Memeriksa cache di Spark UI

Sebuah dataframe partitioned_df tersedia. Dataframe ini digunakan untuk mendaftarkan tabel sementara bernama text. Tabel text kemudian di-cache menggunakan spark.catalog.cacheTable('text'). Jika Anda menjalankan Spark secara lokal, Spark UI tersedia di http://localhost:4040/storage/. Untuk keperluan latihan ini, perhatikan gambar berikut. Gambar ini menunjukkan tampilan Spark UI setelah cache untuk text dimuat:

Spark UI Storage

Gambar ini menunjukkan bahwa sebuah tabel bernama text dengan tujuh partisi di-cache di memori. Manakah dari berikut ini yang akan langsung menyebabkan tampilan di atas muncul di Spark UI?

Melakukan transformasi pada dataframe dasar, misalnya df = partitioned_df.distinct().
Melakukan penghitungan pada dataframe dasar, misalnya: partitioned_df.count()
Menjalankan kueri pada tabel, misalnya: spark.sql("select count(*) from text")
Menjalankan kueri dan menampilkan hasilnya, misalnya: spark.sql("select count(*) from text").show()

Latihan ini adalah bagian dari kursus

Pengantar Spark SQL dalam Python

Latihan interaktif praktis

Ubah teori menjadi tindakan dengan salah satu latihan interaktif kami.

Mulai berolahraga

Latihan ini adalah bagian dari kursus

Pengantar Spark SQL dalam Python

SkillTag.level.advancedSkillTag.label

4.7+

Mulai Kursus Gratis

In this chapter you will learn how to create and query a SQL table in Spark. Spark SQL brings the expressiveness of SQL to Spark. You will also learn how to use SQL window functions in Spark. Window functions perform a calculation across rows that are related to the current row. They greatly simplify achieving results that are difficult to express using only joins and traditional aggregations. We'll use window functions to perform running sums, running differences, and other operations that are challenging to perform in basic SQL.

Exercise 1: Creating and querying a SQL table in Spark Exercise 2: Create a SQL table from a dataframe Exercise 3: Determine the column names of a table Exercise 4: Window function SQL Exercise 5: Running sums using window function SQL Exercise 6: Fix the broken query Exercise 7: Dot notation and SQL Exercise 8: Aggregation, step by step Exercise 9: Aggregating the same column twice Exercise 10: Aggregate dot SQL Exercise 11: Convert window function from dot notation to SQL

In this chapter, you will be loading natural language text. Then you will apply a moving window analysis to find frequent word sequences.

Exercise 1: Loading natural language text Exercise 2: Loading a dataframe from a parquet file Exercise 3: Split and explode a text column Exercise 4: Using monotonically_increasing_id()Exercise 5: Moving window analysis Exercise 6: Creating context window feature data Exercise 7: Repartitioning the data Exercise 8: Common word sequences Exercise 9: What type of data is this Exercise 10: Finding common word sequences Exercise 11: Unique 5-tuples in sorted order Exercise 12: Most frequent 3-tuples per chapter

In the previous chapters you learned how to use the expressiveness of window function SQL. However, this expressiveness now makes it important that you understand how to properly cache dataframes and cache SQL tables. It is also important to know how to evaluate your application. You learn how to do do this using the Spark UI. You'll also learn a best practice for logging in Spark. Spark SQL brings with it another useful tool for tuning query performance issues, the query execution plan. You will learn how to use the execution plan for evaluating the provenance of a dataframe.

Exercise 1: Caching Exercise 2: Latihan caching: bagian 1 Exercise 3: Berlatih caching: SQL Exercise 4: Latihan caching: menggabungkan semuanya Exercise 5: Melakukan cache dan menghapus cache tabel Exercise 6: Spark UI Exercise 7: Tab storage di Spark UI Exercise 8: Memeriksa cache di Spark UI

Latihan Saat Ini

Exercise 9: Pencatatan (Logging)Exercise 10: Latihan logging Exercise 11: Latihan logging 2 Exercise 12: Rencana kueri Exercise 13: Latihan rencana kueri Exercise 14: Latihan membaca rencana kueri 2

Previous chapters provided you with the tools for loading raw text, tokenizing it, and extracting word sequences. This is already very useful for analysis, but it is also useful for machine learning. What you've learned now comes together by using logistic regression to classify text. By the conclusion of this chapter, you will have loaded raw natural language text data and used it to train a text classifier.

Exercise 1: Extract Transform Select Exercise 2: Practicing creating a UDF Exercise 3: Practicing array column Exercise 4: Creating feature data for classification Exercise 5: Creating a UDF for vector data Exercise 6: Applying a UDF to vector data Exercise 7: Transforming text to vector format Exercise 8: Text Classification Exercise 9: Label the data Exercise 10: Split the data Exercise 11: Train the classifier Exercise 12: Predicting and evaluating Exercise 13: Evaluate the classifier Exercise 14: Predict test data Exercise 15: Recap