installing CPU and GPU versions of frameworks on appropriate nodes and discusses options like TensorFlow, MXNet, and PyTorch. The document also outlines integrating Apache MXNet with NiFi for tasks like image classification using models on edge nodes or a model server.
Using Apache Hadoop and related technologies as a data warehouse has been an area of interest since the early days of Hadoop. In recent years Hive has made great strides towards enabling data warehousing by expanding its SQL coverage, adding transactions, and enabling sub-second queries with LLAP. But data warehousing requires more than a full powered SQL engine. Security, governance, data movement, workload management, monitoring, and user tools are required as well. These functions are being addressed by other Apache projects such as Ranger, Atlas, Falcon, Ambari, and Zeppelin. This talk will examine how these projects can be assembled to build a data warehousing solution. It will also discuss features and performance work going on in Hive and the other projects that will enable more data warehousing use cases. These include use cases like data ingestion using merge, support for OLAP cubing queries via Hive’s integration with Druid, expanded SQL coverage, replication of data between data warehouses, advanced access control options, data discovery, and user tools to manage, monitor, and query the warehouse.
Speaker
Alan Gates, Co-founder, Hortonworks
Data at Scales and the Values of Starting Small with Apache NiFi & MiNiFiAldrin Piri
This document discusses Apache NiFi and Apache MiNiFi. It begins with an overview of NiFi, describing its key features like guaranteed delivery, data buffering, and data provenance. It then introduces MiNiFi as a smaller version of NiFi that can operate on edge devices with limited resources. A use case is presented of a courier service gathering data from disparate sources using both NiFi and MiNiFi. The document concludes by discussing the NiFi ecosystem and encouraging participation in the community.
The document provides an introduction and overview of Apache NiFi and its architecture. It discusses how NiFi can be used to effectively manage and move data between different producers and consumers. It also summarizes key NiFi features like guaranteed delivery, data buffering, prioritization, and data provenance. Finally, it briefly outlines the NiFi architecture and components as well as opportunities for the future of the MiniFi project.
Data Con LA 2018 - Streaming and IoT by Pat AlwellData Con LA
Hortonworks DataFlow (HDF) is built with the vision of creating a platform that enables enterprises to build dataflow management and streaming analytics solutions that collect, curate, analyze and act on data in motion across the datacenter and cloud. Do you want to be able to provide a complete end-to-end streaming solution, from an IoT device all the way to a dashboard for your business users with no code? Come to this session to learn how this is now possible with HDF 3.1.
The document discusses Apache NiFi and its role in the Hadoop ecosystem. It provides an overview of NiFi, describes how it can be used to integrate with Hadoop components like HDFS, HBase, and Kafka. It also discusses how NiFi supports stream processing integrations and outlines some use cases. The document concludes by discussing future work, including improving NiFi's high availability, multi-tenancy, and expanding its ecosystem integrations.
Curb your insecurity with HDP - Tips for a Secure Clusterahortonworks
NOTE: Slides contains gifs which may appear as dark pics.
You got your cluster installed and configured. You celebrate, until the party is ruined by your company's Security officer stamping a big "Deny" on your Hadoop cluster. And oops!! You cannot place any data onto the cluster until you can demonstrate it is secure. In this session you will learn the tips and tricks to fully secure your cluster for data at rest, data in motion and all the apps including Spark. Your Security officer can then join your Hadoop revelry (unless you don't authorize him to, with your newly acquired admin rights)
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Predicting Test Results without Execution (FSE 2024)Andre Hora
As software systems grow, test suites may become complex, making it challenging to run the tests frequently and locally. Recently, Large Language Models (LLMs) have been adopted in multiple software engineering tasks. It has demonstrated great results in code generation, however, it is not yet clear whether these models understand code execution. Particularly, it is unclear whether LLMs can be used to predict test results, and, potentially, overcome the issues of running real-world tests. To shed some light on this problem, in this paper, we explore the capability of LLMs to predict test results without execution. We evaluate the performance of the state-of-the-art GPT-4 in predicting the execution of 200 test cases of the Python Standard Library. Among these 200 test cases, 100 are passing and 100 are failing ones. Overall, we find that GPT-4 has a precision of 88.8%, recall of 71%, and accuracy of 81% in the test result prediction. However, the results vary depending on the test complexity: GPT-4 presented better precision and recall when predicting simpler tests (93.2% and 82%) than complex ones (83.3% and 60%). We also find differences among the analyzed test suites, with the precision ranging from 77.8% to 94.7% and recall between 60% and 90%. Our findings suggest that GPT-4 still needs significant progress in predicting test results.
How Generative AI is Shaping the Future of Software Application DevelopmentMohammedIrfan308637
Generative AI is revolutionizing software development. Find out how it enhances innovation and productivity. https://www.qisacademy.com/blog-detail/the-power-of-generative-ai-in-software-application-development
Empowering Businesses with Intelligent Software Solutions - GrawlixAarisha Shaikh
Explore Grawlix's comprehensive suite of intelligent software solutions designed to drive transformative growth and scalability for businesses. This presentation covers our expertise in bespoke software development, digital marketing, web design, cloud solutions, cybersecurity, AI/ML, and IT consulting. Discover how Grawlix's customized solutions enhance productivity, streamline processes, and enable data-driven decision-making. Learn about our key projects, technologies, and the dedicated team who ensures exceptional client satisfaction through innovation and excellence.
AI is revolutionizing DevOps by advancing algorithmic optimizations in pipelines, elevating efficiency levels, and introducing predictive functionalities. This article examines how AI is reshaping continuous integration, deployment strategies, monitoring practices, and incident management within DevOps ecosystems, ultimately amplifying efficiency and dependability.
PathSpotter: Exploring Tested Paths to Discover Missing Tests (FSE 2024)Andre Hora
When creating test cases, ideally, developers should test both the expected and unexpected behaviors of the program to catch more bugs and avoid regressions. However, the literature has provided evidence that developers are more likely to test expected behaviors than unexpected ones. In this paper, we propose PathSpotter, a tool to automatically identify tested paths and support the detection of missing tests. Based on PathSpotter, we provide an approach to guide us in detecting missing tests. To evaluate it, we submitted pull requests with test improvements to open-source projects. As a result, 6 out of 8 pull requests were accepted and merged in relevant systems, including CPython, Pylint, and Jupyter Client. These pull requests created/updated 32 tests and added 80 novel assertions covering untested cases. This indicates that our test improvement solution is well received by open-source projects.
In today's dynamic business landscape, ERP software systems are essential tools for businesses worldwide, including those in the UAE. These systems cater to the unique needs of the UAE's rapidly changing economy and expanding industries.
This blog examines the top 10 ERP companies in the UAE, highlighting their innovative products, exceptional customer support, and significant impact on the regional business community. These companies excel in providing ERP solutions that enhance efficiency and growth for businesses throughout the UAE.
1. **Odoo**
- Odoo ERP is a comprehensive business management solution with features like accounting, HR, sales, inventory control, and CRM. Its user-friendly interface simplifies processes and boosts productivity. Banibro IT Solutions leverages Odoo to transform business operations.
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- Suitable for: Small, Medium, Large Businesses
- Open Source: Yes
- Cloud-based: Yes (Cloud and On-premises)
- Support: Phone, Chat, Email
- Payment: Yearly, Monthly
- Multi-Language: Yes
- OS Support: Windows, Mac, iOS, Android
- API: Available
2. **Microsoft Dynamics 365**
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- Suitable for: Small, Medium, Large Businesses
- Support: Phone, Chat, Email, Knowledge Base
- Payment: One-Time, Yearly, Monthly
- Multi-Language: No
- OS Support: Web App, Windows, iOS, Android
- API: Not specified
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- Open Source: Yes/No
- Cloud-based: Yes (Cloud and On-premises)
- Support: Phone, Email, Video Tutorials
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- Multi-Language: Yes
- OS Support: Web App, Windows, Mac, iOS, Android
- API: Available
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- Suitable for: Small, Medium, Large Businesses
- Support: Phone, Chat, Email, Knowledge Base
- Payment: One-Time, Yearly, Monthly
- Multi-Language: No
- OS Support: Web App, Windows, Mac, iOS, Android
- API: Not specified
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- Suitable for: Small, Medium, Large Businesses
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Windows Server, Microsoft’s robust operating system, is the cornerstone of enterprise IT infrastructure, tailored for mission-critical operations. It helps in managing enterprise-level tasks, including data storage, applications, and communication.
Proper licensing of Windows Server is essential for both legal compliance and optimal functionality within business environments.
Windows Server comes in various edition and before any edition is used in your organization, it is required you license them appropriately. The licensing can be complex and capital demanding when you don’t know what you want or understand the licensing requirements.
Even if successfully licensed, there are various activities you can practice as an organization to make sure your Server is operating optimally and there is real value for money. This requires a deeper understanding of best practices and our team of cloud and licensing experts can be of support.
Send the team an email, info@q-advise.com let’s have a look at your needs, together with you decide which licensing model will best work in your case, assist you with savings options and share with you how pre-owned licensing can help you get licensed adequately also.
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Monitoring the Execution of 14K Tests: Methods Tend to Have One Path that Is ...Andre Hora
The literature has provided evidence that developers are likely to test some behaviors of the program and avoid other ones. Despite this observation, we still lack empirical evidence from real-world systems. In this paper, we propose to automatically identify the tested paths of a method as a way to detect the method’s behaviors. Then, we provide an empirical study to assess the tested paths quantitatively. We monitor the execution of 14,177 tests from 25 real-world Python systems and assess 11,425 tested paths from 2,357 methods. Overall, our empirical study shows that one tested path is prevalent and receives most of the calls, while others are significantly less executed. We find that the most frequently executed tested path of a method has 4x more calls than the second one. Based on these findings, we discuss practical implications for practitioners and researchers and future research directions.