Prática de Shadowing: The Secret Sauce Behind NoSQL: LSM Tree - Aprenda a falar inglês com vídeo
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NoSQL databases like Cassandra have exploded in popularity in recent years.
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One driver is the insatiable demand to ingest huge amounts of
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data from an ever-growing number of sources like mobile application and IoT devices.
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The secret sauce behind many of these NoSQL databases is a data structure called Log Structure Merge Tree.
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An LSM tree is optimized for fast writes.
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To understand how an LSM tree works, let's first take a look at how data is typically stored in a relational database.
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A relational database is commonly backed by a data structure called a B-tree.
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A B-tree is optimized for reads.
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Updating the B-tree is relatively expensive as it involves random I.O and might include updating multiple pages on disk.
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This limits how fast a B tree can ingest data.
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An LSM tree works differently.
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Writes are bashed in memory as they arrive in a structure called a mem table.
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A mem table is ordered by object key and is usually implemented as a balanced binary tree.
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As a mem table reaches a certain size, it is flushed to disk as an immutable sorted string table.
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An ss table stores the key value pairs in a sorted sequence.
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These writes are all sequential I.O.
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They are fast on any storage media.
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The new ss table becomes the most recent segment of the LSM tree.
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As more data comes in, more and more of these immutable SS tables are created and added to the LSM tree,
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with each one representing a small chronological subset of the incoming changes.
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Since SS tables are immutable, an update to an existing object key does not override an existing table.
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Instead, a new entry is added to the most recent SS table, which supersedes any entries in the old SS tables for the object key.
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Deleting an object requires special handling too, since we cannot mark anything in the SS table as deleted.
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To perform a delete, it adds a marker called a tombstone to the most recent SS table for the object key.
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When we encounter the tombstone on read, we know the object has been deleted.
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And yes, it is a bit unintuitive that a delete takes up extra space.
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To serve a read request, we first try to find the key in the mem table, then in the most recent SS table in the LSM tree,
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then the next SS table, and so on.
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Since an SS table is sorted, the lookup can be done efficiently.
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The accumulation of SS tables presents two issues.
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As the number of SSTables grows, it will take an increasingly long time to look up a key.
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And as the SSTables accumulate, there are more and more outdated entries as keys are updated and tombstones are added.
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These take up precious disk space.
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To fix these issues, there is a periodic merging and compaction process running in the background
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to merge SSTables and discard outdated or deleted values.
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This reclaims this space and caps the number of SSTables a read has to look through.
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Since the tables are sorted, this merging and compaction process is simple and efficient.
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The approach is similar to the one used in the merge phase of the merge sort algorithm.
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This, in a nutshell, is how an LSM tree provides fast writes.
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Let's recap.
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An LSM tree buffers incoming writes in memory.
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When the buffer fills up, we sort and flush it to disk as an immutable SS table.
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The number of SS tables increases as more buffers get flushed to disk.
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This creates a problem for reads, as each read has to search through these SS tables to perform a lookup.
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To cap the number of SS tables it has to search through for each read, the LSM tree merges the SSTables and compacts them in the background.
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Let's look at the compaction process more closely.
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When the SSTables are merged, they are organized into levels.
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This is where the tree part of the LSM tree comes into the picture.
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There are different strategies to determine when and how the SSTables are merged and compacted.
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There are two broad strategies, size-tiered compaction and leveled compaction.
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Size tier compaction is optimized for write throughput.
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Level compaction is more read optimized.
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We won't go into too much detail on how these compaction strategies work.
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There are a few key points to remember though.
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Compaction keeps the number of SS tables manageable.
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The SS tables are organized into levels, with each level gets exponentially larger as SS tables from the level above emerged into it.
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Compaction consumes a lot of I.O.
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A mistuned compaction could starve the system and slow down both read and write.
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Finally, let's visit some common optimizations for the LSM trees in production systems.
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There are many optimization strategies that try to provide read performance closer to that of the B tree.
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What we explain below are just the common ones, and they are by no means exhaustive.
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To look up a key, it performs searches on the SS tables at every level.
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Even though search is fast on sorted data, going through all the on-disk SS tables consumes a lot of I.O.
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Many systems keep a summary table in memory that contains the min-max range of each disk block of every level.
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It allows the system to skip searches on those disk blocks where the key does not fall within the range.
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This saves a lot of random I.O.
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Another issue that could be potentially expensive is to look up a key that does not exist.
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This would require looking through all the eligible blocks at all the levels.
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Most systems keep a bloom filter at each level.
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A bloom filter is a space-efficient data structure that returns a firm no if a key does not exist, or a probably yes if a key might exist.
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This allows the system to skip a level entirely if the key does not exist there, which dramatically reduces the number of random I.O required.
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In conclusion, NoSQL databases backed by an LSM tree could be tuned to support a very high write rate.
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As with any database, proper tuning is the key.
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For an LSM tree, compaction tuning is the most critical.
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If you would like to learn more about system design, check out our books and weekly newsletter.
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Please subscribe if you learn something new.
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Thank you so much and we'll see you next time.
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Vocabulário e dicas de fala para esta lição
Esta aula de conversação de nível C1 usa o vídeo “The Secret Sauce Behind NoSQL: LSM Tree”. As palavras que mais se repetem: table, tree, key, Lsm, compaction. Este vídeo tem 77 frases e 1089 palavras para praticar shadowing. A fala dura 7:26. O falante mantém um ritmo estável de cerca de 147 palavras por minuto, confortável para o shadowing. Apenas 72% das palavras estão entre as 3.000 mais comuns do inglês, por isso o vocabulário é exigente.
Vocabulário principal deste vídeo
As 15 palavras mais avançadas do vídeo, com pronúncia e significado:
| Palavra | Pronúncia | Significado |
|---|---|---|
| merge verbo | /mɝd͡ʒ/ | unir, convergir |
| disk substantivo | /dɪsk/ | disco |
| immutable adjetivo | imutável | |
| optimize verbo | /ˈɑptɪmaɪz/ | otimizar |
| tombstone substantivo | /ˈtuːmstoʊn/ | lápide, campa |
| buffer substantivo | /ˈbʌfəɹ/ | solução tampão |
| flush verbo | /ˈflʌʃ/ | dar descarga, puxar descarga |
| ingest verbo | /ɪnˈd͡ʒɛst/ | ingerir |
| relational adjetivo | relacional | |
| outdated adjetivo | /aʊtˈdeɪtɪd/ | obsoleto, ultrapassado |
| optimization substantivo | /ˌɑptəmaɪˈzeɪʃən/ | otimização, optimização |
| consume verbo | /kənˈsum/ | consumir |
| incoming adjetivo | /ˈɪnˌkʌmɪŋ/ | entrante |
| organize verbo | /ˈɔɹɡənaɪz/ | organizar |
| compact adjetivo | /kəmˈpækt/ | compacto |
Phrasal verbs que você vai ouvir
| Palavra | Significado |
|---|---|
| check out verbo | verificar, sacar só |
| fill up verbo | encher |
| slow down verbo | desacelerar |
Gramática neste vídeo
As estruturas que o falante mais usa, com as palavras exatas do vídeo:
| Estrutura | No vídeo |
|---|---|
| Voz passiva be + particípio passado — o foco está no que acontece, não em quem faz | is optimized · is typically stored · is commonly backed |
| Present perfect have/has + particípio passado — uma ação passada que ainda importa agora | have exploded · has been deleted |
Pronúncia para ficar de olho
- Os sons de “th”: throughput /ˈθɹuː.pʊt/, algorithm /ˈælɡəɹɪðm̩/
- Os sons de “sh” e “zh”: compaction /kəmˈpækʃən/, flush /ˈflʌʃ/, optimization /ˌɑptəmaɪˈzeɪʃən/, exponentially /ɛkspə(ʊ)ˈnɛnʃəli/, insatiable /ɪnˈseɪʃ(j)əbəl/
- Palavras longas — acerte a sílaba tônica: optimization /ˌɑptəmaɪˈzeɪʃən/, accumulate /əˈkjuːmjʊˌleɪt/, chronological /ˌkɹɑnəˈlɑd͡ʒɪkəl/, exponentially /ɛkspə(ʊ)ˈnɛnʃəli/, insatiable /ɪnˈseɪʃ(j)əbəl/
Sons difíceis para falantes de português:
- Consoante final — sem acrescentar um “i” depois: disk /dɪsk/, ingest /ɪnˈd͡ʒɛst/, outdated /aʊtˈdeɪtɪd/, compact /kəmˈpækt/, accumulate /əˈkjuːmjʊˌleɪt/
- /l/ final — a língua toca o céu da boca, não vira “u”: chronological /ˌkɹɑnəˈlɑd͡ʒɪkəl/, insatiable /ɪnˈseɪʃ(j)əbəl/, manageable /ˈmænəd͡ʒəb(ə)l/, nutshell /ˈnʌt.ʃɛl/, sequential /səˈkwɛntʃəl/
Como praticar com este vídeo
- Ouça o vídeo inteiro uma vez sem falar e anote as palavras que você não conhece.
- Faça shadowing frase por frase na velocidade normal, repetindo cada uma até o seu ritmo ficar igual ao do falante.
- Grave a sua voz e compare com o original, prestando atenção a palavras como merge, disk, immutable.
O que é a Técnica de Shadowing?
Shadowing é uma técnica de aprendizado de idiomas com base científica, originalmente desenvolvida para o treinamento de intérpretes profissionais. O método é simples, mas poderoso: você ouve áudio em inglês nativo e repete imediatamente em voz alta — como uma sombra seguindo o falante com 1-2 segundos de atraso. Pesquisas mostram melhora significativa na precisão da pronúncia, entonação, ritmo, sons conectados, compreensão auditiva e fluência na fala.